Common input for all CGO solvers
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This page is part of the CGO Manual. See CGO Manual. |
Contents |
Input parameters
Prob structure
The following fields are used in the problem description structure Prob | |||||||||||||
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Field | Description | ||||||||||||
Name | Name of the problem. Used for security when doing warm starts. | ||||||||||||
FUNCS.f | The routine to compute the function, given as a string, e.g. CGOF. | ||||||||||||
FUNCS.c | The routine to compute the nonlinear constraint, e.g. CGOC. | ||||||||||||
x_L | Lower bounds for each element in x. Must be finite. | ||||||||||||
x_U | Upper bounds for each element in x. Must be finite. | ||||||||||||
b_L | Lower bounds for the linear constraints. | ||||||||||||
b_U | Upper bounds for the linear constraints. | ||||||||||||
A | Linear constraint matrix. | ||||||||||||
c_L | Lower bounds for the nonlinear constraints. | ||||||||||||
c_U | Upper bounds for the nonlinear constraints. | ||||||||||||
WarmStart | If true, >0, the solver reads the output from the last run from the mat-file cgoSave.mat, and continues from the last run.
If Prob.CGO.WarmStartInfo has been defined through a call to WarmDefGLOBAL, this field is used instead of the cgoSave.mat file. In the last case, exactly the same points are used. In the first case, a new test is made which points to use. | ||||||||||||
MaxCPU | Maximal CPU Time (in seconds) to be used. | ||||||||||||
user | User field used to send information to low-level functions. | ||||||||||||
PriLevOpt | Print Level.
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PriLevSub | Print Level in subproblem solvers, see help in snSolve and gnSolve. | ||||||||||||
f_Low | Lower bound on the optimal function value. If defined, used to restrict the target values into interval ['f_Low,min(surface)]. | ||||||||||||
optParam | Structure with optimization parameters. See the table below describing the fields used in the optParam structure. | ||||||||||||
CGO | Structure with parameters specific to costly global optimization. See the table below describing the fields used in the CGO structure. | ||||||||||||
GO | Structure with parameters specific to global optimization. See the table below describing the fields used in the GO structure. | ||||||||||||
MIP | Structure with parameters specific to mixed integer optimization. See the table below describing the fields used in the MIP structure. |
optParam structure
The most important field is MaxFunc, normally use default values for other fields.
fields used in Prob.optParam: | |
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Field | Description |
MaxFunc | Maximal number of costly function evaluations, default 300 for rbfSolve and arbfMIP, and default 200 for ego. MaxFunc must be <= 5000. If WarmStart = 1 and MaxFunc <= nFunc (Number of f(x) used) then set MaxFunc := MaxFunc + nFunc. |
IterPrint | Print one information line each iteration, and the new x tried. Default IterPrint = 1. fMinI means the best f(x) is infeasible. fMinF means the best f(x) is feasible (also integer feasible). |
fGoal | Goal for function value, not used if inf or empty. |
eps_f | Relative accuracy for function value, fTol == eps_f. Stop if |f - f Goal| <= |fGoal| * fTol, if fGoal ≠ 0. Stop if |f - fGoal| <= fTol, if fGoal = 0. See the output field maxTri. |
bTol | Linear constraint tolerance. |
cTol | Nonlinear constraint tolerance. |
MaxIter | Maximal number of iterations used in the local optimization on the re- sponse surface in each step. Default 1000, except for pure IP problems, then max(GO.MaxFunc, MaxIter);. |
CGO structure
The CGO solvers need an initial set of at least n+1 points, where n = dim(x).
Either an initial Experimental Design is needed or the user may input its own choice of points, or a combination of both.
A warm start with points from previous run(s) using any CGO solver is also possible.
The input parameters for the Experimental Design are given as fields in Prob.CGO.
See help expDesign for details on how to set the following fields in Prob.CGO defining the Experimental Design:
Percent, nSample, X, F, Cc, RandState, AddMP, nTrial, CLHMethod
Note that the setting of RandState might influence parts of the CGO solvers as well.
fields used in Prob.CGO: | |||||||||||||||||||||
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Field | Description | ||||||||||||||||||||
Percent | See expDesign for detailed information on how to set the experimental design parameters. | ||||||||||||||||||||
nSample | |||||||||||||||||||||
X | |||||||||||||||||||||
F | |||||||||||||||||||||
CX | |||||||||||||||||||||
RandState | |||||||||||||||||||||
AddMP | |||||||||||||||||||||
nTrial | |||||||||||||||||||||
CLHMethod | |||||||||||||||||||||
SCALE | 0 - Original search space (Default if any integer values)
1 - Transform search space to unit cube (Default if no integers) | ||||||||||||||||||||
objType | Tranformation of function F, all transformations are computed and saved. Selected objType is used in CGO interpolations.
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REPLACE | In objType == 8, large function values Z are replaced by new values Replacement: Z:= FMAX + log_{10}(Z-FMAX+1), where FMAX = 10^{REPLACE}, | ||||||||||||||||||||
XMIN | Matrix d x nLocal with nLocal minima previously found. Best point(s) found might be global minima Sampling of points will be avoided around these points, see epsDist below | ||||||||||||||||||||
FMIN | Column vector with nLocal costly function values f(x) computed at the nLocal points in XMIN | ||||||||||||||||||||
epsDist | If epsDist > 10^{-7}, all points x in the sphere ||x - XMIN(:,i)|| <= epsDist are excluded from the search. Default is epsDist = 0, then the solver does not try to detect local minima, and just continues until maxFunc reached | ||||||||||||||||||||
SMOOTH | = 1 The problem is smooth enough for local search using numerical gradient estimation methods
= 0 the problem is nonsmooth or noisy, and local search methods using numerical gradient estimation are likely to produce garbage search directions. | ||||||||||||||||||||
globalSolver | Global optimization solver used for subproblem optimization. Default glcCluster (SMOOTH=1) or glcDirect (SMOOTH=0). If the global- Solver is [glcCluster|glcCluster]], the fields Prob.GO.maxFunc1, Prob.GO.maxFunc2, Prob.GO.maxFunc3, Prob.GO.localSolver, Prob.GO.DIRECT and other fields set in Prob.GO are used. See the help for these parameters in glcCluster. | ||||||||||||||||||||
localSolver | Local optimization solver used for subproblem optimization. If not defined, the TOMLAB default constrained NLP solver is used. |
GO structure
fields used in Prob.GO: | |
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Field | Description |
GO | Structure Prob.GO (Default values are set for all fields).
The following fields are used: |
MaxFunc | Total number of function values |
MaxIter | Maximal number of iterations in each local search. |
localSolver | Optionally change local solver used ('snopt' or 'npsol' etc.) If not defined, then Prob.CGO.localSolver is used |
The following fields are only used by glcCluster, see help glcCluster | |
maxFunc1 | See glcCluster |
maxFunc2 | |
maxFunc3 | |
DIRECT | |
maxLocalTry |
MIP structure
Defines integer optimization parameters.
fields used in Prob.MIP: | |
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Field | Description |
IntVars | If empty, all variables are assumed non-integer.
If islogical(IntVars) (=all elements are 0/1), then 1 = integer variable, 0 = continuous variable. If any element > 1, IntVars is the indices for integer variables. |