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<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''>= 1E-12''<br /> <br /> Default: ''1E-7''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for quadratically constrained problems (QCP). For LPs and for QPs (i.e. when all the constraints are linear) see BAREPCOMP. <br /> <br /> Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Always select best estimate node, never select best bound<br /> 1 Always select best bound node<br /> 7 Select best bound node occasionally<br /> Any positive integer - select best bound node less frequently than best estimate node<br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sets best bound interval for MIP strategy<br /> When you set this parameter to best estimate node selection, the best bound interval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree.<br /> <br /> BBINTERVAL=0 means to never select the best bound node.<br /> <br /> BBINTERVAL=1 means to always select the best bound node, and is thus equivalent to NODESEL=1.<br /> <br /> Higher values of BBINTERVAL mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default value of this parameter is 7.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Decides whether to apply bound strengthening in mixed integer programs. <br /> <br /> Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||-1 Let CPLEX decide<br /> <br /> 0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> 3 Reduce aggressively with ''tilting''<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <br /> The most aggressive setting (3) applies a method called ''tilting''. Tilting can cut off additional fractional solutions in some models, potentially yielding better progress in both the best node and best integer solution. <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3151 CPLEX Parameter Table 2021-09-23T12:27:35Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */ COEREDIND</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''>= 1E-12''<br /> <br /> Default: ''1E-7''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for quadratically constrained problems (QCP). For LPs and for QPs (i.e. when all the constraints are linear) see BAREPCOMP. <br /> <br /> Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Always select best estimate node, never select best bound<br /> 1 Always select best bound node<br /> 7 Select best bound node occasionally<br /> Any positive integer - select best bound node less frequently than best estimate node<br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sets best bound interval for MIP strategy<br /> When you set this parameter to best estimate node selection, the best bound interval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree.<br /> <br /> BBINTERVAL=0 means to never select the best bound node.<br /> <br /> BBINTERVAL=1 means to always select the best bound node, and is thus equivalent to NODESEL=1.<br /> <br /> Higher values of BBINTERVAL mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default value of this parameter is 7.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Decides whether to apply bound strengthening in mixed integer programs. <br /> <br /> Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||-1 Let CPLEX decide<br /> <br /> 0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> 3 Reduce aggressively with *tilting*<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3150 CPLEX Parameter Table 2021-09-09T13:14:05Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''>= 1E-12''<br /> <br /> Default: ''1E-7''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for quadratically constrained problems (QCP). For LPs and for QPs (i.e. when all the constraints are linear) see BAREPCOMP. <br /> <br /> Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Always select best estimate node, never select best bound<br /> 1 Always select best bound node<br /> 7 Select best bound node occasionally<br /> Any positive integer - select best bound node less frequently than best estimate node<br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sets best bound interval for MIP strategy<br /> When you set this parameter to best estimate node selection, the best bound interval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree.<br /> <br /> BBINTERVAL=0 means to never select the best bound node.<br /> <br /> BBINTERVAL=1 means to always select the best bound node, and is thus equivalent to NODESEL=1.<br /> <br /> Higher values of BBINTERVAL mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default value of this parameter is 7.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Decides whether to apply bound strengthening in mixed integer programs. <br /> <br /> Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3149 CPLEX Parameter Table 2021-09-08T09:43:37Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */ bbinterval</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''>= 1E-12''<br /> <br /> Default: ''1E-7''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for quadratically constrained problems (QCP). For LPs and for QPs (i.e. when all the constraints are linear) see BAREPCOMP. <br /> <br /> Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Always select best estimate node, never select best bound<br /> 1 Always select best bound node<br /> 7 Select best bound node occasionally<br /> Any positive integer - select best bound node less frequently than best estimate node<br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sets best bound interval for MIP strategy<br /> When you set this parameter to best estimate node selection, the best bound interval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree.<br /> <br /> BBINTERVAL=0 means to never select the best bound node.<br /> <br /> BBINTERVAL=1 means to always select the best bound node, and is thus equivalent to NODESEL=1.<br /> <br /> Higher values of BBINTERVAL mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default value of this parameter is 7.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3148 CPLEX Parameter Table 2021-09-08T09:09:15Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''>= 1E-12''<br /> <br /> <br /> <br /> Default: ''1E-7''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for QCP problems. That is, for quadratically constrained problems. For<br /> <br /> LPs and for QPs (that is, when all the constraints are linear) see BAREPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Best estimate node always selected or, any positive integer<br /> <br /> <br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP strategy bbinterval.<br /> <br /> When using nodeselect 2, the bbinterval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree. A bbinterval of 0 means to never select the best bound node. A bbinterval of 1 means to always select the best bound node, and is thus equivalent to nodeselect 1. Higher values of bbinterval mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default bbinterval is 7.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3147 CPLEX Parameter Table 2021-09-06T13:33:24Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1.0E20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for QCP problems. That is, for quadratically constrained problems. For<br /> <br /> LPs and for QPs (that is, when all the constraints are linear) see BAREPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Best estimate node always selected or, any positive integer<br /> <br /> <br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP strategy bbinterval.<br /> <br /> When using nodeselect 2, the bbinterval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree. A bbinterval of 0 means to never select the best bound node. A bbinterval of 1 means to always select the best bound node, and is thus equivalent to nodeselect 1. Higher values of bbinterval mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default bbinterval is 7.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3146 CPLEX Parameter Table 2021-09-06T13:32:03Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''>= 1.0E-12''<br /> <br /> <br /> <br /> Default: ''1.0E-8''<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 1.0E12<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1020<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for QCP problems. That is, for quadratically constrained problems. For<br /> <br /> LPs and for QPs (that is, when all the constraints are linear) see BAREPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Best estimate node always selected or, any positive integer<br /> <br /> <br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP strategy bbinterval.<br /> <br /> When using nodeselect 2, the bbinterval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree. A bbinterval of 0 means to never select the best bound node. A bbinterval of 1 means to always select the best bound node, and is thus equivalent to nodeselect 1. Higher values of bbinterval mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default bbinterval is 7.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3145 CPLEX Parameter Table 2021-09-06T10:07:19Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''8<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 108<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1020<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for QCP problems. That is, for quadratically constrained problems. For<br /> <br /> LPs and for QPs (that is, when all the constraints are linear) see BAREPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Best estimate node always selected or, any positive integer<br /> <br /> <br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP strategy bbinterval.<br /> <br /> When using nodeselect 2, the bbinterval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree. A bbinterval of 0 means to never select the best bound node. A bbinterval of 1 means to always select the best bound node, and is thus equivalent to nodeselect 1. Higher values of bbinterval mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default bbinterval is 7.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=CPLEX_Parameter_Table&diff=3144 CPLEX Parameter Table 2021-03-08T13:01:46Z

<p>WikiSysop: /* Table: CPLEX Parameters Overview */</p> <hr /> <div>{{Part Of Manual|title=the CPLEX Manual|link=[[CPLEX|CPLEX]]}}<br /> <br /> ====Table: CPLEX Parameters Overview====<br /> <br /> {| class="wikitable"<br /> !TOMLAB parameter<br /> !Value<br /> |-valign="top"<br /> |cpxControl.ADVIND||0 Off: do not use advanced start information<br /> 1 On: CPLEX will use an advanced basis supplied by the user<br /> <br /> 2 On: CPLEX will crush an advanced basis or starting vector supplied by the user<br /> <br /> <br /> Default: 0<br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''An indicator which, if set to 1 or 2, uses advanced starting information when optimization is<br /> <br /> initiated. Setting 2 may be effective for MIPs in which the percentage of integer constraints is low. It may also reduce the solution time of fixed MIPs.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGCUTLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: 3<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Constraint aggregation limit for cut generation. Limits the number of constraints that can be<br /> <br /> aggregated for generating flow cover and mixed integer rounding cuts<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGFILL||Any non-negative integer<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator fill. Limits variable substitutions by the aggregator. If the net result<br /> <br /> of a single substitution is more nonzeros than this value, the substitution is not made.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.AGGIND||-1 Automatic (1 for LP, infinite for MIP)<br /> <br /> 0 Do not use any aggregator<br /> <br /> Any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Preprocessing aggregator application limit. Invokes the aggregator to use substitution where<br /> <br /> possible to reduce the number of rows and columns before the problem is solved. If set to a positive value, the aggregator is applied the specified number of times or until no more reductions are possible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARALG||0 Default setting<br /> <br /> 1 Infeasibility-estimate start<br /> <br /> 2 Infeasibility-constant start<br /> <br /> 3 Standard barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier algorithm.<br /> <br /> The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1) when solving sub-problems in a Mixed Integer Programming problem, and the standard barrier algorithm (setting 3) in other cases. The standard barrier algorithm is almost always fastest. However, on problems that are primal or dual infeasible (common for Mixed Integer sub-problems), the standard algorithm may not work as well as the alternatives. The two alternative algorithms (settings 1 and 2) may eliminate numerical difficulties related to infeasibility, but are generally slower.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCOLNZ||0 Dynamically calculated or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier column nonzeros.<br /> <br /> Used in the recognition of dense columns. If columns in the presolved and aggregated problem exist with more entries than this value, such columns are considered dense and are treated specially by the CPLEX Barrier Optimizer to reduce their effect. If the problem contains fewer than 400 rows, dense column handling is NOT initiated.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARCROSSALG||-1 No crossover<br /> <br /> 0 Automatic<br /> <br /> 1 Primal crossover<br /> <br /> 2 Dual crossover<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier crossover method.<br /> <br /> Determines which, if any, crossover method is performed at the end of a Barrier optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARDISPLAY||0 No progress information<br /> <br /> 1 Normal setup and iteration information<br /> <br /> 2 Diagnostic information<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier display information.<br /> <br /> Determines the level of barrier information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAREPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''8<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for LP and QP problems.<br /> <br /> For problems with quadratic constraints (QCP), see BARQCPEPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARGROWTH||1.0 or greater.<br /> <br /> <br /> <br /> Default: 108<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier growth.<br /> <br /> Used to detect unbounded optimal faces. At higher values, the barrier algorithm is less likely to conclude that the problem has an unbounded optimal face, but more likely to have numerical difficulties if the problem has an unbounded face.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARITLIM||0 No Barrier iterations or, any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier iteration limit.<br /> <br /> Sets the number of Barrier iterations before termination. When set to 0, no Barrier iterations occur, but problem<br /> <br /> "setup" occurs and information about the setup is displayed (such as Cholesky factorization information).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARMAXCOR||-1 Automatically determined<br /> <br /> 0 None<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier maximum correction limit.<br /> <br /> Sets the maximum number of centering corrections done on each iteration. An explicit value greater than 0 may improve the numerical performance of the algorithm at the expense of computation time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BAROBJRNG||Any positive number<br /> <br /> <br /> <br /> Default: 1020<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier objective range.<br /> <br /> Sets the maximum absolute value of the objective function. The barrier algorithm looks at this limit to detect unbounded problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARORDER||0 Automatic<br /> <br /> 1 Approximate minimum degree (AMD)<br /> <br /> 2 Approximate minimum fill (AMF)<br /> <br /> 3 Nested dissection (ND)<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier ordering algorithm.<br /> <br /> Sets the algorithm to be used to permute the rows of the constraint matrix in order to reduce fill in the Cholesky factor.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARQCPEPCOMP||Any positive number ''= ''10''-''12<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Convergence tolerance for QCP problems. That is, for quadratically constrained problems. For<br /> <br /> LPs and for QPs (that is, when all the constraints are linear) see BAREPCOMP. Sets the tolerance on complementarity for convergence. The barrier algorithm terminates with an optimal solution if the relative complementarity is smaller than this value. Changing this tolerance to a smaller value may result in greater numerical precision of the solution, but also increases the chance of a convergence failure in the algorithm and consequently may result in no solution at all. Therefore, caution is advised in deviating from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BARSTARTALG||1 Dual is 0<br /> <br /> 2 Estimate dual<br /> <br /> 3 Average of primal estimate, dual 0<br /> <br /> 4 Average of primal estimate, estimate dual<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Barrier starting point algorithm.<br /> <br /> Sets the algorithm to be used to compute the initial starting point for the barrier optimizer.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BBINTERVAL||0 Best estimate node always selected or, any positive integer<br /> <br /> <br /> <br /> Default: 7<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP strategy bbinterval.<br /> <br /> When using nodeselect 2, the bbinterval is the interval at which the best bound node, instead of the best estimate node, is selected from the tree. A bbinterval of 0 means to never select the best bound node. A bbinterval of 1 means to always select the best bound node, and is thus equivalent to nodeselect 1. Higher values of bbinterval mean that the best bound node will be selected less frequently; experience has shown it to be beneficial to occasionally select the best bound node, and therefore the default bbinterval is 7.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BNDSTRENIND||-1 Automatically determined<br /> <br /> 0 Do not apply bound strengthening<br /> <br /> 1 Apply bound strengthening<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Bound strengthening indicator.<br /> <br /> Used when solving mixed integer programs. Bound strengthening tightens the bounds on variables, perhaps to the point where the variable can be fixed and thus removed from consideration during branch & cut. This reduction is usually beneficial, but occasionally, due to its iterative nature, takes a long time.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BRDIR||-1 Down branch selected first<br /> <br /> 0 Automatically determined<br /> <br /> 1 Up branch selected first<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP branching direction.<br /> <br /> Used to decide which branch, the up or the down branch, should be taken first at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.BTTOL||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 0.9999<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Backtracking tolerance.<br /> <br /> Controls how often backtracking is done during the branching process. The decision when to backtrack depends on three values that change during the course of the optimization:<br /> <br /> - the objective function value of the best integer feasible solution ("incumbent")<br /> <br /> - the best remaining objective function value of any unexplored node ("best node")<br /> <br /> - the objective function value of the most recently solved node ("current objective").<br /> <br /> If a cutoff tolerance (see CUTUP and CUTLO) has been set by the user then that value is used as the incumbent until an integer feasible solution is found. The "target gap" is defined to be the absolute value of the difference between the incumbent and the best node, multiplied by this backtracking parameter. CPLEX does not backtrack until the absolute value of the difference between the current objective and the best node is at least as large as the target gap. Low values of this backtracking parameter thus tend to increase the amount of backtracking, which makes the search process more of a pure best-bound search. Higher parameter values tend to decrease backtracking, making the search more of a pure depth-first search. The backtracking value has effect only after an integer feasible solution is found or when a cutoff has been specified. Note that this backtracking value merely permits backtracking but does not force it; CPLEX may choose to continue searching a limb of the tree if it seems a promising candidate for finding an integer feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLIQUES||-1 Do not generate clique cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate clique cuts moderately<br /> <br /> 2 Generate clique cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP cliques indicator.<br /> <br /> Determines whether or not clique cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate cliques should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CLOCKTYPE||1 CPU time<br /> <br /> 2 Wall clock time (total physical time elapsed)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Computation time reporting.<br /> <br /> Determines how computation times are measured.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COEREDIND||0 Do not use coefficient reduction<br /> <br /> 1 Reduce only to integral coefficients<br /> <br /> 2 Reduce all potential coefficients<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Coefficient reduction setting.<br /> <br /> Determines how coefficient reduction is used. Coefficient reduction improves the objective value of the initial<br /> <br /> (and subsequent) LP relaxations solved during branch & cut by reducing the number of non-integral vertices.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COLREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer.<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Variable (column) read limit.<br /> <br /> Sets the number of variables that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.COVERS||-1 Do not generate cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate cover cuts moderately<br /> <br /> 2 Generate cover cuts aggressively<br /> <br /> 3 Generate cover cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP covers indicator.<br /> <br /> Determines whether or not cover cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate covers should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CRAIND||'''LP Primal:'''<br /> <br /> 0 Ignore objective coefficients during crash<br /> <br /> -1 or 1 Alternate ways of using objective coefficients<br /> <br /> '''LP Dual:'''<br /> <br /> 1 Default starting basis<br /> <br /> 0 or -1 Aggressive starting basis<br /> <br /> '''QP Primal:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 Ignore Q terms and use LP solver for crash<br /> <br /> 1 Ignore objective and use LP solver for crash<br /> <br /> '''QP Dual:'''<br /> <br /> -1 Slack basis<br /> <br /> 0 or 1 Use Q terms for crash<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex crash ordering.<br /> <br /> Determines how CPLEX orders variables relative to the objective function when selecting an initial basis.<br /> <hr /><br /> |-valign="top"<br /> <br /> |cpxControl.CUTLO||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower cutoff.<br /> <br /> When the problem is a maximization problem, the LOWERCUTOFF parameter is used to cut off any nodes that have an objective value below the lower cutoff value. On a continued mixed integer optimization, the larger of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the LOWERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTPASS||-1 None<br /> <br /> 0 Automatically determined<br /> <br /> Positive values give number of passes to perform<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Number of cutting plane passes.<br /> <br /> Sets the upper limit on the number of passes CPLEX performs when generating cutting planes on a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTSFACTOR||Any non-negative number.<br /> <br /> <br /> <br /> Default: 4.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Row multiplier factor for cuts.<br /> <br /> Limits the number of cuts that can be added. The number of rows in the problem with cuts added is limited to CUTSFACTOR times the original number of rows. If the problem is presolved, the original number of rows is that from the presolved problem.<br /> <br /> A CUTSFACTOR of 1.0 or less means that no cuts will be generated. Because cuts can be added and removed during the course of optimization, CUTSFACTOR may not correspond directly to the number of cuts seen during the node log or in the summary table at the end of optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.CUTUP||Any number.<br /> <br /> <br /> <br /> Default: 1e^75 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper cutoff.<br /> <br /> Cuts off any nodes that have an objective value above the upper cutoff value, when the problem is a minimization problem. When a mixed integer optimization problem is continued, the smaller of these values and the updated cutoff found during optimization are used during the next mixed integer optimization. A too-restrictive value for the UPPERCUTOFF parameter may result in no integer solutions being found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DATACHECK||0 Off (do not check)<br /> <br /> 1 On (check)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Data consistency checking indicator.<br /> <br /> When set to 1 (On), extensive checking is performed on data in the array arguments, such as checking that indices are within range, that there are no duplicate entries and that values are valid for the type of data or are valid numbers. This is useful for debugging applications.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DEPIND||0 Off (do not use dependency checker)<br /> <br /> 1 On (use dependency checker)<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dependency indicator.<br /> <br /> Determines whether to activate the "dependency checker". If on, the dependency checker searches for dependent rows during preprocessing. If off, dependent rows are not identified.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DETTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Deterministic time limit.<br /> <br /> Sets a time limit expressed in ticks, a unit to measure work done deterministically.<br /> The length of a deterministic tick may vary by platform. Nevertheless, ticks are normally consistent measures for a given platform (combination of hardware and software) carrying the same load. In other words, the correspondence of ticks to clock time depends on the hardware, software, and the current load of the machine. For the same platform and same load, the ratio of ticks per second stays roughly constant, independent of the model solved. However, for very short optimization runs, the variation of this ratio is typically high.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DISJCUTS||-1 Do not generate disjunctive cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate disjunctive cuts moderately<br /> <br /> 2 Generate disjunctive cuts aggressively<br /> <br /> 3 Generate disjunctive cuts very aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP disjunctive cuts indicator.<br /> <br /> Determines whether or not disjunctive cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate disjunctive cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DIVETYPE||0 automatic<br /> <br /> 1 traditional dive<br /> <br /> 2 probing dive<br /> <br /> 3 guided dive<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dive strategy.<br /> <br /> The MIP traversal strategy occasionally performs probing dives, where it looks ahead at both children nodes before deciding which node to choose. The default (automatic) setting lets CPLEX choose when to perform a probing dive, 1 directs CPLEX never to perform probing dives, 2 always to probe, 3 spend more time exploring potential solutions that are similar to the current incumbent. Setting 2, always to probe, is helpful for finding integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.DPRIIND||0 Determined automatically<br /> <br /> 1 Standard dual pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing in slack space<br /> <br /> 4 Steepest-edge pricing, unit initial norms<br /> <br /> 5 Devex pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Dual simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternate settings.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EACHCUTLIM||0 No cuts<br /> <br /> N+ Limit each type of cut<br /> <br /> <br /> <br /> Default: 2.1e9<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Type of cut limit.<br /> <br /> Sets a limit for each type of cut. This parameter allows you to set a uniform limit on the number of cuts of a each type that CPLEX generates. By default, the limit is the largest integer supported by a given platform; that is, there is no effective limit by default. Tighter limits on the number of cuts of each type may benefit certain models. For example, a limit on each type of cut will prevent any one type of cut from being created in such large number that the limit on the total number of all types of cuts is reached before other types of cuts have an opportunity to be created. This parameter does not influence the number of Gomory cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPAGAP||Any non-negative number.<br /> <br /> <br /> <br /> Default: 10''-''6 .<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute mipgap tolerance.<br /> <br /> Sets an absolute tolerance on the gap between the best integer objective and the objective of the best node remaining. When this difference falls below the value of the EPGAP parameter, the mixed integer optimization is stopped.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPGAP||Any number from 0.0 to 1.0<br /> <br /> <br /> <br /> Default: 10''-''4<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative mipgap tolerance.<br /> <br /> Sets a relative tolerance on the gap between the best integer objective and the objective of the best node remaining. When the value<br /> <br /> ''|bestnode - bestinteger|''<br /> <br /> 10''-''10 + ''|bestinteger|''<br /> <br /> falls below the value of the MIPGAP parameter, the mixed integer optimization is stopped. For example, to instruct CPLEX to stop as soon as it has found a feasible integer solution proved to be within five percent of optimal, set the relative mipgap tolerance to 0''.''05.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPINT||Any number from 10''-''9 to 0''.''5.<br /> <br /> <br /> <br /> Default: 10''-''5<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Integrality tolerance.<br /> <br /> Specifies the amount by which an integer variable can be different from an integer and still be considered feasible.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPMRK||Any number from 0''.''0001 to 0''.''99999<br /> <br /> <br /> <br /> Default: 0''.''01<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Markowitz tolerance.<br /> <br /> Influences pivot selection during basis factorization. Increasing the Markowitz threshold may improve the numerical properties of the solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPOPT||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance.<br /> <br /> Influences the reduced-cost tolerance for optimality. This parameter governs how closely CPLEX must approach the theoretically optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPPER||Any positive number ''= ''10''-''8<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Perturbation constant.<br /> <br /> Sets the amount by which CPLEX perturbs the upper and lower bounds on the variables when a problem is perturbed. This parameter can be set to a smaller value if the default value creates too large a change in the problem.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRELAX||Any positive number<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt tolerance.<br /> <br /> Sets epsilon used to measure relaxation in FeasOpt.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.EPRHS||Any number from 10''-''9 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's basic variables may violate their bounds. FEASIBILITY influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FEASOPTMODE||0 Minimize the sum of all required relaxations in first phase only<br /> <br /> 1 Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 2 Minimize the number of constraints and bounds requiring relaxation in first phase only<br /> <br /> 3 Minimize the number of constraints and bounds requiring relaxation in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> 4 Minimize the sum of squares of required relaxations in first phase only<br /> <br /> 5 Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''FeasOpt settings.<br /> <br /> FeasOpt works in two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution among those that require only as much relaxation as it found necessary in the first phase.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWCOVERS||-1 Do not generate flow cover cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow cover cuts moderately<br /> <br /> 2 Generate flow cover cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow cover cuts indicator.<br /> <br /> Determines whether or not to generate flow cover cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow cover cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FLOWPATHS||-1 Do not generate flow path cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate flow path cuts moderately<br /> <br /> 2 Generate flow path cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP flow path cut indicator.<br /> <br /> Determines whether or not flow path cuts should be generated for the problem. Setting the value to 0, the default, indicates that the attempt to generate flow path cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FPHEUR||-1 Do not apply the feasibility pump heuristic<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution<br /> <br /> 2 Apply the feasibility pump heuristic with an emphasis on finding a feasible solution with a good objective value<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility pump switch.<br /> <br /> Turns on or off the feasibility pump heuristic. At the default setting 0 (zero), CPLEX automatically chooses whether or not to apply the feasibility pump heuristic on the basis of characteristics of the model. To turn off the feasibility pump heuristic, set the parameter to -1 (minus one). If the parameter is set to 1 (one), the feasibility pump tries to find a feasible solution without taking the objective function into account. If the parameter is set to 2, the heuristic usually finds solutions of better objective value, but is more likely to fail to find a feasible solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCAND||Any positive integer.<br /> <br /> <br /> <br /> Default: 200<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Candidate limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of candidate variables for generating Gomory fractional cuts.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACCUTS||-1 Do not generate Gomory fractional cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate Gomory fractional cuts moderately<br /> <br /> 2 Generate Gomory fractional cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP Gomory fractional cuts indicator.<br /> <br /> Determines whether or not Gomory fractional cuts should be generated for the problem. Setting the value to<br /> <br /> 0, the default, indicates that the attempt to generate Gomory fractional cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.FRACPASS||0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Pass limit for generating Gomory fractional cuts.<br /> <br /> Limits the number of passes for generating Gomory fractional cuts. At the default setting of 0, CPLEX decides. The parameter is ignored if the Gomory fractional cut parameter, FRACCUTS, is set to a nonzero value.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.GUBCOVERS||-1 Do not generate GUB cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate GUB cuts moderately<br /> <br /> 2 Generate GUB cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP GUB cuts indicator.<br /> <br /> Determines whether or not to generate GUB cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate GUB cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.HEURFREQ||-1 None<br /> <br /> 0 Automatic<br /> <br /> or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP heuristic frequency.<br /> <br /> Determines how often to apply the periodic heuristic. Setting the value to -1 turns off the periodic heuristic. Setting the value to 0, the default, applies the periodic heuristic at an interval chosen automatically. Setting the value to a positive number applies the heuristic at the requested node interval. For example, setting HEURISTICFREQ to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.IMPLBD||-1 Do not generate implied bound cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate implied bound cuts moderately<br /> <br /> 2 Generate implied bound cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP implied bound cuts indicator.<br /> <br /> Determines whether or not to generate implied bound cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate implied bound cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.INTSOLLIM||Any positive integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP solution limit.<br /> <br /> Set the number of MIP solutions to be found before stopping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ITLIM||Any non-negative integer.<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex maximum iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.LBHEUR||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Local branching heuristic.<br /> <br /> This parameter lets you control whether CPLEX applies a local branching heuristic to try to improve new incumbents found during a MIP search. By default, this parameter is false; that is, it is off by default. If you turn it on, CPLEX will invoke a local branching heuristic only when it finds a new incumbent. If CPLEX finds multiple incumbents at a single node, the local branching heuristic will be applied only to the last one found.<br /> <hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LOCALIMPLBD||-1 Do not generate locally valid implied bound cuts<br /> <br /> 0 Automatic: let CPLEX choose; default.<br /> <br /> 1 Generate locally valid implied bound cuts moderately<br /> <br /> 2 Generate locally valid implied bound cuts aggressively<br /> <br /> 3 Generate locally valid implied bound cuts very aggressively<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP locally valid implied bound cuts switch.<br /> <br /> Instructs CPLEX whether or not to generate locally valid implied bound cuts for the model. The default value 0 (zero) specifies that the attempt to generate locally valid implied bound cuts should continue only if it seems to be helping.<hr/><br /> <br /> |-valign="top"<br /> <br /> |cpxControl.LPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for linear optimization.<br /> <br /> Determines which algorithm is used. Currently, the behavior of the Automatic setting is that CPLEX almost always invokes the dual simplex method. The one exception is when solving the relaxation of an MILP model when multiple threads have been requested. In this case, the Automatic setting will use the concurrent optimization method. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MEMORYEMPHASIS||0 Off: Do not emphasize conservation of memory<br /> <br /> 1 On: Emphasize conservation of memory<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory setting.<br /> <br /> Some information (that require a basis) may be unavailable when using this parameter.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPDISPLAY||0 No display<br /> <br /> 1 Display integer feasible solutions<br /> <br /> 2 Display nodes under MIPINTERVAL<br /> <br /> 3 Same as 2 with information on node cuts<br /> <br /> 4 Same as 3 with LP subproblem information at root<br /> <br /> 5 Same as 4 with LP subproblem information at nodes<br /> <br /> <br /> <br /> Default: 2<br /> <br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log display information.<br /> <br /> Determines what CPLEX reports to the screen during mixed integer optimization. The amount of information displayed increases with increasing values of this parameter.<br /> <br /> A setting of 0 causes no node log to be displayed until the optimal solution is found. A setting of 1 displays an entry for each integer feasible solution found. Each entry contains the objective function value, the node count, the number of unexplored nodes in the tree, and the current optimality gap. A setting of 2 also generates an entry for every nth node (where n is the setting of the MIP INTERVAL parameter). A setting of 3 additionally generates an entry for every nth node giving the number of cuts added to the problem for the previous INTERVAL nodes. A setting of 4 additionally generates entries for the LP root relaxation according to the SIMDISPLAY setting. A setting of 5 additionally generates entries for the LP subproblems, also according to the SIMDISPLAY setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPEMPHASIS||0 [BALANCED] Balance optimality and feasibility<br /> <br /> 1 [FEASIBILITY] Emphasize feasibility over optimality<br /> <br /> 2 [OPTIMALITY] Emphasize optimality over feasibility<br /> <br /> 3 [BESTBOUND] Emphasize moving best bound<br /> <br /> 4 [HIDDENFEAS] Emphasize hidden feasibility<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP emphasis indicator.<br /> <br /> With the default setting of BALANCED, CPLEX works toward a rapid proof of an optimal solution, but balances that with effort toward finding high quality feasible solutions early in the optimization. When set to FEASIBILITY, CPLEX frequently will generate more feasible solutions as it optimizes the problem, at some sacrifice in the speed to the proof of optimality. When set to OPTIMALITY, less effort may be applied to finding feasible solutions early. With the setting BESTBOUND, even greater emphasis is placed on proving optimality through moving the best bound value, so that the detection of feasible solutions along the way becomes almost incidental. When set to HIDDENFEAS, the MIP optimizer works hard to find high quality feasible solutions that are otherwise very difficult to find, so consider this setting when the FEASIBILITY emphasis has difficulty finding solutions of acceptable quality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPINTERVAL||Any positive integer<br /> <br /> <br /> <br /> Default: 100<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node log interval<br /> <br /> Controls the frequency of node logging when MIPDISPLAY is set higher than 1.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDIND||0 Off (do not use order information)<br /> <br /> 1 On (use order information if it exists)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order indicator.<br /> <br /> When set to on, uses the priority order (if it exists) for the next mixed integer optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPORDTYPE||0 Do not generate a priority order<br /> <br /> 1 Use decreasing cost<br /> <br /> 2 Use increasing bound range<br /> <br /> 3 Use increasing cost per coefficient count<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP priority order generation.<br /> <br /> Used to select the type of generic priority order to generate when no priority order is present.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIPSEARCH||0 Automatic: let CPLEX choose.<br /> <br /> 1 Apply traditional branch and cut strategy;<br /> <br /> disable dynamic search<br /> <br /> 2 Apply dynamic search<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP dynamic search switch.<br /> <br /> Sets the search strategy for a mixed integer program (MIP). By default, CPLEX chooses whether to apply dynamic search or conventional branch and cut based on characteristics of the model and presence (or absence) of callbacks. To benefit from dynamic search, a MIP must not include query callbacks.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIQCPSTRAT||0 Automatic: let CPLEX choose.<br /> <br /> 1 Solve a QCP node relaxation at each node<br /> <br /> 2 Solve an LP node relaxation at each node<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIQQ strategy switch.<br /> <br /> Sets the strategy that CPLEX uses to solve a quadratically constrained mixed integer program (MIQQ). When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCP node relaxation of the model at each node. When you set this parameter to the value 2, you tell CPLEX to attempt to solve only an LP node relaxation of the model at each node.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MIRCUTS||-1 Do not generate MIR cuts<br /> <br /> 0 Automatically determined<br /> <br /> 1 Generate MIR cuts moderately<br /> <br /> 2 Generate MIR cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP MIR (mixed integer rounding) cut indicator.<br /> <br /> Determines whether or not to generate MIR cuts for the problem. Setting the value to 0, the default, indicates that the attempt to generate MIR cuts should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.MPSLONGNUM||0: Write file in standard MPS format<br /> <br /> 1 Write with full precision (up to 15 digits)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Degree of precision displayed in output files (MPS).<br /> <br /> When this parameter is set to its default value 1 (one), numbers are written to MPS files in full-precision; that is, up to 15 significant digits may be written. The setting 0 (zero) writes files that correspond to the standard MPS format, where at most 12 characters can be used to represent a value. This limit may result in loss of precision.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETDISPLAY||0 No display<br /> <br /> 1 Display true objective values<br /> <br /> 2 Display penalized objective values<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network logging display indicator.<br /> <br /> Settings 1 and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1 usually does not.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPOPT||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Optimality tolerance for the network optimizer.<br /> <br /> The optimality tolerance specifies the amount a reduced cost may violate the criterion for an optimal solution.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETEPRHS||Any number from 10''-''11 to 10''-''1<br /> <br /> <br /> <br /> Default: 10''-''6<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Feasibility tolerance for the network optimizer.<br /> <br /> The feasibility tolerance specifies the degree to which a problem's flow value may violate its bounds. This tolerance influences the selection of an optimal basis and can be reset to a higher value when a problem is having difficulty maintaining feasibility during optimization. You may also wish to lower this tolerance after finding an optimal solution if there is any doubt that the solution is truly optimal. If the feasibility tolerance is set too low, CPLEX may falsely conclude that a problem is infeasible. If you encounter reports of infeasibility during Phase II of the optimization, a small adjustment in the feasibility tolerance may improve performance.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETFIND||1 Extract pure network only<br /> <br /> 2 Try reflection scaling<br /> <br /> 3 Try general scaling<br /> <br /> <br /> <br /> Default: 2 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex network extraction level.<br /> <br /> Establishes the level of network extraction for network simplex optimizations. The default value is suitable for recognizing commonly used modeling approaches when representing a network problem within an LP formulation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETITLIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex iteration limit.<br /> <br /> Sets the maximum number of iterations to be performed before the algorithm terminates without reaching optimality.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NETPPRIIND||0 Automatic<br /> <br /> 1 Partial pricing<br /> <br /> 2 Multiple partial pricing<br /> <br /> 3 Multiple partial pricing with sorting<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Network Simplex pricing algorithm.<br /> <br /> The default (0) shows best performance for most problems, and currently is equivalent to 3.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODEFILEIND||0 No node file<br /> <br /> 1 Node file in memory and compressed<br /> <br /> 2 Node file on disk<br /> <br /> 3 Node file on disk and compressed<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node storage file indicator.<br /> <br /> Used when working memory, WORKMEM, has been exceeded by the size of the tree. If the node file parameter is set to zero when the tree memory limit is reached, optimization is terminated. Otherwise, a group of nodes is removed from the in-memory set as needed. By default, CPLEX transfers nodes to node files when the in-memory set is larger than 128 MBytes, and it keeps the resulting node 'files' in compressed form in memory. At settings 2 and 3, the node files are transferred to disk, in compressed and uncompressed form respectively, into a directory named by the WORKDIR parameter, and CPLEX actively manages which nodes remain in memory for processing. The use of node files is described in more detail in the CPLEX User's Manual.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODELIM||Any non-negative integer<br /> <br /> <br /> <br /> Default: Large (varies by computer)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node limit.<br /> <br /> Sets the maximum number of nodes solved before the algorithm terminates, without reaching optimality. When this parameter is set to 0 (zero), CPLEX completes processing at the root; that is, cuts are created and heuristics are applied at the root, but no nodes are created. When the parameter is set to 1 (one), CPLEX branches at the root; that is, nodes are created but not solved.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NODESEL||0 Depth-first search<br /> <br /> 1 Best-bound search<br /> <br /> 2 Best-estimate search<br /> <br /> 3 Alternative best-estimate search<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP node selection strategy.<br /> <br /> Used to set the rule for selecting the next node to process when backtracking. The depth-first search strategy chooses the most recently created node. The best-bound strategy chooses the node with the best objective function for the associated LP relaxation. The best-estimate strategy selects the node with the best estimate of the integer objective value that would be obtained from a node once all integer infeasibilities are removed. An alternative best-estimate search is also available.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NUMERICALEMPHASIS||0 Off: Do not emphasize extreme caution in computation<br /> <br /> 1 On: Emphasize extreme caution in computation<br /> <br /> <br /> <br /> Default: Off<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Numerical emphasis.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.NZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Nonzero element read limit.<br /> <br /> Sets the number of nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJDIF||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. This absolute value is subtracted from (added to) the newly found integer objective value when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The OBJDIFFERENCE parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. Negative values for this parameter can result in some integer solutions that are worse than or the same as those previously generated, but does not necessarily result in the generation of all possible integer solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJLLIM||Any number<br /> <br /> <br /> <br /> Default: ''-''1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Lower objective value limit.<br /> <br /> Setting a lower objective function limit causes CPLEX to halt the optimization process once the minimum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.OBJULIM||Any number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper objective value limit.<br /> <br /> Setting an upper objective function limit causes CPLEX to halt the optimization process once the maximum objective function value limit has been exceeded. This limit applies only during Phase II of the simplex method.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PARALLELMODE||-1 Enable opportunistic parallel search mode<br /> <br /> 0 Automatic: let CPLEX decide whether to invoke deterministic or opportunistic search, depending on the threads parameter<br /> <br /> 1 Automatic: Enable deterministic parallel search mode<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Parallel mode switch.<br /> <br /> See [[CPLEX Parallel Mode]].<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation indicator.<br /> <br /> Setting this parameter to 1 causes all problems to be automatically perturbed as optimization begins. A setting of 0 allows CPLEX to determine dynamically, during solution, whether progress is slow enough to merit a perturbation. The situations in which a setting of 1 helps are rare and restricted to problems that exhibit extreme degeneracy.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PERLIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex perturbation limit.<br /> <br /> Sets the number of stalled iterations before perturbation is performed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POLISHTIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Polishing best solution.<br /> <br /> Regulates the amount of time spent on polishing the best solution found. During solution polishing, CPLEX applies its effort to improve the best feasible solution. Polishing can yield better solutions in some situations. The default value of the polishing time parameter is 0 (zero); that is, spend no time polishing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.POPULATELIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: 20<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on number of solutions generated for the solution pool.<br /> <br /> Limits the number of solutions generated for the solution pool during each call to the populate procedure. Populate stops when it has generated POPULATELIM solutions. A solution is counted if it is valid for all filters, consistent with the relative and absolute pool gap parameters, and has not been rejected by the incumbent callback (if any exists), whether or not it improves the objective of the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PPRIIND||-1 Reduced-cost pricing<br /> <br /> 0 Hybrid reduced-cost & devex pricing<br /> <br /> 1 Devex pricing<br /> <br /> 2 Steepest-edge pricing<br /> <br /> 3 Steepest-edge pricing with slack initial norms<br /> <br /> 4 Full pricing<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal Simplex pricing algorithm.<br /> <br /> The default pricing (0) usually provides the fastest solution time, but many problems benefit from alternative settings<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREDUAL||-1 Off<br /> <br /> 0 Automatic<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve dual setting.<br /> <br /> Determines whether CPLEX Presolve should pass the primal or dual linear programming problem to the linear programming optimization algorithm. By default, CPLEX chooses automatically. If the DUAL indicator is set to 1, the CPLEX presolve algorithm is applied to the primal problem, but the resulting dual linear program is passed to the optimizer. This is a useful technique for problems with more constraints than variables.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREIND||0 Off (do not use presolve)<br /> <br /> 1 On (use presolve)<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Presolve indicator.<br /> <br /> When set to 1, invokes the CPLEX Presolve to simplify and reduce problems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRELINEAR||0 Only linear reductions<br /> <br /> 1 Full reductions<br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linear reduction indicator.<br /> <br /> If only linear reductions are performed, each variable in the original model can be expressed as a linear form of variables in the presolved model. This guarantees, for example, that users can add their own custom cuts to the presolved model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PREPASS||-1 Determined automatically<br /> <br /> 0 Do not use Presolve or, any positive integer Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of Presolve passes made.<br /> <br /> When set to a nonzero value, invokes the CPLEX Presolve to simplify and reduce problems. When set to a positive value, the Presolve is applied the specified number of times, or until no more reductions are possible. At the default value of -1, Presolve should continue only if it seems to be helping.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRESLVND||-1 No node presolve<br /> <br /> 0 Automatic<br /> <br /> 1 Force node presolve<br /> <br /> 2 Probes all integer-infeasible variables at each node to find those that can be fixed<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Node presolve selector.<br /> <br /> Indicates whether node presolve should be performed at the nodes of a mixed integer programming solution. Node presolve can significantly reduce solution time for some models. The default setting is generally effective at determining whether to apply node presolve, although runtimes can be reduced for some models by turning node presolve off.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PRICELIM||0 Determined automatically or, any positive integer<br /> <br /> <br /> <br /> Default: 0 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex pricing candidate list size.<br /> <br /> Sets the maximum number of variables kept in the pricing candidate list.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBE||-1 No probing<br /> <br /> 0 Automatic<br /> <br /> 1-3 Probing level<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP probe.<br /> <br /> Determines the amount of probing on variables to be performed before MIP branching. Higher settings perform more probing. Probing can be very powerful but very time consuming at the start. Setting the parameter to values above the default of 0 (automatic) can result in dramatic reductions or dramatic increases in solution time, depending on the model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.PROBETIME||Any positive number in seconds<br /> <br /> <br /> <br /> Default: 1e75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Amount of time spent on probing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMAKEPSDIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Indefinite MIQP indicator.<br /> <br /> Determines whether CPLEX will attempt to adjust a MIQP formulation, in which all the variables appearing in the quadratic term are binary. When this feature is active, adjustments will be made to the elements of a quadratic matrix that is not nominally positive semi-definite ("PSD", as required by CPLEX for all QP formulations), to make it PSD, and will also attempt to tighten an already PSD matrix for better numerical behavior. The default setting of 1 means "yes" but you can turn it off if necessary; most models should benefit from the default setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPMETHOD||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Method for continuous quadratic optimization.<br /> <br /> Determines algorithm. Currently, the behavior of the Automatic setting is that CPLEX invokes the barrier method for continuous QP models, and the dual simplex method for root relaxations of MIQP models. The Automatic setting may be expanded in the future so that CPLEX chooses the method based on additional problem characteristics.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QPNZREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''QP Q matrix nonzero read limit.<br /> <br /> Sets the number of Q matrix nonzeros that can be read.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.QTOLININD||-1 Automatic: let CPLEX decide, '''default'''.<br /> <br /> 0 Off: CPLEX does not linearize quadratic terms in the objective function of QP, MIQP<br /> <br /> 1 On: CPLEX linearizes quadratic terms in the objective function of QP, MIQP<br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Linearization switch for QP and MIQP.<br /> <br /> This parameter switches on or off linearization of the quadratic terms in the objective function of a quadratic program (QP) or of a mixed integer quadratic program (MIQP) during preprocessing.<br /> <br /> In a convex mixed integer quadratic program (MIQP), this parameter controls whether CPLEX linearizes the product of binary variables in the objective function during presolve.<br /> In a nonconvex quadratic program (QP) or mixed integer quadratic program (MIQP) solved to global optimality according to the solution target parameter, this parameter controls how CPLEX linearizes the product of bounded variables in the objective function during presolve.<br /> This parameter interacts with certain settings of the parameter solution target type:<br /> <br /> When the solution target type is set to 1 (one) (that is, CPLEX searches for a globally optimal solution to a convex model), then in a convex MIQP, this linearization switch tells CPLEX to replace the product of a binary variable and a bounded linear variable by a linearly constrained variable.<br /> When the solution target type is set to 3, then in a nonconvex QP or nonconvex MIQP, this linearization switch controls the initial relaxation.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REDUCE||0 No primal and dual reductions<br /> <br /> 1 Only primal reductions<br /> <br /> 2 Only dual reductions<br /> <br /> 3 Both primal and dual reductions<br /> <br /> <br /> <br /> Default: 3 values<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Primal and dual reduction type.<br /> <br /> Determines whether primal reductions, dual reductions, or both, are performed during preprocessing.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REINV||0 Determined automatically<br /> <br /> or, any integer from 1 to 10 000<br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex refactorization frequency.<br /> <br /> Sets the number of iterations between refactorizations of the basis matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELAXPREIND||-1 Determined automatically<br /> <br /> 0 Off (do not use presolve on initial relaxation)<br /> <br /> 1 On (use presolve on initial relaxation)<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxed LP presolve indicator.<br /> <br /> Determines whether LP presolve is applied to the root relaxation in a mixed integer program. Sometimes additional reductions can be made beyond any MIP presolve reductions that were already done.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RELOBJDIF||Any integer from 0.0 to 1.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative objective difference cutoff.<br /> <br /> Used to update the cutoff each time a mixed integer solution is found. The value is multiplied by the absolute value of the integer objective and subtracted from (added to) the newly found integer objective when minimizing (maximizing). This forces the mixed integer optimization to ignore integer solutions that are not at least this amount better than the one found so far. The relative objective difference parameter can be adjusted to improve problem solving efficiency by limiting the number of nodes; however, setting this parameter at a value other than zero (the default) can cause some integer solutions, including the true integer optimum, to be missed. If both RELOBJDIFFERENCE and OBJDIFFERENCE are nonzero, the value of OBJDIFFERENCE is used.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPAIRTRIES||Any positive integer<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Repair tries.<br /> <br /> If a user provides a MIP start (full or partial) that cannot be extended into a feasible solution, CPLEX will try to repair it.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.REPEATPRESOLVE||-1 Automatic: Let CPLEX choose whether to re-apply presolve<br /> <br /> 0 Turn off repeat presolve<br /> <br /> 1 Repeat presolve without cuts<br /> <br /> 2 Repeat presolve with cuts<br /> <br /> 3 Repeat presolve with cuts and allow new root cuts<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP presolve setting.<br /> <br /> How to re-apply the MIP presolve techniques of the preprocessor to a MIP model at the root after preprocessing has otherwise finished (that is, after cut generation at the root).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.RINSHEUR||-1 None<br /> <br /> 0 Automatic (default) or, any positive integer Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relaxation induced neighborhood search heuristic determines how often to apply the relaxation<br /> <br /> induced neighborhood search heuristic (RINS heuristic). Setting the value to -1 turns off the RINS heuristic. Setting the value to 0, the default, applies the RINS heuristic at an interval chosen automatically by CPLEX. Setting the value to a positive number applies the RINS heuristic at the requested node interval. For example, setting RINSHEUR to 20 dictates that the RINS heuristic be called at node 0, 20, 40, 60, etc.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ROWREADLIM||Any integer from 0 to 268 435 450<br /> <br /> <br /> <br /> Default: Varies by computer<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description:'''<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCAIND||-1 No scaling<br /> <br /> 0 Equilibrium scaling method<br /> <br /> 1 More aggressive scaling<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Scale parameter.<br /> <br /> Sets the method to be used for scaling the problem matrix.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SCRIND||0 Off<br /> <br /> 1 On<br /> <br /> <br /> <br /> Default: On<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Messages to screen indicator.<br /> <br /> Indicates whether or not results messages are displayed on screen.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting subproblem algorithm.<br /> <br /> Sets the algorithm to be used for solving sifting subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTDISPLAY||0 No display<br /> <br /> 1 Display major iterations<br /> <br /> 2 Display LP subproblem information within each sifting iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Sifting display information.<br /> <br /> Determines the amount of sifting progress information to be displayed.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIFTITLIM||Any nonnegative integer<br /> <br /> <br /> <br /> Default: BIGINT<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Upper limit on sifting iterations.<br /> <br /> Sets the maximum number of sifting iterations that may be performed if convergence to optimality has not been reached.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SIMDISPLAY||0 No iteration messages until solution<br /> <br /> 1 Iteration info after each refactorization<br /> <br /> 2 Iteration info for each iteration<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex iteration display information.<br /> <br /> Determines how often CPLEX reports during simplex optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SINGLIM||Any positive integer<br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Simplex singularity repair limit.<br /> <br /> Restricts the number of times CPLEX attempts to repair the basis when singularities are encountered. Once this limit is exceeded, CPLEX replaces the current basis with the best factorizable basis that has been found.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLAGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Absolute gap for solution pool.<br /> <br /> Sets an absolute tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the objective of the incumbent solution according to this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLCAPACITY||Any nonnegative integer; 0 (zero) turns off all features of the solution pool.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Limit on the number of solutions kept in the solution pool.<br /> <br /> Limits the number of solutions kept in the solution pool. At most, SOLNPOOLCAPACITY solutions will be stored in the pool. Superfluous solutions are managed according to the replacement strategy set by the solution pool replacement parameter (SOLNPOOLREPLACE). If the solution pool replacement parameter is set to its default value then the value that the user sets for the solution pool capacity parameter will be increased automatically in cases where the pool is extended.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLGAP||Any nonnegative real number.<br /> <br /> <br /> <br /> Default: 1.0e+75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Relative gap for the solution pool.<br /> <br /> Sets a relative tolerance on the objective bound for the solutions in the solution pool. Solutions that are worse (either greater in the case of a minimization, or less in the case of a maximization) than the incumbent solution by this measure are not kept in the solution pool.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLINTENSITY||0 Automatic: let CPLEX choose<br /> <br /> 1 Mild: generate few solutions quickly<br /> <br /> 2 Moderate: generate a larger number of solutions<br /> <br /> 3 Aggressive: generate many solutions and expect performance penalty<br /> <br /> 4 Very aggressive: enumerate all practical solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Controls the trade-off between the number of solutions generated for the solution pool and the<br /> <br /> amount of time or memory consumed. This parameter applies both to MIP optimization and to the populate procedure.<br /> <br /> Values from 1 (one) to 4 invoke increasing effort to find larger numbers of solutions. Higher values are more expensive in terms of time and memory but are likely to yield more solutions.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLNPOOLREPLACE||0 Extend the pool if necessary to accommodate more solutions<br /> <br /> 1 Replace the first solution by the most recent solution; first in, first out<br /> <br /> 2 Replace the solution which has the worst objective<br /> <br /> 3 Replace solutions in order to build a set of diverse solutions<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Solution pool replacement strategy.<br /> <br /> Designates the strategy for replacing a solution in the solution pool when the solution pool has reached its capacity.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SOLUTIONTARGET||0 Automatic; let CPLEX decide, '''default'''. <br /> <br /> 1 Searches for a globally optimal solution to a convex model.<br /> <br /> 2 Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal.<br /> <br /> 3 Searches for a globally optimal solution to a nonconvex model; changes problem type to MIQP if necessary.<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Specifies the type of solution CPLEX attempts to compute when CPLEX solves a nonconvex, continuous or mixed integer quadratic model; that is, nonconvex QP or nonconvex MIQP. In other words, the variables of the model can be continuous or mixed integer and continuous; the objective function includes a quadratic term, and the objective function is not positive semi-definite (non PSD).<br /> <br /> By default, CPLEX first attempts to compute a provably optimal solution to such a problem. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX terminates and returns the error 5002 (Q_NOT_POS_DEF).<br /> <br /> When this parameter is set to 1 (one), CPLEX searches for a globally optimal solution to a convex model. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP.<br /> <br /> When this parameter is set to 2, CPLEX first attempts to compute a provably optimal solution. If CPLEX cannot compute a provably optimal solution because the objective function is not convex, CPLEX searches for a solution that satisfies first-order optimality conditions but is not necessarily globally optimal. In such a case, you can query the solution status to determine the kind of solution that CPLEX found.<br /> <br /> When this parameter is set to 3, if the problem type is QP, CPLEX first changes the problem type to MIQP. CPLEX then solves the problem (whether originally QP or MIQP) to global optimality. In problems of type QP or MIQP, this setting interacts with linearization switch for QP, MIQP (QTOLININD). <br /> <br /> Tip: When the value of this parameter is 3 (that is, you have instructed CPLEX to search for a globally optimal solution to a nonconvex QP or MIQP), then information about dual values is not available for the solution.<br /> <br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STARTALG||0 Automatic<br /> <br /> 1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> 6 Concurrent Dual, Barrier and Primal<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP starting LP algorithm.<br /> <br /> Determines which LP algorithm should be used to solve the initial relaxation of the MIP.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGCANDLIM||Any positive number<br /> <br /> <br /> <br /> Default: 10<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP candidate list<br /> <br /> Controls the length of the candidate list when using the "strong branching" variable selection setting (VARSEL 3).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.STRONGITLIM||Any positive number<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP simplex iterations<br /> <br /> Controls the number of simplex iterations performed on each variable in the candidate list when using the<br /> <br /> "strong branching" variable selection setting (VARSEL 3). The default setting 0 chooses the iteration limit automatically.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBALG||1 Primal Simplex<br /> <br /> 2 Dual Simplex<br /> <br /> 3 Network Simplex<br /> <br /> 4 Barrier<br /> <br /> 5 Sifting<br /> <br /> <br /> <br /> Default: 2<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subproblem LP algorithm.<br /> <br /> Sets the algorithm to be used on MIP subproblems.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SUBMIPNODELIM||Any positive integer.<br /> <br /> <br /> <br /> Default: 500<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP subnode limit. Restricts the number of nodes searched, during application of the relaxation<br /> <br /> induced neighborhood search (RINS) heuristic.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.SYMMETRY||-1 Determines automatically<br /> <br /> 0 Off<br /> <br /> 1 Moderate<br /> <br /> 2 Aggressive<br /> <br /> 3 Very aggressive<br /> <br /> 4 Highly aggressive<br /> <br /> 5 Extremely aggressive ++<br /> <br /> <br /> <br /> Default: -1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Symmetry breaking cuts.<br /> <br /> Determines whether symmetry breaking cuts may be added, during the preprocessing phase, to a MIP model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.THREADS||Minimum: 1<br /> <br /> Maximum: determined by computer<br /> <br /> <br /> <br /> Default: 0 (Determined by CPLEX)<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global default thread count.<br /> <br /> Determines the default number of parallel processes (threads) that will be invoked by any CPLEX parallel optimizer. This provides a convenient way to control parallelism with a single parameter setting.<br /> <hr/><br /> |-valign="top"<br /> <br /> <br /> |cpxControl.TILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Global time limit.<br /> <br /> Sets the maximum time, in seconds, for computations before termination, as measured according to the setting of the CLOCKTYPE parameter. The time limit applies to primal simplex, dual simplex, barrier, and mixed integer optimizations, as well as infeasibility finder computations. (Network simplex and barrier crossover operations are exceptions; these processes do not terminate if the time limit is exceeded.) The time limit includes preprocessing time. For 'hybrid' optimizations (such as network optimization followed by dual or primal simplex, barrier optimization followed by crossover), the cumulative time applies.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TRELIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 1e^75<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tree memory limit.<br /> <br /> Sets an absolute upper limit on the size (in megabytes) of the branch & cut tree. If this limit is exceeded, CPLEX terminates optimization.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGDISPLAY||0 Turn off display<br /> <br /> 1 Display standard, minimal reporting<br /> <br /> 2 Display standard report plus parameter settings being tried<br /> <br /> 3 Display exhaustive report and log<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning information display.<br /> <br /> Specifies the level of information reported by the tuning tool as it works.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGMEASURE||1 Mean average of time to compare different<br /> <br /> parameter sets<br /> <br /> 2 Minmax approach to compare the time of different parameter sets<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning measure.<br /> <br /> Controls the measure for evaluating progress when a model is being tuned.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGREPEAT||Any non-negative number<br /> <br /> <br /> <br /> Default: 1<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning repeater.<br /> <br /> Specifies the number of times tuning is to be repeated on perturbed versions of a given problem. The problem is perturbed automatically by CPLEX permuting its rows and columns. This repetition is helpful when only one problem is being tuned, as repeated perturbation and re-tuning may lead to more robust tuning results.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.TUNINGTILIM||Any non-negative number<br /> <br /> <br /> <br /> Default: 10000<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Tuning time limit.<br /> <br /> Sets a time limit per model.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.VARSEL||-1 Branch on variable with minimum infeasibility<br /> <br /> 0 Branch variable automatically selected<br /> <br /> 1 Branch on variable with maximum infeasibility<br /> <br /> 2 Branch based on pseudo costs<br /> <br /> 3 Strong branching<br /> <br /> 4 Branch based on pseudo reduced costs<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP variable selection strategy.<br /> <br /> Used to set the rule for selecting the branching variable at the node which has been selected for branching. The maximum infeasibility rule chooses the variable with the largest fractional value; the minimum infeasibility rule chooses the variable with the smallest fractional value. The minimum infeasibility rule (-1) may lead more quickly to a first integer feasible solution, but is usually slower overall to reach the optimal integer solution. The maximum infeasibility rule (1) forces larger changes earlier in the tree, which tend to produce faster overall times to reach the optimal integer solution. Pseudo cost (2) variable selection is derived from pseudo-shadow prices. Strong branching (3) causes variable selection based on partially solving a number of subproblems with tentative branches to see which branch is the most promising. This strategy can be effective on large, difficult MIP problems. Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs. The default value (0) allows CPLEX to select the best rule based on the problem and its progress.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKDIR||Default: "."<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Directory for working files.<br /> <br /> Specifies the name of an existing directory into which CPLEX may store temporary working files, such as for<br /> <br /> MIP node files or for out-of-core Barrier.<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.WORKMEM||Any positive number, in megabytes<br /> <br /> <br /> <br /> Default: 128.0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''Memory available for working storage.<br /> <br /> Specifies an upper limit on the amount of central memory, in megabytes, that CPLEX is permitted to use for working files (see WORKDIR).<br /> <hr/><br /> |-valign="top"<br /> <br /> |cpxControl.ZEROHALFCUTS||-1 Do not generate zero-half cuts<br /> <br /> 0 Automatic: let CPLEX choose<br /> <br /> 1 Generate zero-half cuts moderately<br /> <br /> 2 Generate zero-half cuts aggressively<br /> <br /> <br /> <br /> Default: 0<br /> <br /> |-valign="top"<br /> <br /> |colspan="2"|'''Description: '''MIP zero-half cuts switch.<br /> <br /> Decides whether or not to generate zero-half cuts for the problem. The value 0 (zero), the default, specifies that the attempt to generate zero-half cuts should continue only if it seems to be helping. If you find that too much time is spent generating zero-half cuts for your model, consider setting this parameter to -1 (minus one) to turn off zero-half cuts. If the dual bound of your model does not make sufficient progress, consider setting this parameter to 2 to generate zero-half cuts more aggressively.<br /> |-valign="top"<br /> <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=3142 Welcome 2020-10-27T09:27:10Z

<p>WikiSysop: /* Links */</p> <hr /> <div>The TOMLAB Environment is a modeling platform for solving applied optimization problems in [http://en.wikipedia.org/wiki/MATLAB MATLAB].<br /> <br /> ==Description==<br /> TOMLAB is a general purpose development and modeling environment in MATLAB for research, teaching and practical solution of optimization problems. It enables a wider range of problems to be solved in MATLAB and provides many additional solvers.<br /> <br /> ==Optimization problems supported==<br /> TOMLAB handles a wide range of problem types, among them:<br /> * Linear programming<br /> * Quadratic programming<br /> * Nonlinear programming<br /> * Mixed-integer programming<br /> * Mixed-integer quadratic programming with or without convex quadratic constraints<br /> * Mixed-integer nonlinear programming<br /> * Linear and nonlinear least squares with L1, L2 and infinity norm<br /> * Exponential data fitting<br /> * Global optimization<br /> * Semi-definite programming problem with bilinear matrix inequalities<br /> * Constrained goal attainment<br /> * Geometric programming<br /> * Genetic programming<br /> * Costly or expensive black-box global optimization<br /> * Nonlinear complementarity problems<br /> <br /> ==TOMLAB Manuals==<br /> <br /> ===TOMLAB Manual===<br /> Includes general TOMLAB information and solver reference for the TOMLAB Base Module.<br /> * [[TOMLAB|TOMLAB Manual]]<br /> <br /> ===Quickguide===<br /> Basics associated with solving problems using the TOMLAB Optimization Environment.<br /> * [[Quickguide]]<br /> <br /> ===Models===<br /> A short introduction to the TOMLAB Models.<br /> * [[Models]]<br /> <br /> ===SAL===<br /> Standalone execution of TOMLAB. Compilation instructions.<br /> * [[SAL]]<br /> <br /> ===Installation===<br /> Guide for installation of demos and permanently licensed TOMLAB components.<br /> * [[Installation]]<br /> <br /> ==Product User's Guides==<br /> <br /> ====Included in the [[SOL|TOMLAB /SOL manual]]====<br /> <table><br /> <tr><br /> <td><br /> *[[MINOS]]<br /> </td><br /> <td><br /> *[[SOL_NPSOL|NPSOL]]<br /> </td><br /> <td><br /> * [[SNOPT]]<br /> </td><br /> </tr><br /> </table><br /> <br /> ====Standalone====<br /> <br /> <table><br /> <tr><br /> <td><br /> * [[AMPL]]</td><br /> <td><br /> * [[CGO]]</td><br /> <td><br /> * [[CONOPT]]</td><br /> <td><br /> * [[CPLEX]]</td><br /> <td><br /> * [[GP]]</td><br /> </tr><br /> <tr><br /> <td><br /> * [[GENO]]</td><br /> <td><br /> * [[GUROBI]]</td><br /> <td><br /> * [[KNITRO]]</td><br /> <td><br /> * [[LGO]]</td><br /> <td><br /> * [[MAD]]</td><br /> </tr><br /> <tr><br /> <td><br /> * [[MINLP]]</td><br /> <td><br /> * [[MIPNLP]]</td><br /> <td><br /> * [[MISQP]]</td><br /> <td><br /> * [[NLPQL]]</td><br /> <td><br /> * [[OQNLP]]</td><br /> </tr><br /> <tr><br /> <td><br /> * [[PENOPT]]</td><br /> <td><br /> * [[PROPT]]</td><br /> <td><br /> * [[SOL]]</td><br /> <td><br /> * [[TomSym]]</td><br /> <td><br /> * [[Xpress]]</td><br /> </tr><br /> </table><br /> <br /> ==PDF==<br /> <br /> *[[PDF|Manuals in PDF format]]<br /> <br /> ==Links==<br /> <br /> * https://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=1015 Welcome 2011-08-19T09:53:21Z

<p>WikiSysop: /* Product User's Guides */</p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> ==TOMLAB Manuals==<br /> <br /> ===TOMLAB Manual===<br /> Includes general TOMLAB information and solver reference for the TOMLAB Base Module.<br /> * [[TOMLAB|TOMLAB Manual]]<br /> <br /> ===Quickguide===<br /> Basics associated with solving problems using the TOMLAB Optimization Environment.<br /> * [[Quickguide]]<br /> <br /> ===Models===<br /> A short introduction to the TOMLAB Models.<br /> * [[Models]]<br /> <br /> ===SAL===<br /> Standalone execution of TOMLAB. Compilation instructions.<br /> * [[SAL]]<br /> <br /> ===Installation===<br /> Guide for installation of demos and permanently licensed TOMLAB components.<br /> * [[Installation]]<br /> <br /> ==Product User's Guides==<br /> <br /> ===CPLEX===<br /> * [[CPLEX]]<br /> <br /> ===TomSym===<br /> * [[TomSym]]<br /> <br /> ===MINOS===<br /> * [[MINOS]]<br /> <br /> ===NPSOL===<br /> * [[NPSOL]]<br /> <br /> ==Solver User's Guides (only for advanced users)==<br /> <br /> ==Links==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=878 Welcome 2011-08-11T12:36:50Z

<p>WikiSysop: </p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> ==TOMLAB Manuals==<br /> <br /> ===TOMLAB Manual===<br /> Includes general TOMLAB information and solver reference for the TOMLAB Base Module.<br /> * [[TOMLAB|TOMLAB Manual]]<br /> <br /> ===Quickguide===<br /> Basics associated with solving problems using the TOMLAB Optimization Environment.<br /> * [[Quickguide]]<br /> <br /> ===Models===<br /> A short introduction to the TOMLAB Models.<br /> * [[Models]]<br /> <br /> ===SAL===<br /> Standalone execution of TOMLAB. Compilation instructions.<br /> * [[SAL]]<br /> <br /> ===Installation===<br /> Guide for installation of demos and permanently licensed TOMLAB components.<br /> * [[Installation]]<br /> <br /> ==Product User's Guides==<br /> <br /> ===TomSym===<br /> * [[TomSym]]<br /> <br /> ===MINOS===<br /> * [[MINOS]]<br /> <br /> ===NPSOL===<br /> * [[NPSOL]]<br /> <br /> ==Solver User's Guides (only for advanced users)==<br /> <br /> ==Links==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=825 Welcome 2011-08-09T12:23:44Z

<p>WikiSysop: </p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[TOMLAB|The TOMLAB Manual]]<br /> * [[Quickguide]]<br /> * [[Models]]<br /> * [[SAL]]<br /> * [[Installation]]<br /> <br /> ===Product User's Guides===<br /> <br /> * [[TomSym]]<br /> * [[MINOS]]<br /> * [[NPSOL]]<br /> <br /> ===Solver User's Guides (only for advanced users)===<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Template:Citation/make_link&diff=548 Template:Citation/make link 2011-07-12T09:21:03Z

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WikiSysop http://tomwiki.com/index.php?title=TOMLAB_Overall_Design&diff=545 TOMLAB Overall Design 2011-07-12T08:40:27Z

<p>WikiSysop: /* Low Level Routines and Gateway Routines */</p> <hr /> <div>{{Part Of Manual|title=the TOMLAB Manual|link=[[TOMLAB|TOMLAB Manual]]}}<br /> <br /> The scope of TOMLAB is large and broad, and therefore there is a need of a well-designed system. It is also natural to use the power of the Matlab language, to make the system flexible and easy to use and maintain. The concept of structure arrays is used and the ability in Matlab to execute Matlab code defined as string expressions and to execute functions specified by a string.<br /> <br /> ==Structure Input and Output==<br /> <br /> Normally, when solving an optimization problem, a direct call to a solver is made with a long list of parameters in the call. The parameter list is solver-dependent and makes it difficult to make additions and changes to the system.<br /> <br /> TOMLAB solves the problem in two steps. First the problem is defined and stored in a Matlab structure. Then the solver is called with a single argument, the problem structure. Solvers that were not originally developed for the TOMLAB environment needs the usual long list of parameters. This is handled by the driver routine ''tomRun.m'' which can call any available solver, hiding the details of the call from the user. The solver output is collected in a standardized result structure and returned to the user.<br /> <br /> ==Introduction to Solver and Problem Types==<br /> <br /> TOMLAB solves a number of different types of optimization problems. The currently defined types are listed in <xr id="tab:probType" />.<br /> <br /> The global variable ''probType ''contains the current type of optimization problem to be solved. An optimization solver is defined to be of type ''solvType'', where ''solvType ''is any of the ''probType ''entries in Table 1. It is clear that a solver of a certain ''solvType ''is able to solve a problem defined to be of another type. For example, a constrained nonlinear programming solver should be able to solve unconstrained problems, linear and quadratic programs and constrained nonlinear least squares problems. In the graphical user interface and menu system an additional variable ''optType ''is defined to keep track of what type of problem is defined as the main subject. As an example, the user may select the type of optimization to be quadratic programming (''optType ''== 2), then select a particular problem that is a linear programming problem (''probType ''== 8) and then as the solver choose a constrained NLP solver like MINOS (''solvType ''== 3).<br /> <br /> <figtable id="tab:probType"><br /> {| class="wikitable" <br /> |+<caption>The different types of optimization problems defined in TOMLAB.</caption><br /> |-<br /> !probType<br /> !<br /> !Type of optimization problem<br /> |-<br /> |'''uc'''||1||Unconstrained optimization (incl. bound constraints).<br /> |-<br /> |'''qp'''||2||Quadratic programming.<br /> |-<br /> |'''con'''||3||Constrained nonlinear optimization.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares problems (incl. bound constraints).<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-Integer programming.<br /> |-<br /> |'''lp'''||8||Linear programming.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization.<br /> |-<br /> |'''glc'''||10||Global mixed-integer nonlinear programming.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic programming.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear optimization.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear Time Series.<br /> |-<br /> |'''lcp'''||22||Linear Mixed-Complimentary Problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear Mixed-Complimentary Problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> Please note that since the actual numbers used for ''probType ''may change in future releases, it is recommended to use the text abbreviations. See help for ''checkType ''for further information. <br /> <br /> Define ''probSet ''to be a set of defined optimization problems belonging to a certain class of problems of type ''probType''. Each ''probSet ''is physically stored in one file, an ''Init File''. In <xr id="tab:probSet" /> the currently defined problem sets are listed, and new ''probSet ''sets are easily added.<br /> <br /> <figtable id="tab:probSet"><br /> {| class="wikitable" <br /> |+<caption>Defined test problem sets in TOMLAB. probSets marked with * are not part of the standard distribution</caption><br /> |-<br /> !probSet<br /> !probType<br /> !Description of test problem set<br /> |-<br /> |'''uc'''||1||Unconstrained test problems.<br /> |-<br /> |'''qp'''||2||Quadratic programming test problems.<br /> |-<br /> |'''con'''||3||Constrained test problems.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares test problems.<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Linear constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-integer programming problems.<br /> |-<br /> |'''lp'''||8||Linear programming problems.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization test problems.<br /> |-<br /> |'''glc'''||10||Global MINLP test problems.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic problems.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear problems.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear time series problems.<br /> |-<br /> |'''lcp'''||22||Linear mixed-complimentary problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear mixed-complimentary problems.<br /> |-<br /> |'''mgh'''||4||More, Garbow, Hillstrom nonlinear least squares problems.<br /> |-<br /> |'''chs'''||3||Hock-Schittkowski constrained test problems.<br /> |-<br /> |'''uhs'''||1||Hock-Schittkowski unconstrained test problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> The names of the predefined Init Files that do the problem setup, and the corresponding low level routines are constructed as two parts. The first part being the abbreviation of the relevant ''probSet'', see <xr id="tab:probSet" />, and the second part denotes the computed task, shown in <xr id="tab:genName" />. The user normally does not have to define the more complicated functions ''o_d2c ''and ''o_d2r''. It is recommended to supply this information when using solvers which can utilize second order information, such as TOMLAB /KNITRO and TOMLAB /CONOPT.<br /> <br /> <figtable id="tab:genName"><br /> {| class="wikitable" <br /> |+<caption>Names for predefined Init Files and low level m-files in TOMLAB.</caption><br /> |-<br /> !Generic name<br /> !Purpose (''o'' is any probSet, e.g. ''o''=con)<br /> |-<br /> |''o_''prob||Init File that either defines a string matrix with problem names<br /> |-<br /> |''o_''f||Compute objective function <math>f(x)</math>.<br /> |-<br /> |''o_''g||Compute the gradient vector <math>g(x)</math>.<br /> |-<br /> |''o_''H||Compute the Hessian matrix <math>H(x)</math>.<br /> |-<br /> |''o_''c||Compute the vector of constraint functions <math>c(x)</math>.<br /> |-<br /> |''o_''dc||Compute the matrix of constraint normals, <math>\partial c(x) /dx</math>.<br /> |-<br /> |''o_''d2c||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> .<br /> |-<br /> |''o_''r||Compute the residual vector <math>r(x)</math>.<br /> |-<br /> |''o_''J ||Compute the Jacobian matrix <math>J(x)</math>.<br /> |-<br /> |''o_''d2r||Compute the 2nd part of the Hessian matrix, <math>\sum_i r_i(x) \partial^2 r_i(x) / dx^2</math><br /> |-<br /> |}<br /> </figtable><br /> <br /> The Init File has two modes of operation; either return a string matrix with the names of the problems in the ''probSet ''or a structure with all information about the selected problem. All fields in the structure, named ''Prob'', are presented in tables in [[TOMLAB Appendix A|Appendix A]]. Using a structure makes it easy to add new items without too many changes in the rest of the system. For further discussion about the definition of optimization problems in TOMLAB, see [[TOMLAB Defining Problems in TOMLAB|Defining Problems in TOMLAB]].<br /> <br /> There are default values for everything that is possible to set defaults for, and all routines are written in a way that makes it possible for the user to just set an input argument empty and get the default.<br /> <br /> ==The Process of Solving Optimization Problems==<br /> <br /> A flow-chart of the process of optimization in TOMLAB is shown in <xr id="fig:optProcess" />. It is inefficient to use an interactive system. This is symbolized with the ''Standard User'' box in the figure, which directly runs the ''Optimization Driver'', ''tomRun''. The direct solver call is possible for all TOMLAB solvers, if the user has executed ''ProbCheck ''prior to the call. See [[TOMLAB Problem Types and Solver Routines]] for a list of the TOMLAB solvers.<br /> <br /> Depending on the type of problem, the user needs to supply the ''low-level ''routines that calculate the objective function, constraint functions for constrained problems, and also if possible, derivatives. To simplify this coding process so that the work has to be performed only once, TOMLAB provides ''gateway ''routines that ensure that any solver can obtain the values in the correct format.<br /> <br /> For example, when working with a least squares problem, it is natural to code the function that computes the vector of residual functions <math>r_i(x_1,x_2,\ldots)</math>, since a dedicated least squares solver probably operates on the residual while a general nonlinear solver needs a scalar function, in this case <math>f(x) = \frac{1}{2} r^T(x)r(x)</math>. Such issues are automatically handled by the gateway functions.<br /> <br /> <figure id="fig:optProcess"><br /> [[File:The process of optimization in TOMLAB.png|thumb|550px|none|alt=The process of optimization in TOMLAB|The process of optimization in TOMLAB.]]<br /> </figure><br /> <br /> ==Low Level Routines and Gateway Routines==<br /> <br /> ''Low level routines ''are the routines that compute:<br /> <br /> *The objective function value<br /> <br /> *The gradient vector<br /> <br /> *The Hessian matrix (second derivative matrix)<br /> <br /> *The residual vector (for nonlinear least squares problems)<br /> <br /> *The Jacobian matrix (for nonlinear least squares problems)<br /> <br /> *The vector of constraint functions<br /> <br /> *The matrix of constraint normals (the constraint Jacobian)<br /> <br /> *The second part of the second derivative of the Lagrangian function. The last three routines are only needed for constrained problems.<br /> <br /> The names of these routines are defined in the structure fields ''Prob.FUNCS.f'', ''Prob.FUNCS.g'', ''Prob.FUNCS.H ''etc.<br /> <br /> It is the task for the ''Assign ''routine to set the names of the low level m-files. This is done by a call to the routine ''conAssign ''with the names as arguments for example. There are ''Assign ''routines for all problem types handled by TOMLAB. As an example, see 'help conAssign' in MATLAB.<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob = conAssign('f', 'g', 'H', HessPattern, x_L, x_U, Name,x_0,... <br /> pSepFunc, fLowBnd, A, b_L, b_U, 'c', 'dc', 'd2c', ConsPattern,... <br /> c_L, c_U, x_min, x_max, f_opt, x_opt);<br /> </syntaxhighlight ><br /> <br /> Only the low level routines relevant for a certain type of optimization problem need to be coded. There are dummy routines for the others. Numerical differentiation is automatically used for gradient, Jacobian and constraint gradient if the corresponding user routine is non present or left out when calling ''conAssign''. However, the solver always needs more time to estimate the derivatives compared to if the user supplies a code for them. Also the numerical accuracy is lower for estimated derivatives, so it is recommended that the user always tries to code the derivatives, if it is possible. Another option is automatic differentiation with TOMLAB /MAD.<br /> <br /> TOMLAB uses gateway routines (''nlp_f'', ''nlp_g'', ''nlp_H'', ''nlp_c'', ''nlp_dc'', ''nlp_d2c'', ''nlp_r'', ''nlp_J'', ''nlp_d2r''). These routines extract the search directions and line search steps, count iterations, handle separable functions, keep track of the kind of differentiation wanted etc. They also handle the separable NLLS case and NLLS weighting. By the use of global variables, unnecessary evaluations of the user supplied routines are avoided.<br /> <br /> To get a picture of how the low-level routines are used in the system, consider <xr id="fig:chainObjFun" /> and <xr id="fig:chainNumFun" />. <xr id="fig:chainObjFun" /> illustrates the chain of calls when computing the objective function value in ''ucSolve ''for a nonlinear least squares problem defined in ''mgh prob'', ''mgh r ''and ''mgh J''. <xr id="fig:chainNumFun" /> illustrates the chain of calls when computing the numerical approximation of the gradient (by use of the routine ''fdng'') in ''ucSolve ''for an unconstrained problem defined in ''uc_prob ''and ''uc_f''. Information about a problem is stored in the structure variable ''Prob'', described in detail in the tables in Appendix A. This variable is an argument to all low level routines. In the field element ''Prob.user'', problem specific information<br /> <br /> <figure id="fig:chainObjFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_f <==> ls_f <==> nlp_r <==> mgh_r<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the objective function value in ''ucSolve'' for a nonlinear least squares problem defined in ''mgh_prob'', ''mgh_r ''and ''mgh_J''.</caption><br /> </figure><br /> <br /> <figure id="fig:chainNumFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_g <==> fdng <==> nlp_r <==> uc_f<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the numerical approximation of the gradient in ''ucSolve'' for an unconstrained problem defined in ''uc_prob ''and ''uc_f''.</caption><br /> </figure><br /> <br /> needed to evaluate the low level routines are stored. This field is most often used if problem related questions are asked when generating the problem. It is often the case that the user wants to supply the low-level routines with additional information besides the variables ''x'' that are optimized. Any unused fields could be defined in the structure ''Prob'' for that purpose. To avoid potential conflicts with future TOMLAB releases, it is recommended to use subfields of ''Prob.user''. It the user wants to send some variables a, B and C, then, after creating the ''Prob'' structure, these extra variables are added to the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob.user.a=a; <br /> Prob.user.B=B; <br /> Prob.user.C=C;<br /> </syntaxhighlight ><br /> <br /> Then, because the ''Prob'' structure is sent to all low-level routines, in any of these routines the variables are picked out from the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> a = Prob.user.a; <br /> B = Prob.user.B; <br /> C = Prob.user.C;<br /> </syntaxhighlight><br /> <br /> A more detailed description of how to define new problems is given in sections [[TOMLAB Solving Linear Quadratic and Integer Programming Problems]], [[TOMLAB Solving Unconstrained and Constrained Optimization Problems]] and [[TOMLAB Solving Global Optimization Problems]]. The usage of ''Prob.user'' is described in [[TOMLAB Utility Functions#User Supplied Problem Parameters]].<br /> <br /> Different solvers all have different demand on how information should be supplied, i.e. the function to optimize, the gradient vector, the Hessian matrix etc. To be able to code the problem only once, and then use this formulation to run all types of solvers, interface routines that returns information in the format needed for the relevant solver were developed.<br /> <br /> <xr id="tab:func" /> describes the low level test functions and the corresponding Init File routine needed for the predefined constrained optimization ('''con''') problems. For the predefined unconstrained optimization ('''uc''') problems, the global optimization ('''glb, glc''') problems and the quadratic programming problems ('''qp''') similar routines have been defined.<br /> <br /> To conclude, the system design is flexible and easy to expand in many different ways.<br /> <br /> <figtable id="tab:func"><br /> {| class="wikitable" <br /> |+<caption>Generally constrained nonlinear (con) test problems.</caption><br /> |-<br /> !Function<br /> !Description<br /> |-<br /> |''con_prob''||Init File. Does the initialization of the '''con '''test problems. <br /> |-<br /> |''con_f''||Compute the objective function <math>f(x)</math> for '''con''' test problems. <br /> |-<br /> |''con_g''||Compute the gradient <math>g(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_H''||Compute the Hessian matrix <math>H(x)</math> of <math>f(x)</math> for '''con ''' test problems.<br /> |-<br /> |''con_c''||Compute the constraint residuals <math>c(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_dc''||Compute the derivative of the constraint residuals for '''con''' test problems.<br /> |-<br /> |''con_d2c''||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> for '''con ''' test problems.<br /> |-<br /> |''con_fm''||Compute merit function <math>\theta(x_k)</math>.<br /> |-<br /> |''con_gm''||Compute gradient of merit function <math>\theta(x_k)</math>.<br /> |}<br /> </figtable></div>

WikiSysop http://tomwiki.com/index.php?title=TOMLAB_Overall_Design&diff=544 TOMLAB Overall Design 2011-07-12T08:34:39Z

<p>WikiSysop: /* The Process of Solving Optimization Problems */</p> <hr /> <div>{{Part Of Manual|title=the TOMLAB Manual|link=[[TOMLAB|TOMLAB Manual]]}}<br /> <br /> The scope of TOMLAB is large and broad, and therefore there is a need of a well-designed system. It is also natural to use the power of the Matlab language, to make the system flexible and easy to use and maintain. The concept of structure arrays is used and the ability in Matlab to execute Matlab code defined as string expressions and to execute functions specified by a string.<br /> <br /> ==Structure Input and Output==<br /> <br /> Normally, when solving an optimization problem, a direct call to a solver is made with a long list of parameters in the call. The parameter list is solver-dependent and makes it difficult to make additions and changes to the system.<br /> <br /> TOMLAB solves the problem in two steps. First the problem is defined and stored in a Matlab structure. Then the solver is called with a single argument, the problem structure. Solvers that were not originally developed for the TOMLAB environment needs the usual long list of parameters. This is handled by the driver routine ''tomRun.m'' which can call any available solver, hiding the details of the call from the user. The solver output is collected in a standardized result structure and returned to the user.<br /> <br /> ==Introduction to Solver and Problem Types==<br /> <br /> TOMLAB solves a number of different types of optimization problems. The currently defined types are listed in <xr id="tab:probType" />.<br /> <br /> The global variable ''probType ''contains the current type of optimization problem to be solved. An optimization solver is defined to be of type ''solvType'', where ''solvType ''is any of the ''probType ''entries in Table 1. It is clear that a solver of a certain ''solvType ''is able to solve a problem defined to be of another type. For example, a constrained nonlinear programming solver should be able to solve unconstrained problems, linear and quadratic programs and constrained nonlinear least squares problems. In the graphical user interface and menu system an additional variable ''optType ''is defined to keep track of what type of problem is defined as the main subject. As an example, the user may select the type of optimization to be quadratic programming (''optType ''== 2), then select a particular problem that is a linear programming problem (''probType ''== 8) and then as the solver choose a constrained NLP solver like MINOS (''solvType ''== 3).<br /> <br /> <figtable id="tab:probType"><br /> {| class="wikitable" <br /> |+<caption>The different types of optimization problems defined in TOMLAB.</caption><br /> |-<br /> !probType<br /> !<br /> !Type of optimization problem<br /> |-<br /> |'''uc'''||1||Unconstrained optimization (incl. bound constraints).<br /> |-<br /> |'''qp'''||2||Quadratic programming.<br /> |-<br /> |'''con'''||3||Constrained nonlinear optimization.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares problems (incl. bound constraints).<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-Integer programming.<br /> |-<br /> |'''lp'''||8||Linear programming.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization.<br /> |-<br /> |'''glc'''||10||Global mixed-integer nonlinear programming.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic programming.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear optimization.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear Time Series.<br /> |-<br /> |'''lcp'''||22||Linear Mixed-Complimentary Problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear Mixed-Complimentary Problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> Please note that since the actual numbers used for ''probType ''may change in future releases, it is recommended to use the text abbreviations. See help for ''checkType ''for further information. <br /> <br /> Define ''probSet ''to be a set of defined optimization problems belonging to a certain class of problems of type ''probType''. Each ''probSet ''is physically stored in one file, an ''Init File''. In <xr id="tab:probSet" /> the currently defined problem sets are listed, and new ''probSet ''sets are easily added.<br /> <br /> <figtable id="tab:probSet"><br /> {| class="wikitable" <br /> |+<caption>Defined test problem sets in TOMLAB. probSets marked with * are not part of the standard distribution</caption><br /> |-<br /> !probSet<br /> !probType<br /> !Description of test problem set<br /> |-<br /> |'''uc'''||1||Unconstrained test problems.<br /> |-<br /> |'''qp'''||2||Quadratic programming test problems.<br /> |-<br /> |'''con'''||3||Constrained test problems.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares test problems.<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Linear constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-integer programming problems.<br /> |-<br /> |'''lp'''||8||Linear programming problems.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization test problems.<br /> |-<br /> |'''glc'''||10||Global MINLP test problems.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic problems.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear problems.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear time series problems.<br /> |-<br /> |'''lcp'''||22||Linear mixed-complimentary problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear mixed-complimentary problems.<br /> |-<br /> |'''mgh'''||4||More, Garbow, Hillstrom nonlinear least squares problems.<br /> |-<br /> |'''chs'''||3||Hock-Schittkowski constrained test problems.<br /> |-<br /> |'''uhs'''||1||Hock-Schittkowski unconstrained test problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> The names of the predefined Init Files that do the problem setup, and the corresponding low level routines are constructed as two parts. The first part being the abbreviation of the relevant ''probSet'', see <xr id="tab:probSet" />, and the second part denotes the computed task, shown in <xr id="tab:genName" />. The user normally does not have to define the more complicated functions ''o_d2c ''and ''o_d2r''. It is recommended to supply this information when using solvers which can utilize second order information, such as TOMLAB /KNITRO and TOMLAB /CONOPT.<br /> <br /> <figtable id="tab:genName"><br /> {| class="wikitable" <br /> |+<caption>Names for predefined Init Files and low level m-files in TOMLAB.</caption><br /> |-<br /> !Generic name<br /> !Purpose (''o'' is any probSet, e.g. ''o''=con)<br /> |-<br /> |''o_''prob||Init File that either defines a string matrix with problem names<br /> |-<br /> |''o_''f||Compute objective function <math>f(x)</math>.<br /> |-<br /> |''o_''g||Compute the gradient vector <math>g(x)</math>.<br /> |-<br /> |''o_''H||Compute the Hessian matrix <math>H(x)</math>.<br /> |-<br /> |''o_''c||Compute the vector of constraint functions <math>c(x)</math>.<br /> |-<br /> |''o_''dc||Compute the matrix of constraint normals, <math>\partial c(x) /dx</math>.<br /> |-<br /> |''o_''d2c||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> .<br /> |-<br /> |''o_''r||Compute the residual vector <math>r(x)</math>.<br /> |-<br /> |''o_''J ||Compute the Jacobian matrix <math>J(x)</math>.<br /> |-<br /> |''o_''d2r||Compute the 2nd part of the Hessian matrix, <math>\sum_i r_i(x) \partial^2 r_i(x) / dx^2</math><br /> |-<br /> |}<br /> </figtable><br /> <br /> The Init File has two modes of operation; either return a string matrix with the names of the problems in the ''probSet ''or a structure with all information about the selected problem. All fields in the structure, named ''Prob'', are presented in tables in [[TOMLAB Appendix A|Appendix A]]. Using a structure makes it easy to add new items without too many changes in the rest of the system. For further discussion about the definition of optimization problems in TOMLAB, see [[TOMLAB Defining Problems in TOMLAB|Defining Problems in TOMLAB]].<br /> <br /> There are default values for everything that is possible to set defaults for, and all routines are written in a way that makes it possible for the user to just set an input argument empty and get the default.<br /> <br /> ==The Process of Solving Optimization Problems==<br /> <br /> A flow-chart of the process of optimization in TOMLAB is shown in <xr id="fig:optProcess" />. It is inefficient to use an interactive system. This is symbolized with the ''Standard User'' box in the figure, which directly runs the ''Optimization Driver'', ''tomRun''. The direct solver call is possible for all TOMLAB solvers, if the user has executed ''ProbCheck ''prior to the call. See [[TOMLAB Problem Types and Solver Routines]] for a list of the TOMLAB solvers.<br /> <br /> Depending on the type of problem, the user needs to supply the ''low-level ''routines that calculate the objective function, constraint functions for constrained problems, and also if possible, derivatives. To simplify this coding process so that the work has to be performed only once, TOMLAB provides ''gateway ''routines that ensure that any solver can obtain the values in the correct format.<br /> <br /> For example, when working with a least squares problem, it is natural to code the function that computes the vector of residual functions <math>r_i(x_1,x_2,\ldots)</math>, since a dedicated least squares solver probably operates on the residual while a general nonlinear solver needs a scalar function, in this case <math>f(x) = \frac{1}{2} r^T(x)r(x)</math>. Such issues are automatically handled by the gateway functions.<br /> <br /> <figure id="fig:optProcess"><br /> [[File:The process of optimization in TOMLAB.png|thumb|550px|none|alt=The process of optimization in TOMLAB|The process of optimization in TOMLAB.]]<br /> </figure><br /> <br /> ==Low Level Routines and Gateway Routines==<br /> <br /> ''Low level routines ''are the routines that compute:<br /> <br /> *The objective function value<br /> <br /> *The gradient vector<br /> <br /> *The Hessian matrix (second derivative matrix)<br /> <br /> *The residual vector (for nonlinear least squares problems)<br /> <br /> *The Jacobian matrix (for nonlinear least squares problems)<br /> <br /> *The vector of constraint functions<br /> <br /> *The matrix of constraint normals (the constraint Jacobian)<br /> <br /> *The second part of the second derivative of the Lagrangian function. The last three routines are only needed for constrained problems.<br /> <br /> The names of these routines are defined in the structure fields ''Prob.FUNCS.f'', ''Prob.FUNCS.g'', ''Prob.FUNCS.H ''etc.<br /> <br /> It is the task for the ''Assign ''routine to set the names of the low level m-files. This is done by a call to the routine ''conAssign ''with the names as arguments for example. There are ''Assign ''routines for all problem types handled by TOMLAB. As an example, see 'help conAssign' in MATLAB.<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob = conAssign('f', 'g', 'H', HessPattern, x_L, x_U, Name,x_0,... <br /> pSepFunc, fLowBnd, A, b_L, b_U, 'c', 'dc', 'd2c', ConsPattern,... <br /> c_L, c_U, x_min, x_max, f_opt, x_opt);<br /> </syntaxhighlight ><br /> <br /> Only the low level routines relevant for a certain type of optimization problem need to be coded. There are dummy routines for the others. Numerical differentiation is automatically used for gradient, Jacobian and constraint gradient if the corresponding user routine is non present or left out when calling ''conAssign''. However, the solver always needs more time to estimate the derivatives compared to if the user supplies a code for them. Also the numerical accuracy is lower for estimated derivatives, so it is recommended that the user always tries to code the derivatives, if it is possible. Another option is automatic differentiation with TOMLAB /MAD.<br /> <br /> TOMLAB uses gateway routines (''nlp_f'', ''nlp_g'', ''nlp_H'', ''nlp_c'', ''nlp_dc'', ''nlp_d2c'', ''nlp_r'', ''nlp_J'', ''nlp_d2r''). These routines extract the search directions and line search steps, count iterations, handle separable functions, keep track of the kind of differentiation wanted etc. They also handle the separable NLLS case and NLLS weighting. By the use of global variables, unnecessary evaluations of the user supplied routines are avoided.<br /> <br /> To get a picture of how the low-level routines are used in the system, consider <xr id="fig:chainObjFun" /> and <xr id="fig:chainNumFun" />. <xr id="fig:chainObjFun" /> illustrates the chain of calls when computing the objective function value in ''ucSolve ''for a nonlinear least squares problem defined in ''mgh prob'', ''mgh r ''and ''mgh J''. <xr id="fig:chainNumFun" /> illustrates the chain of calls when computing the numerical approximation of the gradient (by use of the routine ''fdng'') in ''ucSolve ''for an unconstrained problem defined in ''uc_prob ''and ''uc_f''. Information about a problem is stored in the structure variable ''Prob'', described in detail in the tables in Appendix A. This variable is an argument to all low level routines. In the field element ''Prob.user'', problem specific information<br /> <br /> <figure id="fig:chainObjFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_f <==> ls_f <==> nlp_r <==> mgh_r<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the objective function value in ''ucSolve'' for a nonlinear least squares problem defined in ''mgh_prob'', ''mgh_r ''and ''mgh_J''.</caption><br /> </figure><br /> <br /> <figure id="fig:chainNumFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_g <==> fdng <==> nlp_r <==> uc_f<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the numerical approximation of the gradient in ''ucSolve'' for an unconstrained problem defined in ''uc_prob ''and ''uc_f''.</caption><br /> </figure><br /> <br /> needed to evaluate the low level routines are stored. This field is most often used if problem related questions are asked when generating the problem. It is often the case that the user wants to supply the low-level routines with additional information besides the variables ''x'' that are optimized. Any unused fields could be defined in the structure ''Prob'' for that purpose. To avoid potential conflicts with future TOMLAB releases, it is recommended to use subfields of ''Prob.user''. It the user wants to send some variables a, B and C, then, after creating the ''Prob'' structure, these extra variables are added to the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob.user.a=a; <br /> Prob.user.B=B; <br /> Prob.user.C=C;<br /> </syntaxhighlight ><br /> <br /> Then, because the ''Prob'' structure is sent to all low-level routines, in any of these routines the variables are picked out from the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> a = Prob.user.a; <br /> B = Prob.user.B; <br /> C = Prob.user.C;<br /> </syntaxhighlight><br /> <br /> A more detailed description of how to define new problems is given in sections [[Users Guide Section 5|5]], [[Users Guide Section 6|6]] and [[Users Guide Section 8|8]]. The usage of ''Prob.user'' is described in [[Users Guide Section 14#User Supplied Problem Parameters|Section 14]].<br /> <br /> Different solvers all have different demand on how information should be supplied, i.e. the function to optimize, the gradient vector, the Hessian matrix etc. To be able to code the problem only once, and then use this formulation to run all types of solvers, interface routines that returns information in the format needed for the relevant solver were developed.<br /> <br /> <xr id="tab:func" /> describes the low level test functions and the corresponding Init File routine needed for the predefined constrained optimization ('''con''') problems. For the predefined unconstrained optimization ('''uc''') problems, the global optimization ('''glb, glc''') problems and the quadratic programming problems ('''qp''') similar routines have been defined.<br /> <br /> To conclude, the system design is flexible and easy to expand in many different ways.<br /> <br /> <figtable id="tab:func"><br /> {| class="wikitable" <br /> |+<caption>Generally constrained nonlinear (con) test problems.</caption><br /> |-<br /> !Function<br /> !Description<br /> |-<br /> |''con_prob''||Init File. Does the initialization of the '''con '''test problems. <br /> |-<br /> |''con_f''||Compute the objective function <math>f(x)</math> for '''con''' test problems. <br /> |-<br /> |''con_g''||Compute the gradient <math>g(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_H''||Compute the Hessian matrix <math>H(x)</math> of <math>f(x)</math> for '''con ''' test problems.<br /> |-<br /> |''con_c''||Compute the constraint residuals <math>c(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_dc''||Compute the derivative of the constraint residuals for '''con''' test problems.<br /> |-<br /> |''con_d2c''||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> for '''con ''' test problems.<br /> |-<br /> |''con_fm''||Compute merit function <math>\theta(x_k)</math>.<br /> |-<br /> |''con_gm''||Compute gradient of merit function <math>\theta(x_k)</math>.<br /> |}<br /> </figtable></div>

WikiSysop http://tomwiki.com/index.php?title=TOMLAB_Overall_Design&diff=543 TOMLAB Overall Design 2011-07-12T08:25:36Z

<p>WikiSysop: /* Introduction to Solver and Problem Types */</p> <hr /> <div>{{Part Of Manual|title=the TOMLAB Manual|link=[[TOMLAB|TOMLAB Manual]]}}<br /> <br /> The scope of TOMLAB is large and broad, and therefore there is a need of a well-designed system. It is also natural to use the power of the Matlab language, to make the system flexible and easy to use and maintain. The concept of structure arrays is used and the ability in Matlab to execute Matlab code defined as string expressions and to execute functions specified by a string.<br /> <br /> ==Structure Input and Output==<br /> <br /> Normally, when solving an optimization problem, a direct call to a solver is made with a long list of parameters in the call. The parameter list is solver-dependent and makes it difficult to make additions and changes to the system.<br /> <br /> TOMLAB solves the problem in two steps. First the problem is defined and stored in a Matlab structure. Then the solver is called with a single argument, the problem structure. Solvers that were not originally developed for the TOMLAB environment needs the usual long list of parameters. This is handled by the driver routine ''tomRun.m'' which can call any available solver, hiding the details of the call from the user. The solver output is collected in a standardized result structure and returned to the user.<br /> <br /> ==Introduction to Solver and Problem Types==<br /> <br /> TOMLAB solves a number of different types of optimization problems. The currently defined types are listed in <xr id="tab:probType" />.<br /> <br /> The global variable ''probType ''contains the current type of optimization problem to be solved. An optimization solver is defined to be of type ''solvType'', where ''solvType ''is any of the ''probType ''entries in Table 1. It is clear that a solver of a certain ''solvType ''is able to solve a problem defined to be of another type. For example, a constrained nonlinear programming solver should be able to solve unconstrained problems, linear and quadratic programs and constrained nonlinear least squares problems. In the graphical user interface and menu system an additional variable ''optType ''is defined to keep track of what type of problem is defined as the main subject. As an example, the user may select the type of optimization to be quadratic programming (''optType ''== 2), then select a particular problem that is a linear programming problem (''probType ''== 8) and then as the solver choose a constrained NLP solver like MINOS (''solvType ''== 3).<br /> <br /> <figtable id="tab:probType"><br /> {| class="wikitable" <br /> |+<caption>The different types of optimization problems defined in TOMLAB.</caption><br /> |-<br /> !probType<br /> !<br /> !Type of optimization problem<br /> |-<br /> |'''uc'''||1||Unconstrained optimization (incl. bound constraints).<br /> |-<br /> |'''qp'''||2||Quadratic programming.<br /> |-<br /> |'''con'''||3||Constrained nonlinear optimization.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares problems (incl. bound constraints).<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-Integer programming.<br /> |-<br /> |'''lp'''||8||Linear programming.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization.<br /> |-<br /> |'''glc'''||10||Global mixed-integer nonlinear programming.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic programming.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear optimization.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear Time Series.<br /> |-<br /> |'''lcp'''||22||Linear Mixed-Complimentary Problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear Mixed-Complimentary Problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> Please note that since the actual numbers used for ''probType ''may change in future releases, it is recommended to use the text abbreviations. See help for ''checkType ''for further information. <br /> <br /> Define ''probSet ''to be a set of defined optimization problems belonging to a certain class of problems of type ''probType''. Each ''probSet ''is physically stored in one file, an ''Init File''. In <xr id="tab:probSet" /> the currently defined problem sets are listed, and new ''probSet ''sets are easily added.<br /> <br /> <figtable id="tab:probSet"><br /> {| class="wikitable" <br /> |+<caption>Defined test problem sets in TOMLAB. probSets marked with * are not part of the standard distribution</caption><br /> |-<br /> !probSet<br /> !probType<br /> !Description of test problem set<br /> |-<br /> |'''uc'''||1||Unconstrained test problems.<br /> |-<br /> |'''qp'''||2||Quadratic programming test problems.<br /> |-<br /> |'''con'''||3||Constrained test problems.<br /> |-<br /> |'''ls'''||4||Nonlinear least squares test problems.<br /> |-<br /> |'''lls'''||5||Linear least squares problems.<br /> |-<br /> |'''cls'''||6||Linear constrained nonlinear least squares problems.<br /> |-<br /> |'''mip'''||7||Mixed-integer programming problems.<br /> |-<br /> |'''lp'''||8||Linear programming problems.<br /> |-<br /> |'''glb'''||9||Box-bounded global optimization test problems.<br /> |-<br /> |'''glc'''||10||Global MINLP test problems.<br /> |-<br /> |'''miqp'''||11||Constrained mixed-integer quadratic problems.<br /> |-<br /> |'''minlp'''||12||Constrained mixed-integer nonlinear problems.<br /> |-<br /> |'''lmi'''||13||Semi-definite programming with Linear Matrix Inequalities.<br /> |-<br /> |'''bmi'''||14||Semi-definite programming with Bilinear Matrix Inequalities.<br /> |-<br /> |'''exp'''||15||Exponential fitting problems.<br /> |-<br /> |'''nts'''||16||Nonlinear time series problems.<br /> |-<br /> |'''lcp'''||22||Linear mixed-complimentary problems.<br /> |-<br /> |'''mcp'''||23||Nonlinear mixed-complimentary problems.<br /> |-<br /> |'''mgh'''||4||More, Garbow, Hillstrom nonlinear least squares problems.<br /> |-<br /> |'''chs'''||3||Hock-Schittkowski constrained test problems.<br /> |-<br /> |'''uhs'''||1||Hock-Schittkowski unconstrained test problems.<br /> |-<br /> |}<br /> </figtable><br /> <br /> The names of the predefined Init Files that do the problem setup, and the corresponding low level routines are constructed as two parts. The first part being the abbreviation of the relevant ''probSet'', see <xr id="tab:probSet" />, and the second part denotes the computed task, shown in <xr id="tab:genName" />. The user normally does not have to define the more complicated functions ''o_d2c ''and ''o_d2r''. It is recommended to supply this information when using solvers which can utilize second order information, such as TOMLAB /KNITRO and TOMLAB /CONOPT.<br /> <br /> <figtable id="tab:genName"><br /> {| class="wikitable" <br /> |+<caption>Names for predefined Init Files and low level m-files in TOMLAB.</caption><br /> |-<br /> !Generic name<br /> !Purpose (''o'' is any probSet, e.g. ''o''=con)<br /> |-<br /> |''o_''prob||Init File that either defines a string matrix with problem names<br /> |-<br /> |''o_''f||Compute objective function <math>f(x)</math>.<br /> |-<br /> |''o_''g||Compute the gradient vector <math>g(x)</math>.<br /> |-<br /> |''o_''H||Compute the Hessian matrix <math>H(x)</math>.<br /> |-<br /> |''o_''c||Compute the vector of constraint functions <math>c(x)</math>.<br /> |-<br /> |''o_''dc||Compute the matrix of constraint normals, <math>\partial c(x) /dx</math>.<br /> |-<br /> |''o_''d2c||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> .<br /> |-<br /> |''o_''r||Compute the residual vector <math>r(x)</math>.<br /> |-<br /> |''o_''J ||Compute the Jacobian matrix <math>J(x)</math>.<br /> |-<br /> |''o_''d2r||Compute the 2nd part of the Hessian matrix, <math>\sum_i r_i(x) \partial^2 r_i(x) / dx^2</math><br /> |-<br /> |}<br /> </figtable><br /> <br /> The Init File has two modes of operation; either return a string matrix with the names of the problems in the ''probSet ''or a structure with all information about the selected problem. All fields in the structure, named ''Prob'', are presented in tables in [[TOMLAB Appendix A|Appendix A]]. Using a structure makes it easy to add new items without too many changes in the rest of the system. For further discussion about the definition of optimization problems in TOMLAB, see [[TOMLAB Defining Problems in TOMLAB|Defining Problems in TOMLAB]].<br /> <br /> There are default values for everything that is possible to set defaults for, and all routines are written in a way that makes it possible for the user to just set an input argument empty and get the default.<br /> <br /> ==The Process of Solving Optimization Problems==<br /> <br /> A flow-chart of the process of optimization in TOMLAB is shown in <xr id="fig:optProcess" />. It is inefficient to use an interactive system. This is symbolized with the ''Standard User'' box in the figure, which directly runs the ''Optimization Driver'', ''tomRun''. The direct solver call is possible for all TOMLAB solvers, if the user has executed ''ProbCheck ''prior to the call. See [[Users Guide Section 3|Section 3]] for a list of the TOMLAB solvers.<br /> <br /> Depending on the type of problem, the user needs to supply the ''low-level ''routines that calculate the objective function, constraint functions for constrained problems, and also if possible, derivatives. To simplify this coding process so that the work has to be performed only once, TOMLAB provides ''gateway ''routines that ensure that any solver can obtain the values in the correct format.<br /> <br /> For example, when working with a least squares problem, it is natural to code the function that computes the vector of residual functions <math>r_i(x_1,x_2,\ldots)</math>, since a dedicated least squares solver probably operates on the residual while a general nonlinear solver needs a scalar function, in this case <math>f(x) = \frac{1}{2} r^T(x)r(x)</math>. Such issues are automatically handled by the gateway functions.<br /> <br /> <figure id="fig:optProcess"><br /> [[File:The process of optimization in TOMLAB.png|thumb|550px|none|alt=The process of optimization in TOMLAB|The process of optimization in TOMLAB.]]<br /> </figure><br /> <br /> ==Low Level Routines and Gateway Routines==<br /> <br /> ''Low level routines ''are the routines that compute:<br /> <br /> *The objective function value<br /> <br /> *The gradient vector<br /> <br /> *The Hessian matrix (second derivative matrix)<br /> <br /> *The residual vector (for nonlinear least squares problems)<br /> <br /> *The Jacobian matrix (for nonlinear least squares problems)<br /> <br /> *The vector of constraint functions<br /> <br /> *The matrix of constraint normals (the constraint Jacobian)<br /> <br /> *The second part of the second derivative of the Lagrangian function. The last three routines are only needed for constrained problems.<br /> <br /> The names of these routines are defined in the structure fields ''Prob.FUNCS.f'', ''Prob.FUNCS.g'', ''Prob.FUNCS.H ''etc.<br /> <br /> It is the task for the ''Assign ''routine to set the names of the low level m-files. This is done by a call to the routine ''conAssign ''with the names as arguments for example. There are ''Assign ''routines for all problem types handled by TOMLAB. As an example, see 'help conAssign' in MATLAB.<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob = conAssign('f', 'g', 'H', HessPattern, x_L, x_U, Name,x_0,... <br /> pSepFunc, fLowBnd, A, b_L, b_U, 'c', 'dc', 'd2c', ConsPattern,... <br /> c_L, c_U, x_min, x_max, f_opt, x_opt);<br /> </syntaxhighlight ><br /> <br /> Only the low level routines relevant for a certain type of optimization problem need to be coded. There are dummy routines for the others. Numerical differentiation is automatically used for gradient, Jacobian and constraint gradient if the corresponding user routine is non present or left out when calling ''conAssign''. However, the solver always needs more time to estimate the derivatives compared to if the user supplies a code for them. Also the numerical accuracy is lower for estimated derivatives, so it is recommended that the user always tries to code the derivatives, if it is possible. Another option is automatic differentiation with TOMLAB /MAD.<br /> <br /> TOMLAB uses gateway routines (''nlp_f'', ''nlp_g'', ''nlp_H'', ''nlp_c'', ''nlp_dc'', ''nlp_d2c'', ''nlp_r'', ''nlp_J'', ''nlp_d2r''). These routines extract the search directions and line search steps, count iterations, handle separable functions, keep track of the kind of differentiation wanted etc. They also handle the separable NLLS case and NLLS weighting. By the use of global variables, unnecessary evaluations of the user supplied routines are avoided.<br /> <br /> To get a picture of how the low-level routines are used in the system, consider <xr id="fig:chainObjFun" /> and <xr id="fig:chainNumFun" />. <xr id="fig:chainObjFun" /> illustrates the chain of calls when computing the objective function value in ''ucSolve ''for a nonlinear least squares problem defined in ''mgh prob'', ''mgh r ''and ''mgh J''. <xr id="fig:chainNumFun" /> illustrates the chain of calls when computing the numerical approximation of the gradient (by use of the routine ''fdng'') in ''ucSolve ''for an unconstrained problem defined in ''uc_prob ''and ''uc_f''. Information about a problem is stored in the structure variable ''Prob'', described in detail in the tables in Appendix A. This variable is an argument to all low level routines. In the field element ''Prob.user'', problem specific information<br /> <br /> <figure id="fig:chainObjFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_f <==> ls_f <==> nlp_r <==> mgh_r<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the objective function value in ''ucSolve'' for a nonlinear least squares problem defined in ''mgh_prob'', ''mgh_r ''and ''mgh_J''.</caption><br /> </figure><br /> <br /> <figure id="fig:chainNumFun"><br /> <syntaxhighlight lang="matlab"><br /> ucSolve <==> nlp_g <==> fdng <==> nlp_r <==> uc_f<br /> </syntaxhighlight><br /> <caption>The chain of calls when computing the numerical approximation of the gradient in ''ucSolve'' for an unconstrained problem defined in ''uc_prob ''and ''uc_f''.</caption><br /> </figure><br /> <br /> needed to evaluate the low level routines are stored. This field is most often used if problem related questions are asked when generating the problem. It is often the case that the user wants to supply the low-level routines with additional information besides the variables ''x'' that are optimized. Any unused fields could be defined in the structure ''Prob'' for that purpose. To avoid potential conflicts with future TOMLAB releases, it is recommended to use subfields of ''Prob.user''. It the user wants to send some variables a, B and C, then, after creating the ''Prob'' structure, these extra variables are added to the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> Prob.user.a=a; <br /> Prob.user.B=B; <br /> Prob.user.C=C;<br /> </syntaxhighlight ><br /> <br /> Then, because the ''Prob'' structure is sent to all low-level routines, in any of these routines the variables are picked out from the structure:<br /> <br /> <syntaxhighlight lang="matlab"><br /> a = Prob.user.a; <br /> B = Prob.user.B; <br /> C = Prob.user.C;<br /> </syntaxhighlight><br /> <br /> A more detailed description of how to define new problems is given in sections [[Users Guide Section 5|5]], [[Users Guide Section 6|6]] and [[Users Guide Section 8|8]]. The usage of ''Prob.user'' is described in [[Users Guide Section 14#User Supplied Problem Parameters|Section 14]].<br /> <br /> Different solvers all have different demand on how information should be supplied, i.e. the function to optimize, the gradient vector, the Hessian matrix etc. To be able to code the problem only once, and then use this formulation to run all types of solvers, interface routines that returns information in the format needed for the relevant solver were developed.<br /> <br /> <xr id="tab:func" /> describes the low level test functions and the corresponding Init File routine needed for the predefined constrained optimization ('''con''') problems. For the predefined unconstrained optimization ('''uc''') problems, the global optimization ('''glb, glc''') problems and the quadratic programming problems ('''qp''') similar routines have been defined.<br /> <br /> To conclude, the system design is flexible and easy to expand in many different ways.<br /> <br /> <figtable id="tab:func"><br /> {| class="wikitable" <br /> |+<caption>Generally constrained nonlinear (con) test problems.</caption><br /> |-<br /> !Function<br /> !Description<br /> |-<br /> |''con_prob''||Init File. Does the initialization of the '''con '''test problems. <br /> |-<br /> |''con_f''||Compute the objective function <math>f(x)</math> for '''con''' test problems. <br /> |-<br /> |''con_g''||Compute the gradient <math>g(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_H''||Compute the Hessian matrix <math>H(x)</math> of <math>f(x)</math> for '''con ''' test problems.<br /> |-<br /> |''con_c''||Compute the constraint residuals <math>c(x)</math> for '''con''' test problems.<br /> |-<br /> |''con_dc''||Compute the derivative of the constraint residuals for '''con''' test problems.<br /> |-<br /> |''con_d2c''||Compute the 2nd part of 2nd derivative matrix of the Lagrangian function, <math>\sum_i \lambda_i \partial^2 c_i(x) / dx^2</math> for '''con ''' test problems.<br /> |-<br /> |''con_fm''||Compute merit function <math>\theta(x_k)</math>.<br /> |-<br /> |''con_gm''||Compute gradient of merit function <math>\theta(x_k)</math>.<br /> |}<br /> </figtable></div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=402 Welcome 2011-07-08T15:15:27Z

<p>WikiSysop: /* Manuals */</p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[TOMLAB|The TOMLAB Manual]]<br /> * [[Quickguide]]<br /> * [[Models]]<br /> * [[SAL]]<br /> * [[Installation]]<br /> <br /> ===Product User's Guides===<br /> <br /> * [[MINOS]]<br /> * [[NPSOL]]<br /> <br /> ===Solver User's Guides (only for advanced users)===<br /> <br /> * [[NLPJOB Version 2.0: A Fortran Code for Multicriteria Optimization - User's Guide]]<br /> * [[NLPQLP: A New Fortran Implementation of a Sequential Quadratic Programming Algorithm for Parallel Computing]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=TOMLAB&diff=263 TOMLAB 2011-07-08T12:12:10Z

<p>WikiSysop: moved Users Guide to TOMLAB Manual</p> <hr /> <div>==Introduction==<br /> <br /> *[[Users Guide Introduction|Introduction]]<br /> <br /> ==Overall Design==<br /> Overall Design presents the general design of TOMLAB.<br /> *[[Users Guide Overall Design|Overall Design]]<br /> <br /> ==Problem Types and Solver Routines==<br /> This section contains strict mathematical definitions of the optimization problem types. All solver routines available in TOMLAB are described.<br /> *[[Users Guide Problem Types and Solver Routines|Problem Types and Solver Routines]]<br /> <br /> ==Defining Problems in TOMLAB==<br /> *[[Users Guide Defining Problems in TOMLAB|Defining Problems in TOMLAB]]<br /> <br /> ==Solving Linear, Quadratic and Integer Programming Problems==<br /> This section contain examples on the process of defining problems and solving them. All test examples are available as part of the TOMLAB distribution. <br /> *[[Users Guide Solving Linear Quadratic and Integer Programming Problems|Solving Linear, Quadratic and Integer Programming Problems]]<br /> <br /> ==Solving Unconstrained and Constrained Optimization Problems==<br /> This section contain examples on the process of defining problems and solving them. All test examples are available as part of the TOMLAB distribution. <br /> *[[Users Guide Solving Unconstrained and Constrained Optimization Problems|Solving Unconstrained and Constrained Optimization Problems]]<br /> <br /> ==Solving Global Optimization Problems==<br /> This section contain examples on the process of defining problems and solving them. All test examples are available as part of the TOMLAB distribution. <br /> *[[Users Guide Solving Global Optimization Problems|Solving Global Optimization Problems]]<br /> <br /> ==Solving Least Squares and Parameter Estimation Problems==<br /> This section contain examples on the process of defining problems and solving them. All test examples are available as part of the TOMLAB distribution. <br /> *[[Users Guide Solving Least Squares and Parameter Estimation Problems|Solving Least Squares and Parameter Estimation Problems]]<br /> <br /> ==Multi Layer Optimization==<br /> This section shows to setup and define multi layer optimization problems in TOMLAB. <br /> *[[Users Guide Multi Layer Optimization|Multi Layer Optimization]]<br /> <br /> ==Tomhelp - The Help Program==<br /> This section contains detailed descriptions of many of the functions in TOMLAB. The TOM solvers, originally developed by the Applied Optimization and Modeling (TOM) group, are described together with TOMLAB driver routine and utility functions. Other solvers, like the Stanford Optimization Laboratory (SOL) solvers are not described, but documentation is available for each solver. <br /> *[[Users Guide TomHelp|Tomhelp - The Help Program]]<br /> <br /> ==TOMLAB Solver Reference==<br /> <br /> *[[Users Guide TOMLAB Solver Reference|TOMLAB Solver Reference]]<br /> <br /> ==TOMLAB Utility Functions==<br /> This section describes the utility functions that can be used, for example tomRun and SolverList.<br /> <br /> *[[Users Guide TOMLAB Utility Functions|TOMLAB Utility Functions]]<br /> <br /> ==Approximation of Derivatives==<br /> This section introduces the different options for derivatives, automatic differentiation. <br /> <br /> *[[Users Guide Approximation of Derivatives|Approximation of Derivatives]]<br /> <br /> ==Special Notes and Features==<br /> This section discusses a number of special system features such as partially separable functions and user supplied parameter information for the function computations. <br /> *[[Users Guide Special Notes and Features|Special Notes and Features]]<br /> <br /> ==Appendix A: Prob - the Input Problem Structure==<br /> Appendix A contains tables describing all elements defined in the problem structure. Some subfields are either empty, or filled with information if the particular type of optimization problem is defined. To be able to set different parameter options for the optimization solution, and change problem dependent information, the user should consult the tables in this Appendix. <br /> <br /> *[[Users Guide Appendix A|Appendix A: Prob - the Input Problem Structure]]<br /> <br /> ==Appendix B: Result - the Output Result Structure==<br /> This Appendix contains tables describing all elements defined in the output result structure returned from all solvers and driver routines. <br /> *[[Users Guide Appendix B|Appendix B: Result - the Output Result Structure]]<br /> <br /> ==Appendix C: TomSym - the Modeling Engine==<br /> *[[Users Guide Appendix C|Appendix C: TomSym - the Modeling Engine]]<br /> <br /> ==Appendix D: Global Variables and Recursive Calls==<br /> Appendix D is concerned with the global variables used in TOMLAB and routines for handling important global variables enabling recursive calls of any depth. <br /> *[[Users Guide Appendix D|Appendix D: Global Variables and Recursive Calls]]<br /> <br /> ==Appendix E: External Interfaces==<br /> This Appendix describes the available set of interfaces to other optimization software, such as CUTE, AMPL, and The Mathworks' Optimization Toolbox. <br /> *[[Users Guide Appendix E|Appendix E: External Interfaces]]<br /> <br /> ==Appendix F: Motivation and Background to TOMLAB==<br /> Appendix F gives some motivation for the development of TOMLAB. <br /> *[[Users Guide Appendix F|Appendix F: Motivation and Background to TOMLAB]]<br /> <br /> ==Appendix G: Performance Tests on Linear Programming Solvers==<br /> *[[Users Guide Appendix G|Appendix G: Performance Tests on Linear Programming Solvers]]</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=262 Welcome 2011-07-08T12:11:46Z

<p>WikiSysop: </p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[TOMLAB Manual|The TOMLAB Manual]]<br /> * [[Quickguide]]<br /> * [[Models]]<br /> * [[SAL]]<br /> * [[Installation]]<br /> <br /> ===Product User's Guides===<br /> <br /> * [[MINOS]]<br /> * [[NPSOL]]<br /> <br /> ===Solver User's Guides (only for advanced users)===<br /> <br /> * [[NLPJOB Version 2.0: A Fortran Code for Multicriteria Optimization - User's Guide]]<br /> * [[NLPQLP: A New Fortran Implementation of a Sequential Quadratic Programming Algorithm for Parallel Computing]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=261 Welcome 2011-07-08T12:10:49Z

<p>WikiSysop: /* Manuals */</p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[The TOMLAB Manual]]<br /> * [[Quickguide]]<br /> * [[Models]]<br /> * [[SAL]]<br /> * [[Installation]]<br /> <br /> ===Product User's Guides===<br /> <br /> * [[MINOS]]<br /> * [[NPSOL]]<br /> <br /> ===Solver User's Guides (only for advanced users)===<br /> <br /> * [[NLPJOB Version 2.0: A Fortran Code for Multicriteria Optimization - User's Guide]]<br /> * [[NLPQLP: A New Fortran Implementation of a Sequential Quadratic Programming Algorithm for Parallel Computing]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=TOMLAB_Appendix_B&diff=161 TOMLAB Appendix B 2011-07-02T19:08:51Z

<p>WikiSysop: moved User's Guide Appendix B to Users Guide Appendix B</p> <hr /> <div>=''Result'' - the Output Result Structure=<br /> <br /> The results of the optimization attempts are stored in a structure array named ''Result''. The currently defined fields in the structure are shown in Table 149. The use of structure arrays make advanced result presentation and statistics possible. Results from many runs may be collected in an array of structures, making postprocessing on all results easy.<br /> <br /> When running global optimization, output results are also stored in ''mat''-files, to enable fast restart (warm start) of the solver. It is seldom the case that one knows that the solver actually converged for a particular problem. Therefore one does restarts until the optimum does not change, and one is satisfied with the results. The infor- mation stored in the mat-file ''glbSave.mat ''by the solver ''glbSolve ''is shown in Table 147. The information stored in the mat-file ''glcSave.mat ''by the solver ''glcSolve ''is shown in Table 148. Different information is stored when using glbFast and glcFast, see the solver reference.<br /> <br /> {|<br /> |+Table 147: Information stored in the mat-file ''glbSave.mat ''by the solver ''glbSolve''. Used for automatic restarts.<br /> |-valign="top"<br /> |'''Variable'''<hr />||'''Description'''<hr /><br /> |-valign="top"<br /> |''C''||Matrix with all rectangle centerpoints, in \[0,1\]-space.<br /> |-valign="top"<br /> |''D''||Vector with distances from centerpoint to the vertices. <br /> |-valign="top"<br /> |''DMin''||Row vector of minimum function value for each distance. <br /> |-valign="top"<br /> |''DSort''||Row vector of all different distances, sorted.<br /> |-valign="top"<br /> |''E''||Computed tolerance in rectangle selection.<br /> |-valign="top"<br /> |''F''||Vector with function values.<br /> |-valign="top"<br /> |''L''||Matrix with all rectangle side lengths in each dimension. <br /> |-valign="top"<br /> |''Name''||Name of the problem. Used for security if doing warm start. <br /> |-valign="top"<br /> |''glbfMin''||Best function value found at a feasible point.<br /> |-valign="top"<br /> |''iMin''||The index in D which has lowest function value, i.e. the rectangle which minimizes (''F - glbf M in ''+ ''E'')''./D ''where ''E ''= ''max''(''EpsGlob * abs''(''glbf M in'')'', ''1''E - ''8).<br /> |}<br /> <br /> <br /> {|<br /> |+Table 148: Information stored in the mat-file ''glcSave.mat ''by the solver ''glcSolve''. Used for automatic restarts.<br /> |-valign="top"<br /> |'''Variable'''<hr />||'''Description'''<hr /><br /> |-valign="top"<br /> |''C''||Matrix with all rectangle centerpoints.<br /> |-valign="top"<br /> |''D''||Vector with distances from centerpoint to the vertices.<br /> |-valign="top"<br /> |''F ''||Vector with function values.<br /> |-valign="top"<br /> |''G''||Matrix with constraint values for each point.<br /> |-valign="top"<br /> |''Name''||Name of the problem. Used for security if doing warm start.<br /> |-valign="top"<br /> |''Split''||Split''(''i, j'') is the number of splits along dimension ''i ''of rectangle ''j''.<br /> |-valign="top"<br /> |''T''||T ''(''i'') is the number of times rectangle ''i ''has been trisected. <br /> |-valign="top"<br /> |''fMinEQ''||sum(abs(infeasibilities)) for minimum points, 0 if no equalities. <br /> |-valign="top"<br /> |''fMinIdx''||Indices of the currently best points.<br /> |-valign="top"<br /> |''feasible''||Flag indicating if a feasible point has been found.<br /> |-valign="top"<br /> |''glcf_min''||Best function value found at a feasible point.<br /> |-valign="top"<br /> |''iL''||''iL''(''i, j'') is the lower bound for rectangle ''j ''in integer dimension ''I ''(''i''). <br /> |-valign="top"<br /> |''iU''||''iU ''(''i, j'') is the upper bound for rectangle ''j ''in integer dimension ''I ''(''i''). ''ignoreidx ''Rectangles to be ignored in the rectangle selection procedure.<br /> |-valign="top"<br /> |''s''||''s''(''j'') is the sum of observed rates of change for constraint ''j''.<br /> |-valign="top"<br /> |''s_0''||''s''_0 is used as ''s''(0).<br /> |-valign="top"<br /> |''t''||''t''(''i'') is the total number of splits along dimension ''i''.<br /> |}<br /> <br /> <br /> {|<br /> |+Table 149: Information stored in the optimization result structure ''Result''.<br /> |-valign="top"<br /> |'''Field'''<hr />||'''Description'''<hr /><br /> |-valign="top"<br /> |''Name''||Problem name.<br /> |-valign="top"<br /> |''P''||Problem number.<br /> |-valign="top"<br /> |''probType''||TOMLAB problem type, according to Table 1, page 11.<br /> |-valign="top"<br /> |''Solver''||Solver used. <br /> |-valign="top"<br /> |''SolverAlgorithm''||Solver algorithm used. <br /> |-valign="top"<br /> |''solvType''||TOMLAB solver type.<br /> |-valign="top"<br /> |''ExitFlag''||0 if convergence to local min. Otherwise errors. ''ExitText ''Text string describing the result of the optimization. ''Inform ''Information parameter, type of convergence.<br /> |-valign="top"<br /> |''CPUtime''||CPU time used in seconds.<br /> |-valign="top"<br /> |''REALtime''||Real time elapsed in seconds.<br /> |-valign="top"<br /> |''Iter''||Number of major iterations.<br /> |-valign="top"<br /> |''MinorIter''||Number of minor iterations (for some solvers).<br /> |-valign="top"<br /> |''maxTri''||Maximum rectangle size.<br /> |-valign="top"<br /> |''FuncEv''||Number of function evaluations needed. ''GradEv ''Number of gradient evaluations needed. ''HessEv ''Number of Hessian evaluations needed. ''ConstrEv ''Number of constraint evaluations needed.<br /> |-valign="top"<br /> |''ConJacEv''||Number of constraint Jacobian evaluations needed.<br /> |-valign="top"<br /> |''ConHessEv''||Number of nonlinear constraint Hessian evaluations needed.<br /> |-valign="top"<br /> |''ResEv''||Number of residual evaluations needed (least squares).<br /> |-valign="top"<br /> |''JacEv''||Number of Jacobian evaluations needed (least squares).<br /> |-valign="top"<br /> |''x_k''||Optimal point.<br /> |-valign="top"<br /> |''f_k''||Function value at optimum.<br /> |-valign="top"<br /> |''g_k''||Gradient value at optimum.<br /> |-valign="top"<br /> |''B_k''||Quasi-Newton approximation of the Hessian at optimum.<br /> |-valign="top"<br /> |''H_k''||Hessian value at optimum.<br /> |-valign="top"<br /> |''y_k''||Dual parameters.<br /> |-valign="top"<br /> |''v_k''||Lagrange multipliers for constraints on variables, linear and nonlinear constraints.<br /> |-valign="top"<br /> |''r_k''||Residual vector at optimum.<br /> |-valign="top"<br /> |''J_k''||Jacobian matrix at optimum.<br /> |-valign="top"<br /> |''Ax''||Value of linear constraints at optimum.<br /> |-valign="top"<br /> |''c_k''||Value of nonlinear constraints at optimum.<br /> |-valign="top"<br /> |''cJac''||Constraint Jacobian at optimum.<br /> |-valign="top"<br /> |''x_0''||Starting point.<br /> |-valign="top"<br /> |''f_0''||Function value at start i.e. ''f ''(''x ''0).<br /> |-valign="top"<br /> |''c_0''||Value of nonlinear constraints at start.<br /> |-valign="top"<br /> |''Ax0''||Value of linear constraints at start.<br /> |-valign="top"<br /> |''xState''||State of each variable, described in Table 150.<br /> |-valign="top"<br /> |''bState''||State of each linear constraint, described in Table 151.<br /> |-valign="top"<br /> |''cState''||State of each general constraint, described in Table 152.<br /> |-valign="top"<br /> |''p_dx''||Matrix where each column is a search direction.<br /> |-valign="top"<br /> |''alphaV''||Matrix where row i stores the step lengths tried for the ''i'':th iteration.<br /> |-valign="top"<br /> |''x_min''||Lowest ''x''-values in optimization. Used for plotting.<br /> |-valign="top"<br /> |''x_max''||Highest ''x''-values in optimization. Used for plotting.<br /> |-valign="top"<br /> |''LS''||Structure with statistical information for least squares problems, see Table 153.<br /> |-valign="top"<br /> |''F_X''||F_X is a global matrix with rows: \[iter no f(x)\].<br /> |-valign="top"<br /> |''SepLS''||General result variable with fields ''z ''and ''Jz''. Used when running separable nonlinear least squares problems.<br /> |-valign="top"<br /> |''QP''||Structure with special fields for QP problems. Used for warm starts, see Table 136.<br /> |-valign="top"<br /> |''SOL''||Structure with some of the fields in the ''Prob.SOL ''structure, the ones needed to do a warm start of a SOL solver, see Table 143. The routine ''WarmDefSOL ''moves the relevant fields back to ''Prob.SOL ''for the subse- quent call.<br /> |-valign="top"<br /> |''DUNDEE''||Structure with special result fields from TOMLAB /MINLP solvers.<br /> |-valign="top"<br /> |''plotData''||Structure with plotting parameters.<br /> |-valign="top"<br /> |''Prob''||Problem structure, see Table 134. Please note that certain solvers that do reformulations of the problem, e.g. ''L1Solve'', ''infSolve ''and ''slsSolve'', return the ''Prob ''structure of the ''reformulated ''problem in this field, ''not ''the original one.<br /> |}<br /> <br /> <br /> The field ''xState ''describes the state of each of the variables. In Table 150 the different values are described. The different conditions for linear constraints are defined by the state variable in field ''bState''. In Table 151 the different values are described.<br /> <br /> <br /> {|<br /> |+Table 150: The state variable ''xState ''for the variable.<br /> |-<br /> |'''Value '''<hr />||'''Description'''<hr /><br /> |-<br /> |0||A free variable.<br /> |-<br /> |1||Variable on lower bound.<br /> |-<br /> |2||Variable on upper bound.<br /> |-<br /> |3||Variable is fixed, lower bound is equal to upper bound.<br /> |}<br /> <br /> <br /> {|<br /> |+Table 151: The state variable ''bState ''for each linear constraint.<br /> |-<br /> |'''Value'''<hr />||'''Description'''<hr /><br /> |-<br /> |0||Inactive constraint.<br /> |-<br /> |1||Linear constraint on lower bound.<br /> |-<br /> |2||Linear constraint on upper bound.<br /> |-<br /> |3||Linear equality constraint.<br /> |}<br /> <br /> <br /> {|<br /> |+Table 152: The state variable ''cState ''for each nonlinear constraint.<br /> |-<br /> |'''Value'''<hr />||'''Description'''<hr /><br /> |-<br /> |0||Inactive constraint.<br /> |-<br /> |1||Nonlinear constraint on lower bound.<br /> |-<br /> |2||Nonlinear constraint on upper bound.<br /> |-<br /> |3||Nonlinear equality constraint.<br /> |}<br /> <br /> <br /> {|<br /> |+Table 153: Information stored in the structure ''Result.LS''.<br /> |-<br /> |'''Field'''<hr />||'''Description'''<hr /><br /> |-<br /> |''SSQ''||''rT · rk ''.<br /> |-<br /> |''Covar''||Covariance matrix (inverse of ''k · Jk '').<br /> |-<br /> |''sigma2''||Estimate of squared standard deviation.<br /> |-<br /> |''Corr''||Correlation matrix (normalized covariance matrix).<br /> |-<br /> |''StdDev''||Estimated standard deviation in parameters.<br /> |-<br /> |''x''||The optimal point x k.<br /> |-<br /> |''ConfLim''||95% confidence limit (roughly) assuming normal distribution of errors.<br /> |-<br /> |''CoeffVar''||Coefficients of variation of estimates.<br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=160 Welcome 2011-07-02T19:08:26Z

<p>WikiSysop: /* Manuals */</p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[Installation | Installation Guide]]<br /> * [[Quickguide]]<br /> * [[Users Guide]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=TEX/Favorites&diff=121 TEX/Favorites 2011-06-29T09:56:54Z

<p>WikiSysop: </p> <hr /> <div>{|<br /> |\textstyle \frac{x}{y} || \frac{x}{y} <br /> |-<br /> |\textstyle \sum_x^n || \sum_{x=1}^{n} <br /> |-<br /> |\textstyle \prod_x^n || \prod^{x=1}_{n} <br /> |-<br /> |\textstyle \int_a^b || \int_{a}^{b} f (x)\,dx <br /> |-<br /> |\textstyle \frac{\partial x}{\partial y} || \frac{\partial x}{\partial y} <br /> |-<br /> |\textstyle \sqrt x || \sqrt{x} <br /> |-<br /> |\textstyle \sqrt[3]{x} || \sqrt[3]{x} <br /> |-<br /> |\textstyle f(x) || f(x) <br /> |-<br /> |\lim || \lim_{x\to\infty} <br /> |-<br /> | *** || ***<br /> |-<br /> |\sin || \sin (x) <br /> |-<br /> |\cos || \cos (x) <br /> |-<br /> |\tan || \tan (x) <br /> |-<br /> |\log || \log (x) <br /> |-<br /> |\ln || \ln (x) <br /> |-<br /> | *** || ***<br /> |-<br /> |\le || \le <br /> |-<br /> |\ge || \ge <br /> |-<br /> |\neq || \neq <br /> |-<br /> |\approx || \approx <br /> |-<br /> |\equiv || \equiv <br /> |-<br /> |\propto || \propto <br /> |-<br /> |\infty || \infty <br /> |-<br /> | *** || ***<br /> |-<br /> |\alpha || \alpha <br /> |-<br /> |\beta || \beta <br /> |-<br /> |\gamma || \gamma <br /> |-<br /> |\delta || \delta <br /> |-<br /> |\epsilon || \epsilon <br /> |-<br /> |\zeta || \zeta <br /> |-<br /> |\eta || \eta <br /> |-<br /> |\theta || \theta <br /> |-<br /> |\vartheta || \vartheta <br /> |-<br /> |\kappa || \kappa <br /> |-<br /> |\lambda || \lambda <br /> |-<br /> |\mu || \mu <br /> |-<br /> |\xi || \xi <br /> |-<br /> |\pi || \pi <br /> |-<br /> |\rho || \rho <br /> |-<br /> |\sigma || \sigma <br /> |-<br /> |\tau || \tau <br /> |-<br /> |\phi || \phi<br /> |-<br /> |\varphi || \varphi<br /> |-<br /> |\chi || \chi<br /> |-<br /> |\psi || \psi<br /> |-<br /> |\omega || \omega <br /> |-<br /> | *** || ***<br /> |-<br /> |\Rightarrow || \Rightarrow <br /> |-<br /> |\rightarrow || \rightarrow <br /> |-<br /> |\Leftarrow || \Leftarrow <br /> |-<br /> |\leftarrow || \leftarrow <br /> |-<br /> |\Leftrightarrow || \Leftrightarrow <br /> |-<br /> |\vec{x} || \vec{x} <br /> |-<br /> | *** || ***<br /> |-<br /> |( || \left( <br /> |-<br /> |) || \right) <br /> |-<br /> |[ || \left[ <br /> |-<br /> |] || \right] <br /> |-<br /> |\{ || \left{ <br /> |-<br /> |\} || \right} <br /> |-<br /> |\textstyle {n \choose k} || {n \choose k} <br /> |-<br /> | *** || ***<br /> |-<br /> |\Box || \Box<br /> |-<br /> |\forall || \forall<br /> |-<br /> |\exists || \exists<br /> |-<br /> |\in || \in<br /> |-<br /> |\not\in || \not\in<br /> |-<br /> | *** || ***<br /> |-<br /> |\mbox{Taylor} || f(x) = \sum_{k=0}^{\infty } \frac{ f^{k} (a) }{ k! } (x - a)^k<br /> |-<br /> |\mbox{Euler}^1 || e^{i \varphi } := \cos \varphi + i \sin \varphi <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=TEX/Favorites&diff=120 TEX/Favorites 2011-06-29T09:56:38Z

<p>WikiSysop: </p> <hr /> <div>\textstyle \frac{x}{y} \frac{x}{y}<br /> \textstyle \sum_x^n \sum_{x=1}^{n}<br /> \textstyle \prod_x^n \prod^{x=1}_{n}<br /> \textstyle \int_a^b \int_{a}^{b} f (x)\,dx<br /> \textstyle \frac{\partial x}{\partial y} \frac{\partial x}{\partial y}<br /> \textstyle \sqrt x \sqrt{x}<br /> \textstyle \sqrt[3]{x} \sqrt[3]{x}<br /> \textstyle f(x) f(x)<br /> \lim \lim_{x\to\infty}<br /> *** ***<br /> \sin \sin (x)<br /> \cos \cos (x)<br /> \tan \tan (x)<br /> \log \log (x)<br /> \ln \ln (x)<br /> *** ***<br /> \le \le<br /> \ge \ge<br /> \neq \neq<br /> \approx \approx<br /> \equiv \equiv<br /> \propto \propto<br /> \infty \infty<br /> *** ***<br /> \alpha \alpha<br /> \beta \beta<br /> \gamma \gamma<br /> \delta \delta<br /> \epsilon \epsilon<br /> \zeta \zeta<br /> \eta \eta<br /> \theta \theta<br /> \vartheta \vartheta<br /> \kappa \kappa<br /> \lambda \lambda<br /> \mu \mu<br /> \xi \xi<br /> \pi \pi<br /> \rho \rho<br /> \sigma \sigma<br /> \tau \tau<br /> \phi \phi<br /> \varphi \varphi<br /> \chi \chi<br /> \psi \psi<br /> \omega \omega<br /> *** ***<br /> \Rightarrow \Rightarrow<br /> \rightarrow \rightarrow<br /> \Leftarrow \Leftarrow<br /> \leftarrow \leftarrow<br /> \Leftrightarrow \Leftrightarrow<br /> \vec{x} \vec{x}<br /> *** ***<br /> ( \left(<br /> ) \right)<br /> [ \left[<br /> ] \right]<br /> \{ \left{<br /> \} \right}<br /> \textstyle {n \choose k} {n \choose k}<br /> *** ***<br /> \Box \Box<br /> \forall \forall<br /> \exists \exists<br /> \in \in<br /> \not\in \not\in<br /> *** ***<br /> \mbox{Taylor} f(x) = \sum_{k=0}^{\infty } \frac{ f^{k} (a) }{ k! } (x - a)^k<br /> \mbox{Euler}^1 e^{i \varphi } := \cos \varphi + i \sin \varphi</div>

WikiSysop http://tomwiki.com/index.php?title=MediaWiki:Texbox&diff=119 MediaWiki:Texbox 2011-06-29T09:55:17Z

<p>WikiSysop: Created page with "The TEX Box"</p> <hr /> <div>The TEX Box</div>

WikiSysop http://tomwiki.com/index.php?title=TEX/Favorites&diff=118 TEX/Favorites 2011-06-29T09:54:20Z

<p>WikiSysop: Created page with "{| |\textstyle \frac{x}{y} || \frac{x}{y} |- |\textstyle \sum_x^n || \sum_{x=1}^{n} |- |\textstyle \prod_x^n || \prod^{x=1}_{n} |- |\textstyle \int_a^b || \int_..."</p> <hr /> <div>{|<br /> |\textstyle \frac{x}{y} || \frac{x}{y} <br /> |-<br /> |\textstyle \sum_x^n || \sum_{x=1}^{n} <br /> |-<br /> |\textstyle \prod_x^n || \prod^{x=1}_{n} <br /> |-<br /> |\textstyle \int_a^b || \int_{a}^{b} f (x)\,dx <br /> |-<br /> |\textstyle \frac{\partial x}{\partial y} || \frac{\partial x}{\partial y} <br /> |-<br /> |\textstyle \sqrt x || \sqrt{x} <br /> |-<br /> |\textstyle \sqrt[3]{x} || \sqrt[3]{x} <br /> |-<br /> |\textstyle f(x) || f(x) <br /> |-<br /> |\lim || \lim_{x\to\infty} <br /> |-<br /> | *** || ***<br /> |-<br /> |\sin || \sin (x) <br /> |-<br /> |\cos || \cos (x) <br /> |-<br /> |\tan || \tan (x) <br /> |-<br /> |\log || \log (x) <br /> |-<br /> |\ln || \ln (x) <br /> |-<br /> | *** || ***<br /> |-<br /> |\le || \le <br /> |-<br /> |\ge || \ge <br /> |-<br /> |\neq || \neq <br /> |-<br /> |\approx || \approx <br /> |-<br /> |\equiv || \equiv <br /> |-<br /> |\propto || \propto <br /> |-<br /> |\infty || \infty <br /> |-<br /> | *** || ***<br /> |-<br /> |\alpha || \alpha <br /> |-<br /> |\beta || \beta <br /> |-<br /> |\gamma || \gamma <br /> |-<br /> |\delta || \delta <br /> |-<br /> |\epsilon || \epsilon <br /> |-<br /> |\zeta || \zeta <br /> |-<br /> |\eta || \eta <br /> |-<br /> |\theta || \theta <br /> |-<br /> |\vartheta || \vartheta <br /> |-<br /> |\kappa || \kappa <br /> |-<br /> |\lambda || \lambda <br /> |-<br /> |\mu || \mu <br /> |-<br /> |\xi || \xi <br /> |-<br /> |\pi || \pi <br /> |-<br /> |\rho || \rho <br /> |-<br /> |\sigma || \sigma <br /> |-<br /> |\tau || \tau <br /> |-<br /> |\phi || \phi<br /> |-<br /> |\varphi || \varphi<br /> |-<br /> |\chi || \chi<br /> |-<br /> |\psi || \psi<br /> |-<br /> |\omega || \omega <br /> |-<br /> | *** || ***<br /> |-<br /> |\Rightarrow || \Rightarrow <br /> |-<br /> |\rightarrow || \rightarrow <br /> |-<br /> |\Leftarrow || \Leftarrow <br /> |-<br /> |\leftarrow || \leftarrow <br /> |-<br /> |\Leftrightarrow || \Leftrightarrow <br /> |-<br /> |\vec{x} || \vec{x} <br /> |-<br /> | *** || ***<br /> |-<br /> |( || \left( <br /> |-<br /> |) || \right) <br /> |-<br /> |[ || \left[ <br /> |-<br /> |] || \right] <br /> |-<br /> |\{ || \left{ <br /> |-<br /> |\} || \right} <br /> |-<br /> |\textstyle {n \choose k} || {n \choose k} <br /> |-<br /> | *** || ***<br /> |-<br /> |\Box || \Box<br /> |-<br /> |\forall || \forall<br /> |-<br /> |\exists || \exists<br /> |-<br /> |\in || \in<br /> |-<br /> |\not\in || \not\in<br /> |-<br /> | *** || ***<br /> |-<br /> |\mbox{Taylor} || f(x) = \sum_{k=0}^{\infty } \frac{ f^{k} (a) }{ k! } (x - a)^k<br /> |-<br /> |\mbox{Euler}^1 || e^{i \varphi } := \cos \varphi + i \sin \varphi <br /> |}</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=116 Welcome 2011-06-29T08:12:18Z

<p>WikiSysop: </p> <hr /> <div>Welcome to the TOMLAB Wiki.<br /> <br /> == Manuals ==<br /> <br /> * [[Installation | Installation Guide]]<br /> * [[Quickguide]]<br /> * [[User's Guide]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=106 Welcome 2011-06-25T18:02:22Z

<p>WikiSysop: </p> <hr /> <div>Welcome to the TOMLAB Wiki<br /> <br /> == Manuals ==<br /> <br /> * [[Installation | Installation Guide]]<br /> * [[Quickguide]]<br /> * [[User's Guide]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=70 Welcome 2011-05-29T13:30:54Z

<p>WikiSysop: Reverted edits by WikiSysop (talk) to last revision by Per</p> <hr /> <div>Weclome to the TOMLAB Wiki<br /> <br /> == Manuals ==<br /> <br /> * [[Installation | Installation Guide]]<br /> * [[Quickguide]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop http://tomwiki.com/index.php?title=Welcome&diff=69 Welcome 2011-05-29T13:29:37Z

<p>WikiSysop: /* Manuals */</p> <hr /> <div>Weclome to the TOMLAB Wiki<br /> <br /> == Manuals ==<br /> <br /> * [[Installation | Installation Guide]]<br /> * [[Quickguide]]<br /> * [[Test]]<br /> <br /> == Examples ==<br /> <br /> == Links ==<br /> <br /> * http://tomopt.com/</div>

WikiSysop