TOMLAB Solver Reference
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This page is part of the TOMLAB Manual. See TOMLAB Manual. |
Detailed descriptions of the TOMLAB solvers, driver routines and some utilities are given in the following sections. Also see the M-file help for each solver. All solvers except for the TOMLAB Base Module are described in separate manuals.
For a description of solvers called using the MEX-file interface, see the M-file help, e.g. for the MINOS solver minosTL.m. For more details, see the User's Guide for the particular solver.
clsSolve
Solves dense and sparse nonlinear least squares optimization problems with linear inequality and equality con- straints and simple bounds on the variables.
conSolve
Solve general constrained nonlinear optimization problems.
cutPlane
Solve mixed integer linear programming problems (MIP).
DualSolve
Solve linear programming problems when a dual feasible solution is available.
expSolve
Solve exponential fitting problems for given number of terms p.
glbDirect
Solve box-bounded global optimization problems.
glbSolve
Solve box-bounded global optimization problems.
glcCluster
Solve general constrained mixed-integer global optimization problems using a hybrid algorithm.
glcDirect
Solve global mixed-integer nonlinear programming problems.
glcSolve
Solve general constrained mixed-integer global optimization problems.
infLinSolve
Finds a linearly constrained minimax solution of a function of several variables with the use of any suitable TOMLAB solver. The decision variables may be binary or integer.
infSolve
Find a constrained minimax solution with the use of any suitable TOMLAB solver.
linRatSolve
Finds a linearly constrained solution of a function of the ratio of two linear functions with the use of any suitable TOMLAB solver. Binary and integer variables are not supported.
lpSimplex
Solve general linear programming problems.
L1Solve
Find a constrained L1 solution of a function of several variables with the use of any suitable nonlinear TOMLAB solver.
MilpSolve
Solve mixed integer linear programming problems (MILP).
minlpSolve
Branch & Bound algorithm for Mixed-Integer Nonlinear Programming (MINLP) with convex or nonconvex sub problems using NLP relaxation (Formulated as minlp-IP).
mipSolve
Solve mixed integer linear programming problems (MIP).
multiMin
multiMin solves general constrained mixed-integer global optimization problems. It tries to find all local minima by a multi-start method using a suitable nonlinear programming subsolver.
multiMINLP
Purpose
multiMINLP solves general constrained mixed-integer global nonlinear optimization problems.
It is aimed for problems where the number of integer combinations nMax is huge and relaxation of the integer constraint is possible.
If no integer variables, multiMINLP calls multiMin. If nMax <= min(Prob.optParam.MaxFunc,5000), glcDirect is used. Otherwise, multiMINLP first finds a set M of local minima calling multiMin with no integer restriction on any variable. The best local minimum is selected. glcDirect is called to find the best integer feasible solution fIP in a small area (< +- 2 units) around the best local minimum found.
The other local minima are pruned, if fOpt(i) > fIP, no integer feasible solution could be found close to this local minimum i.
The area close to the remaining candidate local minima are searched one by one by calling glcDirect to find any fIPi < fIP.
multiMINLP solves problems of the form
Failed to parse (unknown function "\multicolumn"): {\displaystyle \begin{array}{cccccc} \min\limits_{x} & f(x) \\ s/t & x_{L} & \leq & x & \leq & x_{U} \\ & b_{L} & \leq & Ax & \leq & b_{U} \\ & c_{L} & \leq & c(x) & \leq & c_{U} \\ & & & \multicolumn{3}{l}{x_{j} \in \MATHSET{N} \ \ \forall j \in $I$} \\ \end{array} }
where Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x,x_{L},x_{U}\in \MATHSET{R}^{n}} , Failed to parse (unknown function "\MATHSET"): {\displaystyle c(x),c_{L},c_{U}\in \MATHSET{R}^{m_{1}}} , Failed to parse (unknown function "\MATHSET"): {\displaystyle A\in \MATHSET{R}^{m_{2}\times n}} and Failed to parse (unknown function "\MATHSET"): {\displaystyle b_{L},b_{U}\in \MATHSET{R}^{m_{2}}} . The variables , the index subset of are restricted to be integers.
Calling Syntax
Result = tomRun('multiMINLP',Prob,...)
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
Prob is a structure, defined as to solve a standard MINLP problem. The Prob structure is fed to the localSolver. See e.g. minlpAssign. | ||
See multiMin and glcDirect for input to the subsolvers e.g. Prob.xInit is used in multiMin (and fCut, RandState, xEQTol). | ||
x_L | Lower bounds for each element in x. If generating random points, -inf elements of x L are set to min(-L,xMin,x U-L) xMin is the minimum of the finite x L values. | |
x_U | Upper bounds for each element in x. If generating random points, inf elements of x U are set to max(L,xMax,x L+L) xMax is the maximum of the finite x U values. | |
L is 100 for nonlinear least squares, otherwise 1000. | ||
b_L | Lower bounds on linear constraints. | |
b_U | Upper bounds on linear constraints. | |
A | The linear constraint matrix. | |
c_L | Lower bounds on nonlinear constraints. | |
c_U | Upper bounds on nonlinear constraints. | |
PriLev | Print Level: | |
0 = Silent | ||
1 = Display 2 summary rows | ||
2 = Display some extra summary rows | ||
5 = Print level 1 in tomRun call | ||
6 = Print level 2 in tomRun call | ||
7 = Print level 3 in tomRun call | ||
xInit | Used in multiMin. See help for multiMin. | |
GO.localSolver | The local solver used to run all local optimizations. Default is the license dependent output of GetSolver('con',1,0). | |
optParam | Structure in Prob, Prob.optParam. Defines optimization parameters. Fields used: | |
MaxFunc | Max number of function evaluations in each subproblem | |
fGoal | Goal for function value f(x), if empty not used. If fGoal is reached, no further local optimizations are done | |
eps_f | Relative accuracy for function value, fTol == eps_f. Stop if abs(f-fGoal) <= abs(fGoal) \* fTol , if fGoal = 0. Stop if abs(f-fGoal) <= fTol , if fGoal ==0. Default 1e-8. | |
bTol | Linear constraint feasibility tolerance. Default 1e-6 | |
cTol | Nonlinear constraint feasibility tolerance. Default 1e-6 | |
MIP | Structure in Prob, Prob.MIP. Defines integer optimization parameters. Fields used: | |
IntVars | If empty, all variables are assumed non-integer. If islogical(IntVars) (=all el- ements are 0/1), then 1 = integer variable, 0 = continuous variable. If any element >1, IntVars is the indices for integer variables. | |
nMax | Number of integer combinations possible, if empty multiMINLP computes nMax. | |
Rfac | Reduction factor for real variables in MINLP subproblem close to local multiMINLP minimum. Bounds set to x_L = max(x_L,x-Rfac\*(x_U-x_L)) and x_U= min(x_U,x+Rfac\*(x_U-x_L)). Default 0.25. |
Description of Outputs
Result | Structure with result from optimization. The following fields are changed: | |
Result structure from the last good optimization step giving the best f(x) value, the possible global MINLP minimum. | ||
The following fields in Result are changed by multiMINLP before return: | ||
ExitFlag | = 0 normal output, of if fGoal set and found. | |
= 1 A solution reaching the user defined fGoal was not found. | ||
= 2 Unbounded problem. | ||
=4 Infeasible problem. | ||
The Solver, SolverAlgorithm and ExitText fields are also reset. | ||
A special field in Result is also returned, Result.multiMINLP: | ||
xOpt | Prob.N x k matrix with k distinct local optima, the test being norm(x k- xOpt(:,i)) <= xEqTol\*max(1,norm(x k)) that if fulfilled assumes x_k to be to close to xOpt(:,i). | |
fOpt | The k function values in the local optima xOpt(:,i),i=1,...,k. | |
Inform | The Inform value returned by the local solver when finding each of the local optima xOpt(:,i); i=1,...,k. The Inform value can be used to judge the validity of the local minimum reported. | |
localTry | Total number of local searches. | |
Iter | Total number of iterations. | |
FuncEv | Total number of function evaluations. | |
GradEv | Total number of gradient evaluations. | |
HessEv | Total number of Hessian evaluations. | |
ConstrEv | Total number of constraint function evaluations. | |
ExitText | Text string giving Inform information. |
nlpSolve
Purpose
Solve general constrained nonlinear optimization problems.
nlpSolve solves problems of the form
where Failed to parse (unknown function "\MATHSET"): {\displaystyle x,x_{L},x_{U}\in \MATHSET{R}^{n}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle c(x),c_{L},c_{U}\in \MATHSET{R} ^{m_{1}}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle A\in \MATHSET{R}^{m_{2}\times n}}
and Failed to parse (unknown function "\MATHSET"): {\displaystyle b_{L},b_{U}\in \MATHSET{R}^{m_{2}}}
.
Calling Syntax
Result = nlpSolve(Prob, varargin)
Result = tomRun('nlpSolve', Prob);
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
A | Constraint matrix for linear constraints. | |
b_L | Lower bounds on the linear constraints. | |
b_U | Upper bounds on the linear constraints. | |
c_L | Lower bounds on the general constraints. | |
c_U | Upper bounds on the general constraints. | |
x_L | Lower bounds on the variables. | |
x_U | Upper bounds on the variables. | |
x_0 | Starting point. | |
PriLevOpt | Print level: 0 Silent, 1 Final result, 2 Each iteration, short, 3 Each iteration, more info, 4 Matrix update information. | |
FUNCS.f | Name of m-file computing the objective function f (x). | |
FUNCS.g | Name of m-file computing the gradient vector g(x). | |
FUNCS.H | Name of m-file computing the Hessian matrix H (x). | |
FUNCS.c | Name of m-file computing the vector of constraint functions c(x). | |
FUNCS.dc | Name of m-file computing the matrix of constraint normals ?c(x)/dx. | |
FUNCS.d2c | Name of m-file computing the second derivatives of the constraints, weighted by an input Lagrange vector | |
NumDiff | How to obtain derivatives (gradient, Hessian). | |
ConsDiff | How to obtain the constraint derivative matrix. | |
SolverQP | Name of the solver used for QP subproblems. If empty, the default solver is used. See GetSolver.m and tomSolve.m. | |
SolverFP | Name of the solver used for FP subproblems. If empty, the default solver is used. See GetSolver.m and tomSolve.m. | |
optParam | Structure with special fields for optimization parameters, see Table 141. | |
Fields used are: eps_g, eps_x, MaxIter, wait, size_x, method, IterPrint, xTol, bTol, cTol, and QN InitMatrix. | ||
varargin | Other parameters directly sent to low level routines. |
Description of Outputs
Result | Structure with result from optimization. The following fields are changed: | |
x_k | Optimal point. | |
f_k | Function value at optimum. | |
g_k | Gradient value at optimum. | |
c_k | Value of constraints at optimum. | |
H_k | Hessian value at optimum. | |
v_k | Lagrange multipliers. | |
x_0 | Starting point. | |
f_0 | Function value at start. | |
cJac | Constraint Jacobian at optimum. | |
xState | State of each variable, described in Table 150. | |
bState | State of each linear constraint, described in Table 151. | |
cState | State of each general constraint. | |
Inform | Type of convergence. | |
ExitFlag | Flag giving exit status. | |
ExitText | 0: Convergence. Small step. Constraints fulfilled. | |
1: Infeasible problem? | ||
2: Maximal number of iterations reached. | ||
3: No progress in either function value or constraint reduction. | ||
Inform | 1: Iteration points are close. | |
2: Small search direction | ||
3: Function value below given estimate. Restart with lower fLow if minimum not reached. | ||
4: Projected gradient small. | ||
10: Karush-Kuhn-Tucker conditions fulfilled. | ||
Iter | Number of iterations. | |
Solver | Solver used. | |
SolverAlgorithm | Solver algorithm used. | |
Prob | Problem structure used. |
Description
The routine nlpSolve implements the Filter SQP by Roger Fletcher and Sven Leyffer presented in the paper \[21\].
M-files Used
tomSolve.m, ProbCheck.m, iniSolve.m, endSolve.m
See Also
conSolve, sTrustr
pdcoTL
Purpose
pdcoTL solves linearly constrained convex nonlinear optimization problems of the kind
where is a convex nonlinear function, Failed to parse (unknown function "\RR"): {\displaystyle x,x_L,x_U \in \RR^n}
, Failed to parse (unknown function "\RR"): {\displaystyle A\in \RR^{m \times n}}
and Failed to parse (unknown function "\RR"): {\displaystyle b_L, b_U \in \RR^m}
.
Calling Syntax
Result=tomRun('pdco',Prob,...);
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
x_0 | Initial x vector, used if non-empty. | |
A | The linear constraint matrix. | |
b_L,b_U | Lower and upper bounds for the linear constraints. | |
PriLevOpt | Print level in pdsco solver. If > 0: prints summary information. | |
SOL | Structure with SOL special parameters: | |
pdco | Options structure with fields as defined by pdcoSet. | |
d1 | Primal regularization vector. Must be a positive vector (length n) or scalar, in which case D1 = diag(d1) is used. Default: 10-4 . | |
d2 | Dual regularization vector. Must be a positive vector (length m) or a scalar value, in which case D2 = diag(d2) is used. Default: 10-4 . | |
y0 | Initial dual parameters for linear constraints (default 0) | |
z0 | Initial dual parameters for simple bounds (default 1/N )
xsize,zsize are used to scale (x, y, z). Good estimates should improve the performance of the barrier method. | |
xsize | Estimate of the biggest x at the solution. (default 1/N ) | |
zsize | Estimate of the biggest z at the solution. (default 1/N ) | |
optParam | Structure with optimization parameters. The following fields are used: | |
MaxIter | Maximum number of iterations. (Prob.SOL.pdco.MaxIter). | |
MinorIter | Maximum number of iterations in LSQR (Prob.SOL.pdco.LSQRMaxIter). | |
eps_x | Accuracy for satisfying x1 . * z1 = 0, x2 .z1 = 0, where z = z1 - z2 and z1 , z2 > 0.(Prob.SOL.pdco.OptTol) | |
bTol | Accuracy for satisfying Ax + D2 r = b, AT y + z = ?f (x) and x - x1 = bL , x +x2 = bU , where x1 , x2 > 0 (Prob.SOL.pdco.FeaTol) | |
wait | 0 - solve the problem with default internal parameters; 1 - pause: allows interactive resetting of parameters. (Prob.SOL.pdco.wait) |
Description of Outputs
Result | Structure with result from optimization. The following fields are set by pdcoTL | |
x_k | Solution vector | |
f_k | Function value at optimum | |
g_k | Gradient of the function at the solution | |
H_k | Hessian of the function at the solution, diagonal only | |
x_0 | Initial solution vector | |
f_0 | Function value at start, x = x_0 | |
xState | State of variables. Free == 0; On lower == 1; On upper == 2; Fixed == 3; | |
bState | State of linear constraints. Free == 0; Lower == 1; Upper == 2; Equality == 3; | |
v_k | Lagrangian multipliers (orignal bounds + constraints ) | |
y_k | Lagrangian multipliers (for bounds + dual solution vector) The full \[z; y\] vec- tor as returned from pdco, including slacks and extra linear constraints after rewriting constraints: -inf < b L < A * x < b U < inf ; non-inf lower AND upper bounds | |
ExitFlag | Tomlab Exit status from pdco MEX | |
Inform | pdcoinformation parameter: 0 = Solution found; | |
0 | Solution found | |
1 | Too many iterations | |
2 | Linesearch failed too often | |
Iter | Number of iterations | |
FuncEv | Number of function evaluations | |
GradEv | Number of gradient evaluations | |
HessEv | Number of Hessian evaluations | |
Solver | Name of the solver ('pdco') | |
SolverAlgorithm | Description of the solver |
Description
pdco implements an primal-dual barrier method developed at Stanford Systems Optimization Laboratory (SOL).
The problem (19) is first reformulated into SOL PDCO form:
Failed to parse (unknown function "\multicolumn"): {\displaystyle \begin{array}{cccccc} \min\limits_x & f(x) \\ \mathrm{s/t} & x_L & \leq & x & \leq & x_U \\ {} & & & Ax & = & b \\ {} & \multicolumn{5}{l}{r \mathrm{\ unconstrained}} \\ \end{array} }
The problem actually solved by pdco is
Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{array}{lllcll} \min\limits_{x,r} & \multicolumn{5}{l}{\phi(x) + \frac{1}{2}\|D_1 x\|^2 + \frac{1}{2}\|r\|^2 } \\ \\ \mathrm{s/t} & x_L & \leq & x & \leq & x_U \\ {} & Ax & + & D_{2}r & = & b \\ \end{array} }
where and are positive-definite diagonal matrices defined from , given in Prob.SOL.d1 and Prob.SOL.d2.
In particular, indicates the accuracy required for satisfying each row of . See pdco for a detailed discussion of and . Note that in pdco, the objective is denoted , and .
Examples
Problem 14 and 15 in tomlab/testprob/con prob.m.m are good examples of the use of pdcoTL.
M-files Used
pdcoSet.m, pdco.m, Tlsqrmat.m
See Also
pdscoTL.m
pdscoTL
Purpose
pdscoTL solves linearly constrained convex nonlinear optimization problems of the kind
where is a convex separable nonlinear function, Failed to parse (unknown function "\RR"): {\displaystyle x,x_L,x_U \in \RR^n} , Failed to parse (unknown function "\RR"): {\displaystyle A\in \RR^{m \times n}} and Failed to parse (unknown function "\RR"): {\displaystyle b_L, b_U \in\RR^m} .
Calling Syntax
Result=tomRun('pdsco',Prob,...);
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
x_0 | Initial x vector, used if non-empty. | |
A | The linear constraints coefficient matrix. | |
b_L,b_U | Lower and upper bounds for the linear constraints. | |
HessPattern | Non-zero pattern for the objective function. Only the diagonal is needed. Default if empty is the unit matrix. | |
PriLevOpt | Print level in pdsco solver. If > 0: prints summary information. | |
SOL | Structure with SOL special parameters: | |
pdco | Options structure with fields as defined by pdscoSet. | |
gamma | Primal regularization parameter. | |
delta | Dual regularization parameter. | |
y0 | Initial dual parameters for linear constraints (default 0) | |
z0 | Initial dual parameters for simple bounds (default 1/N ) | |
xsize,zsize are used to scale (x, y, z). Good estimates should improve the performance of the barrier method. | ||
xsize | Estimate of the biggest x at the solution. (default 1/N ) | |
zsize | Estimate of the biggest z at the solution. (default 1/N ) | |
optParam | Structure with optimization parameters. The following fields are used: | |
MaxIter | Maximum number of iterations. (Prob.SOL.pdco.MaxIter). | |
MinorIter | Maximum number of iterations in LSQR (Prob.SOL.pdco.LSQRMaxIter). | |
eps_x | Accuracy for satisfying x. * z = 0 | |
bTol | Accuracy for satisfying Ax + r = b, AT y + z = ?f (x) and x - x1 = bL , x + x2 =bU, where x1 , x2 > 0. (Prob.SOL.pdco.FeaTol) | |
wait | 0 - solve the problem with default internal parameters; 1 - pause: allows interactive resetting of parameters. (Prob.SOL.pdco.wait) |
Description of Outputs
Result | Structure with result from optimization. The following fields are set by pdscoTL: | |
x_k | Solution vector | |
f_k | Function value at optimum | |
g_k | Gradient of the function at the solution | |
H_k | Hessian of the function at the solution, diagonal only | |
x_0 | Initial solution vector | |
f_0 | Function value at start, x = x_0 | |
xState | State of variables. Free == 0; On lower == 1; On upper == 2; Fixed == 3; | |
bState | State of linear constraints. Free == 0; Lower == 1; Upper == 2; Equality == 3; | |
v_k | Lagrangian multipliers (orignal bounds + constraints ) | |
y k | Lagrangian multipliers (for bounds + dual solution vector) The full \[z; y\] vec- tor as returned from pdsco, including slacks and extra linear constraints after rewriting constraints: -inf < b L < A * x < b U < inf ; non-inf lower AND upper bounds | |
ExitFlag | Tomlab Exit status from pdsco MEX | |
Inform | pdsco information parameter: 0 = Solution found; | |
0 | Solution found | |
1 | Too many iterations | |
2 | Linesearch failed too often | |
Iter | Number of iterations | |
FuncEv | Number of function evaluations | |
GradEv | Number of gradient evaluations | |
HessEv | Number of Hessian evaluations | |
Solver | Name of the solver ('pdsco') | |
SolverAlgorithm | Description of the solver |
Description
pdsco implements an primal-dual barrier method developed at Stanford Systems Optimization Laboratory (SOL). The problem (20) is first reformulated into SOL PDSCO form:
The problem actually solved by pdsco is
Failed to parse (unknown function "\multicolumn"): {\displaystyle \begin{array}{lllcll} \min\limits_{x,r} & \multicolumn{5}{l}{f(x) + \frac{1}{2}\|\gamma x\|^2 + \frac{1}{2}\|r / \delta \|^2 } \\ \\ \mathrm{s/t} & & & x & \geq & 0 \\ {} & Ax & + & r & = & b \\ {} & \multicolumn{5}{l}{r \mathrm{\ unconstrained}} \\ \end{array} }
where is the primal regularization parameter, typically small but 0 is allowed. Furthermore, is the dual regularization parameter, typically small or 1; must be strictly greater than zero.
With positive the primal-dual solution is bounded and unique.
See pdsco for a detailed discussion of </math>\gamma</math> and . Note that in pdsco, the objective is denoted , and .
Examples
Problem 14 and 15 in tomlab/testprob/con prob.m are good examples of the use of pdscoTL.
M-files Used
pdscoSet.m, pdsco.m, Tlsqrmat.m
See Also
pdcoTL.m
qpSolve
Purpose
Solve general quadratic programming problems.
qpSolve solves problems of the form
where Failed to parse (unknown function "\MATHSET"): {\displaystyle x,x_{L},x_{U}\in \MATHSET{R} ^{n}}
, Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F\in \MATHSET{R}^{n\times n}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle c \in \MATHSET{R}^{n}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle A\in \MATHSET{R}^{m\times n}}
and Failed to parse (unknown function "\MATHSET"): {\displaystyle b_{L},b_{U}\in \MATHSET{R}^{m}}
.
Calling Syntax
Result = qpSolve(Prob) or
Result = tomRun('qpSolve', Prob, 1);
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
QP.F | Constant matrix, the Hessian. | |
QP.c | Constant vector. | |
A | Constraint matrix for linear constraints. | |
b_L | Lower bounds on the linear constraints. | |
b_U | Upper bounds on the linear constraints. | |
x_L | Lower bounds on the variables. | |
x_U | Upper bounds on the variables. | |
x_0 | Starting point. | |
optParam | Structure with special fields for optimization parameters, see Table 141.Fields used are: eps f, eps Rank, MaxIter, wait, bTol and PriLev. |
Description of Outputs
Result | Structure with result from optimization. The following fields are changed: | |
x_k | Optimal point. | |
f_k | Function value at optimum. | |
g_k | Gradient value at optimum. | |
H_k | Hessian value at optimum. | |
v_k | Lagrange multipliers. | |
x_0 | Starting point. | |
f_0 | Function value at start. | |
xState | State of each variable, described in Table 150 . | |
Iter | Number of iterations. | |
ExitFlag | 0: OK, see Inform for type of convergence. | |
2: Can not find feasible starting point x_0. | ||
3: Rank problems. Can not find any solution point. | ||
4: Unbounded solution. | ||
10: Errors in input parameters. | ||
Inform | If ExitF lag > 0, I nf orm = ExitF lag, otherwise I nf orm show type of convergence: | |
0: Unconstrained solution. | ||
1: ? = 0. | ||
2: ? = 0. No second order Lagrange mult. estimate available. | ||
3: ? and 2nd order Lagr. mult. positive, problem is not negative definite. | ||
4: Negative definite problem. 2nd order Lagr. mult. positive, but releasing variables leads to the same working set. | ||
Solver | Solver used. | |
SolverAlgorithm | Solver algorithm used. | |
Prob | Problem structure used. |
Description
Implements an active set strategy for Quadratic Programming. For negative definite problems it computes eigen- values and is using directions of negative curvature to proceed. To find an initial feasible point the Phase 1 LP problem is solved calling lpSimplex. The routine is the standard QP solver used by nlpSolve, sTrustr and conSolve.
M-files Used
ResultDef.m, lpSimplex.m, tomSolve.m, iniSolve.m, endSolve.m
See Also
qpBiggs, qpe, qplm, nlpSolve, sTrustr and conSolve
slsSolve
Purpose
Find a Sparse Least Squares (sls) solution to a constrained least squares problem with the use of any suitable TOMLAB NLP solver.
slsSolve solves problems of the type:
where Failed to parse (unknown function "\Rdim"): {\displaystyle x,x_L,x_U \in \Rdim{n}}
, Failed to parse (unknown function "\Rdim"): {\displaystyle r(x) \in \Rdim{m}}
, Failed to parse (unknown function "\Rdim"): {\displaystyle A \in\Rdim{m_1,n}}
, Failed to parse (unknown function "\Rdim"): {\displaystyle b_L,b_U \in \Rdim{m_1}}
and Failed to parse (unknown function "\Rdim"): {\displaystyle c(x),c_L,c_U \in\Rdim{m_2}}
.
The use of slsSolve is mainly for large, sparse problems, where the structure in the Jacobians of the residuals and the nonlinear constraints are utilized by a sparse NLP solver, e.g. SNOPT.
Calling Syntax
Result=slsSolve(Prob,PriLev)
Description of Inputs
Prob | Problem description structure. Should be created in the cls format, preferably by callingProb=clsAssign(...) if using the TQ format. | |
slsSolve uses two special fields in Prob: | ||
SolverL2 | Text string with name of the NLP solver used for solving the reformulated problem. Valid choices are conSolve, nlpSolve, sTrustr, clsSolve. Suitable SOL solvers, if available: minos, snopt, npopt. | |
L2Type | Set to 1 for standard constrained formulation. Currently this is the only allowed choice. | |
All other fields should be set as expected by the nonlinear solver selected. In particular: | ||
A | Linear constraint matrix. | |
b_L | Lower bounds on the linear constraints. | |
b_U | Upper bounds on the linear constraints. | |
c_L | Upper bounds on the nonlinear constraints. | |
c_U | Lower bounds on the nonlinear constraints. | |
x_L | Lower bounds on the variables. | |
x_U | Upper bounds on the variables. | |
x_0 | Starting point. | |
ConsPattern | The nonzero pattern of the constraint Jacobian. | |
JacPattern | The nonzero pattern of the residual Jacobian. | |
Note that Prob.LS.y must be of correct length if JacPattern is empty (but ConsPattern is not). slsSolve will create the new Prob.ConsPattern to be used by the nonlinear solver using the information in the supplied ConsPattern and JacPattern. | ||
PriLev | Print level in slsSolve. Default value is 2. | |
0 | Silent except for error messages. | |
> 1 | Print summary information about problem transformation. slsSolve calls Print- Result(Result,PriLev). | |
2 | Standard output in PrintResult. |
Description of Outputs
Result | Structure with results from optimization. The contents of Result depend on which nonlinear solver was used to solved | |
slsSolve transforms the following fields of Result back to the format of the original problem: | ||
x_k | Optimal point. | |
r_k | Residual at optimum. | |
J_k | Jacobian of residuals at optimum. | |
c_k | Nonlinear constraint vector at optimum. | |
v_k | Lagrange multipliers. | |
g_k | The gradient vector is calculated as J kT · r k. | |
cJac | Jacobian of nonlinear constraints at optimum. | |
x_0 | Starting point. | |
xState | State of variables at optimum. | |
cState | State of constraints at optimum. | |
Result.Prob | The problem structure defining the reformulated problem. |
Description
The constrained least squares problem is solved in slsSolve by rewriting the problem as a general constrained optimization problem. A set of m (the number of residuals) extra variables are added at the end of the vector of unknowns. The reformulated problem
Failed to parse (SVG with PNG fallback (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{array}{cccccc} \min \limits_x & \multicolumn{5}{l}{\frac{1}{2} z^T z} \\ \mbox{subject to} & x_L & \leq & (x_1,x_2,\ldots,x_n) & \leq & x_U \\ {} & b_L & \leq & Ax & \leq & b_U \\ {} & c_L & \leq & c(x) & \leq & c_U \\ {} & 0 & \leq & r(x) - z & \leq & 0 \\ \end{array} }
is then solved by the solver given by Prob.SolverL2.
Examples
slsDemo.m
M-files Used
iniSolve.m, GetSolver.m
sTrustr
Purpose
Solve optimization problems constrained by a convex feasible region.
sTrustr solves problems of the form
where Failed to parse (unknown function "\MATHSET"): {\displaystyle x,x_{L},x_{U}\in \MATHSET{R}^{n}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle c(x),c_{L},c_{U}\in \MATHSET{R}^{m_{1}}}
, Failed to parse (unknown function "\MATHSET"): {\displaystyle A\in \MATHSET{R}^{m_{2}\times n}}
and Failed to parse (unknown function "\MATHSET"): {\displaystyle b_{L},b_{U}\in \MATHSET{R}^{m_{2}}}
.
Calling Syntax
Result = sTrustr(Prob, varargin)
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
A | Constraint matrix for linear constraints. | |
b_L | Lower bounds on the linear constraints. | |
b_U | Upper bounds on the linear constraints. | |
c_L | Lower bounds on the general constraints. | |
c_U | Upper bounds on the general constraints. | |
x_L | Lower bounds on the variables. | |
x_U | Upper bounds on the variables. | |
x_0 | Starting point. | |
FUNCS.f | Name of m-file computing the objective function f (x). | |
FUNCS.g | Name of m-file computing the gradient vector g(x). | |
FUNCS.H | Name of m-file computing the Hessian matrix H (x). | |
FUNCS.c | Name of m-file computing the vector of constraint functions c(x). | |
FUNCS.dc | Name of m-file computing the matrix of constraint normals ?c(x)/dx. | |
optParam | Structure with special fields for optimization parameters, see Table 141. | |
Fields used are: eps f, eps g, eps c, eps x, eps Rank, MaxIter, wait, size x, size f, xTol, LowIts, PriLev, method and QN InitMatrix. | ||
PartSep | Structure with special fields for partially separable functions, see Table 142. | |
varargin | Other parameters directly sent to low level routines. |
Description of Outputs
Result | Structure with result from optimization. The following fields are changed: | |
x_k | Optimal point. | |
f_k | Function value at optimum. | |
g_k | Gradient value at optimum. | |
c_k | Value of constraints at optimum. | |
H_k | Hessian value at optimum. | |
v_k | Lagrange multipliers. | |
x_0 | Starting point. | |
f_0 | Function value at start. | |
cJac | Constraint Jacobian at optimum. | |
xState | State of each variable, described in Table 150. | |
Iter | Number of iterations. | |
ExitFlag | Flag giving exit status. | |
Inform | Binary code telling type of convergence: | |
1: Iteration points are close. | ||
2: Projected gradient small. | ||
3: Iteration points are close and projected gradient small. | ||
4: Relative function value reduction low for LowIts iterations. | ||
5: Iteration points are close and relative function value reduction low for LowIts iterations. | ||
6: Projected gradient small and relative function value reduction low for LowIts iterations. | ||
7: Iteration points are close, projected gradient small and relative function value reduction low for LowIts iterations. | ||
8: Too small trust region. | ||
9: Trust region small. Iteration points close. | ||
10: Trust region and projected gradient small. | ||
11: Trust region and projected gradient small, iterations close. | ||
12: Trust region small, Relative f(x) reduction low. | ||
13: Trust region small, Relative f(x) reduction low. Iteration points are close. | ||
14: Trust region small, Relative f(x) reduction low. Projected gradient small. | ||
15: Trust region small, Relative f(x) reduction low. Iteration points close, Projected gradient small. | ||
101: Maximum number of iterations reached. | ||
102: Function value below given estimate. | ||
103: Convergence to saddle point (eigenvalues computed). | ||
Solver | Solver used. | |
SolverAlgorithm | Solver algorithm used. | |
Prob | Problem structure used. |
Description
The routine sTrustr is a solver for general constrained optimization, which uses a structural trust region algorithm combined with an initial trust region radius algorithm (itrr). The feasible region defined by the constraints must be convex. The code is based on the algorithms in \[13\] and \[67\]. BFGS or DFP is used for the Quasi-Newton update, if the analytical Hessian is not used. sTrustr calls internal routine itrr.
M-files Used
qpSolve.m, tomSolve.m, iniSolve.m, endSolve.m
See Also
conSolve, nlpSolve, clsSolve
Tfmin
Purpose
Minimize function of one variable. Find miniumum x in [x_L, x_U] for function Func within tolerance xTol. Solves using Brents minimization algorithm. Reference: "Computer Methods for Mathematical Computations", Forsythe, Malcolm, and Moler, Prentice-Hall, 1976.
Calling Syntax
[x, nFunc] = Tfmin(Func, x_L, x_U, xTol, Prob)
Description of Inputs
Variable | Description |
Func | Function of x to be minimized. Func must be defined as: |
f = Func(x) if no 5th argument Prob is given or | |
f = Func(x, Prob) if 5th argument Prob is given. | |
x_L | Lower bound on x. |
x_U | Upper bound on x. |
xTol | Tolerance on accuracy of minimum. |
Prob | Structure (or any Matlab variable) sent to Func. If many parameters are to be sent to Func set them in Prob as a structure. Example for parameters a and b: |
Prob.user.a = a; Prob.user.b = b; | |
[x, nFunc] = Tfmin('myFunc',0,1,1E-5,Prob); In myFunc: | |
function f = myFunc(x, Prob) | |
a = Prob.user.a; | |
b = Prob.user.b; | |
f = "matlab expression dependent of x, a and b"; |
Description of Outputs
Variable | Description |
x | Solution. |
nFunc | Number of calls to Func. |
Tfzero
Purpose
Tfzero, TOMLAB fzero routine.
Tfzero, TOMLAB fzero routine.\\ \\Find a zero for in the interval . Tfzero searches for a zero of a function between the given scalar values and until the width of the interval (xLow, xUpp) has collapsed to within a tolerance specified by the stopping criterion, . The method used is an efficient combination of bisection and the secant rule and is due to T. J. Dekker.
Calling Syntax
[xLow, xUpp, ExitFlag] = Tfzero(x L, x U, Prob, x 0, RelErr, AbsErr)
Description of Inputs
Variable | Description |
x_L | Lower limit on the zero x to f(x). |
x_U | Upper limit on the zero x to f(x). |
Prob | Structure, sent to Matlab routine ZeroFunc. The function name should be set in Prob.FUNCS.f0. Only the function will be used, not the gradient. |
x_0 | An initial guess on the zero to f(x). If empty, x 0 is set as the middle point in [x_L, x_U]. |
RelErr | Relative error tolerance, default 1E-7. |
AbsErr | Absolute error tolerance, default 1E-14. |
Description of Outputs
Variable | Description |
xLow | Lower limit on the zero x to f(x). |
xUpp | Upper limit on the zero x to f(x). |
ExitFlag | Status flag 1,2,3,4,5. |
1: xLow is within the requested tolerance of a zero. The interval (xLow, xUpp) collapsed to the requested tolerance, the function changes sign in (xLow, xUpp), and f(x) decreased in magnitude as (xLow, xUpp) collapsed. | |
2: f(xLow) = 0. However, the interval (xLow, xUpp) may not have collapsed to the requested tolerance. | |
3: xLow may be near a singular point of f(x). The interval (xLow, xUpp) collapsed to the requested tolerance and the function changes sign in (xLow, xUpp), but f(x) increased in magnitude as (xLow, xUpp) collapsed, i.e. abs(f (xLow)) > max(abs(f (xLow - I N )), abs(f (xU pp - I N ))). | |
4: No change in sign of f(x) was found although the interval (xLow, xUpp) collapsed to the requested tolerance. The user must examine this case and decide whether xLow is near a local minimum of f(x), or xLow is near a zero of even multiplicity, or neither of these. | |
5: Too many (> 500) function evaluations used. |
ucSolve
Purpose
Solve unconstrained nonlinear optimization problems with simple bounds on the variables.
ucSolve solves problems of the form
where Failed to parse (unknown function "\MATHSET"): {\displaystyle x,x_{L},x_{U}\in \MATHSET{R} ^{n}}
.
Calling Syntax
Result = ucSolve(Prob, varargin)
Description of Inputs
Prob | Problem description structure. The following fields are used: | |
x_L | Lower bounds on the variables. | |
x_U | Upper bounds on the variables. | |
x_0 | Starting point. | |
FUNCS.f | Name of m-file computing the objective function f (x). | |
FUNCS.g | Name of m-file computing the gradient vector g(x). | |
FUNCS.H | Name of m-file computing the Hessian matrix H (x). | |
f_Low | Lower bound on function value. | |
Solver.Alg | Solver algorithm to be run: | |
0: Gives default, either Newton or BFGS. | ||
1: Newton with subspace minimization, using SVD. | ||
2: Safeguarded BFGS with inverse Hessian updates (standard). | ||
3: Safeguarded BFGS with Hessian updates. | ||
4: Safeguarded DFP with inverse Hessian updates. | ||
5: Safeguarded DFP with Hessian updates. | ||
6: Fletcher-Reeves CG. | ||
7: Polak-Ribiere CG. | ||
8: Fletcher conjugate descent CG-method. | ||
Solver.Method | Method used to solve equation system: | |
0: SVD (default). | ||
1: LU-decomposition. | ||
2: LU-decomposition with pivoting. | ||
3: Matlab built in QR. | ||
4: Matlab inversion. | ||
5: Explicit inverse. | ||
Solver.Method | Restart or not for C-G method: | |
0: Use restart in CG-method each n:th step. | ||
1: Use restart in CG-method each n:th step. | ||
LineParam | Structure with line search parameters, see routine LineSearch and Table 140. | |
optParam | Structure with special fields for optimization parameters, see Table 141. | |
Fields used are: eps absf, eps f, eps g, eps x, eps Rank, MaxIter, wait, size x, xTol, size f, LineSearch, LineAlg, xTol, IterPrint and QN InitMatrix. | ||
PriLevOpt | Print level. | |
varargin | Other parameters directly sent to low level routines. |
Description of Outputs
Result | Structure with result from optimization. The following fields are changed: | |
x_k | Optimal point. | |
f_k | Function value at optimum. | |
g_k | Gradient value at optimum. | |
H_k | Hessian value at optimum. | |
B_k | Quasi-Newton approximation of the Hessian at optimum. | |
v_k | Lagrange multipliers. | |
x_0 | Starting point. | |
f_0 | Function value at start. | |
xState | State of each variable, described in Table 150. | |
Iter | Number of iterations. | |
ExitFlag | 0 if convergence to local min. Otherwise errors. | |
Inform | Binary code telling type of convergence: | |
1: Iteration points are close. | ||
2: Projected gradient small. | ||
4: Relative function value reduction low for LowIts iterations. | ||
101: Maximum number of iterations reached. | ||
102: Function value below given estimate. | ||
104: Convergence to a saddle point. | ||
Solver | Solver used. | |
SolverAlgorithm | Solver algorithm used. | |
Prob | Problem structure used. |
Description
The solver ucSolve includes several of the most popular search step methods for unconstrained optimization. The search step methods included in ucSolve are: the Newton method, the quasi-Newton BFGS and DFP methods, the Fletcher-Reeves and Polak-Ribiere conjugate-gradient method, and the Fletcher conjugate descent method. The quasi-Newton methods may either update the inverse Hessian (standard) or the Hessian itself. The Newton method and the quasi-Newton methods updating the Hessian are using a subspace minimization technique to handle rank problems, see Lindstr¨om \[53\]. The quasi-Newton algorithms also use safe guarding techniques to avoid rank problem in the updated matrix. The line search algorithm in the routine LineSearch is a modified version of an algorithm by Fletcher \[20\]. Bound constraints are treated as described in Gill, Murray and Wright \[28\]. The accuracy in the line search is critical for the performance of quasi-Newton BFGS and DFP methods and for the CG methods. If the accuracy parameter Prob.LineParam.sigma is set to the default value 0.9, ucSolve changes it automatically according to:
Prob.Solver.Alg | Prob.LineParam.sigma |
4,5 (DFP) | 0.2 |
6,7,8 (CG) | 0.01 |
M-files Used
ResultDef.m, LineSearch.m, iniSolve.m, tomSolve.m, endSolve.m
See Also
clsSolve c
Additional solvers
Documentation for the following solvers is only available at http://tomopt.com and in the m-file help.
- goalSolve - For sparse multi-objective goal attainment problems, with linear and nonlinear constraints.
- Tlsqr - Solves large, sparse linear least squares problem, as well as unsymmetric linear systems.
- lsei - For linearly constrained least squares problem with both equality and inequality constraints.
- Tnnls - Also for linearly constrained least squares problem with both equality and inequality constraints.
- qld - For convex quadratic programming problem.