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| R2012a Documentation → Global Optimization Toolbox | |
Learn more about Global Optimization Toolbox |
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| Contents | Index |
psoptimset
options = psoptimset
options = psoptimset('param1',value1,'param2',value2,...)
options = psoptimset(oldopts,'param1',value1,...)
options = psoptimset(oldopts,newopts)
psoptimset with no input or output arguments displays a complete list of parameters with their valid values.
options = psoptimset (with no input arguments) creates a structure called options that contains the options, or parameters, for the pattern search and sets parameters to their default values.
options = psoptimset('param1',value1,'param2',value2,...) creates a structure options and sets the value of 'param1' to value1, 'param2' to value2, and so on. Any unspecified parameters are set to their default values. It is sufficient to type only enough leading characters to define the parameter name uniquely. Case is ignored for parameter names.
options = psoptimset(oldopts,'param1',value1,...) creates a copy of oldopts, modifying the specified parameters with the specified values.
options = psoptimset(oldopts,newopts) combines an existing options structure, oldopts, with a new options structure, newopts. Any parameters in newopts with nonempty values overwrite the corresponding old parameters in oldopts.
The following table lists the options you can set with psoptimset. See Pattern Search Options for a complete description of the options and their values. Values in {} denote the default value. You can also view the optimization parameters and defaults by typing psoptimset at the command line.
| Option | Description | Values |
|---|---|---|
| Cache | With Cache set to 'on', patternsearch keeps a history of the mesh points it polls and does not poll points close to them again at subsequent iterations. Use this option if patternsearch runs slowly because it is taking a long time to compute the objective function. If the objective function is stochastic, it is advised not to use this option. | 'on'| {'off'} |
| CacheSize | Size of the history | Positive scalar | {1e4} |
| CacheTol | Positive scalar specifying how close the current mesh point must be to a point in the history in order for patternsearch to avoid polling it. Use if 'Cache' option is set to 'on'. | Positive scalar | {eps} |
| CompletePoll | Complete poll around current iterate | 'on'| {'off'} |
| CompleteSearch | Complete search around current iterate when the search method is a poll method | 'on'| {'off'} |
| Display | Level of display | 'off'| 'iter'| 'diagnose'| {'final'} |
| InitialMeshSize | Initial mesh size for pattern algorithm | Positive scalar| {1.0} |
| InitialPenalty | Initial value of the penalty parameter | Positive scalar| {10} |
| MaxFunEvals | Maximum number of objective function evaluations | Positiveinteger| {2000*numberOfVariables} |
| MaxIter | Maximum number of iterations | Positiveinteger| {100*numberOfVariables} |
| MaxMeshSize | Maximum mesh size used in a poll/search step | Positive scalar| {Inf} |
| MeshAccelerator | Accelerate convergence near a minimum | 'on'| {'off'} |
| MeshContraction | Mesh contraction factor, used when iteration is unsuccessful | Positive scalar|{0.5} |
| MeshExpansion | Mesh expansion factor, expands mesh when iteration is successful | Positive scalar| {2.0} |
| MeshRotate | Rotate the pattern before declaring a point to be optimum | 'off'| {'on'} |
| OutputFcns | Specifies a user-defined function that an optimization function calls at each iteration | Function handle or cell array of function handles | {[]} |
| PenaltyFactor | Penalty update parameter | Positive scalar| {100} |
| PlotFcns | Specifies plots of output from the pattern search | @psplotbestf| @psplotmeshsize| @psplotfuncount| @psplotbestx| {[]} |
| PlotInterval | Specifies that plot functions will be called at every interval | {1} |
| PollingOrder | Order of poll directions in pattern search | 'Random'| 'Success'| {'Consecutive'} |
| PollMethod | Polling strategy used in pattern search | {'GPSPositiveBasis2N'} | 'GPSPositiveBasisNp1'| 'GSSPositiveBasis2N'| 'GSSPositiveBasisNp1'| 'MADSPositiveBasis2N'| 'MADSPositiveBasisNp1' |
| ScaleMesh | Automatic scaling of variables | {'on'}| 'off' |
| SearchMethod | Type of search used in pattern search | @GPSPositiveBasis2N| @GPSPositiveBasisNp1| @GSSPositiveBasis2N| @GSSPositiveBasisNp1| @MADSPositiveBasis2N| @MADSPositiveBasisNp1| @searchga| @searchlhs| @searchneldermead | {[]} |
| TimeLimit | Total time (in seconds) allowed for optimization | Positivescalar| {Inf} |
| TolBind | Binding tolerance | Positive scalar| {1e-3} |
| TolCon | Tolerance on constraints | Positivescalar| {1e-6} |
| TolFun | Tolerance on function | Positivescalar| {1e-6} |
| TolMesh | Tolerance on mesh size | Positive scalar| {1e-6} |
| TolX | Tolerance on variable | Positivescalar| {1e-6} |
| UseParallel | Compute objective functions of a poll or search in parallel. | 'always' | {'never'} |
| Vectorized | Specifies whether functions are vectorized | 'on'| {'off'} |
Learn how to use optimization to solve systems of equations, fit models to data, or optimize system performance.
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