sdo.OptimizeOptions

Optimization option set for sdo.optimize function

Description

Use an sdo.OptimizeOptions object to specify options for solving a design optimization problem using the sdo.optimize function. You can specify options such as a solver method, solver options, and the use of parallel computing during optimization.

Creation

Syntax

opt = sdo.OptimizeOptions

opt = sdo.OptimizeOptions(Name,Value)

Description

opt = sdo.OptimizeOptions creates a default option set for solving a design optimization problem using the sdo.optimize function. To modify the properties of this option set for your specific application, use dot notation.

opt = sdo.OptimizeOptions(Name,Value) creates an option set with properties specified using one or more name-value arguments.

Properties

Method — Optimization solver

'fmincon' (default) | 'fminsearch' | 'lsqnonlin' | 'patternsearch' | 'paretosearch' | 'surrogateopt'

Optimization solver that sdo.optimize uses to solve the optimization problem, specified as one of the following values:

• 'fmincon' — Default for design optimization, derivative based method, allows nonlinear constraints and parameter bounds.

• 'fminsearch' — Derivative-free method.

• 'lsqnonlin' — Default for parameter estimation. This method works with gradient of residual between model output and data. This method allows nonlinear constraints (since R2023b) and parameter bounds.

• 'patternsearch' — Derivative-free method. This method allows nonlinear constraints and parameter bounds. Pattern search requires Global Optimization Toolbox software.

• 'paretosearch' — This method searches iteratively for nondominated points to solve multiobjective optimization problems. It requires Global Optimization Toolbox software. For more information, see paretosearch (Global Optimization Toolbox).

• 'surrogateopt' — Surrogate-based method. This method allows nonlinear constraints and discrete-valued parameters. The method requires parameter bounds. Using surrogate-based optimization requires Global Optimization Toolbox software.

See the Optimization Toolbox™ and Global Optimization Toolbox documentation for more information on these solvers.

MethodOptions — Optimization solver options

[1x1 optim.options.Fmincon] (default) | options object created using optimoptions

Optimization solver options, specified as an optimization options object, created using optimoptions. The options are configured based on the Method property. Because 'fmincon' is the default optimization solver method, the default value for this property is an optimoptions object for fmincon solver. For information about the available optimization solver options, consult the options table for your solver based on the Method specified:

• fmincon options

• fminsearch options

• lsqnonlin options

• patternsearch options (Global Optimization Toolbox)

• paretosearch options (Global Optimization Toolbox)

• surrogateopt options (Global Optimization Toolbox)

To modify the solver options, use dot notation. For example, opt.MethodOptions.StepTolerance = 1.5e-3.

Some of the default values for patternsearch and paretosearch options are different from those in their respective pages. They are:

Option	Method	Default
ConstraintTolerance	patternsearch, paretosearch	1e-3
Display	patternsearch, paretosearch	'iter'
MaxIterations	patternsearch, paretosearch	100
FunctionTolerance	patternsearch	1e-3
MaxFunctionEvaluations	patternsearch	Inf
StepTolerance	patternsearch	1e-3
UseCompleteSearch	patternsearch	true
MeshTolerance	paretosearch	1e-3
ParetoSetChangeTolerance	paretosearch	1e-3
ParetoSetSize	paretosearch	30

Restarts — Number of times to restart optimization

0 (default) | nonnegative integer

Number of times to restart optimization if convergence criteria are not satisfied, specified as a nonnegative integer. At each restart, the initial values of the tunable parameters are set to the final value of the previous optimization run.

StopIfFeasible — Option to stop optimization once feasible solution is found

'on' (default) | 'off'

Option to stop optimization once a feasible solution satisfying constraints is found, specified as one of the following values:

• 'on' — Terminate as soon as a feasible solution is found.

• 'off' — Continue to search for solutions that are typically located further inside the constraint region.

The software ignores this option when you track a reference signal or your problem has a cost.

GradFcn — Option to return gradient information from cost or constraint function

'off' (default) | 'on'

Option to specify that the cost or constraint function you provide to sdo.optimize provides gradient information, specified as one of the following values:

• 'off' — Your cost or constraint function does not provide gradient information. The software uses central differences to compute the gradients.

• 'on' — Your cost or constraint function provides gradient information as one of its output arguments.

UseParallel — Parallel computing option

false or 0 (default) | true or 1

Parallel computing option for fmincon, lsqnonlin, and patternsearch optimization solvers, specified as one of the following:

• false or 0 — Do not use parallel computing during optimization.

• true or 1 — Use parallel computing during optimization.

Parallel Computing Toolbox™ software must be installed to enable parallel computing for the optimization methods.

When set to true, the methods compute the following in parallel:

• fmincon — Finite difference gradients

• lsqnonlin — Finite difference gradients

• patternsearch — Poll and search set evaluation

Note

Parallel computing is not supported for fminsearch.

For parallel computing, specify a simulation scenario for the Simulink^® model to optimize as an sdo.SimulationTest object in the OptimizedModel property. You can specify model file dependencies in the ParallelFileDependencies property, or you can specify paths to dependencies in the ParallelPathDependencies property, if needed.

ParallelFileDependencies — File dependencies to use during parallel optimization

{} (default) | cell array of character vectors

File dependencies to use during parallel optimization, specified as a cell array of character vectors. Each character vector can specify either an absolute or relative path to a file. These files are copied to the workers during parallel optimization. Use sdo.getModelDependencies to find the dependencies of a Simulink model.

Example: opt.ParallelFileDependencies = {'C:\matlab\work\file1.m','C:\matlab\myProject\file2.m'}

ParallelPathDependencies — Paths to dependencies to use during parallel optimization

{} (default) | cell array of character vectors

Paths to dependencies to use during parallel optimization, specified as a cell array of character vectors. If you do not want to copy the files to workers, use this property instead of the ParallelFileDependencies property. These path dependencies are temporarily added to the workers during parallel optimization. Use sdo.getModelDependencies to find the dependencies of a Simulink model.

Example: opt.ParallelPathDependencies = {'C:\matlab\work','C:\matlab\myProject'}

OptimizedModel — Name of Simulink model to optimize

'' (default) | sdo.SimulationTest object | character vector

Name of Simulink model to optimize, specified as either an sdo.SimulationTest object or a character vector with the name of the model.

Specify OptimizedModel as an sdo.SimulationTest object when using both parallel optimization (UseParallel = true) and fast restart.

Parallel Computing Toolbox software must be installed to enable parallel optimization.

Example: Simulator = sdo.SimulationTest('model_demo')

BackupFilename — Name of .mat backup file

[] (default) | string

Name of the .mat backup file, specified as a string. If you specify a filename, the software saves the parameter iteration data to the file during estimation or optimization. If MATLAB^® crashes due to a computer shutdown during estimation or optimization, you can retrieve the data using this backup file. If the estimation or optimization completes without any interruption, the software automatically deletes the file.

Object Functions

sdo.optimize

Solve design optimization problem

Examples

Create Default Optimization Options Object

Create a default sdo.OptimizeOptions option set.

opt = sdo.OptimizeOptions;

Specify options using dot notation.

opt.Method = 'lsqnonlin';
opt.GradFcn = 'on';

Any property values you do not specify remain at their default values.

Specify Optimization Solver

Create an option set object that specifies nonlinear least-squares optimization solver as its solving method.

opt = sdo.OptimizeOptions('Method','lsqnonlin')

opt = 
  OptimizeOptions with properties:

                      Method: 'lsqnonlin'
               MethodOptions: [1×1 optim.options.Lsqnonlin]
                    Restarts: 0
              StopIfFeasible: 'on'
                     GradFcn: 'off'
                 UseParallel: 0
    ParallelPathDependencies: {}
    ParallelFileDependencies: {}
              OptimizedModel: ''
              BackupFilename: ''

You can modify solver options using dot notation. For example, set the solver algorithm to the Levenberg-Marquardt method.

opt.MethodOptions.Algorithm = 'levenberg-marquardt';

View the method options.

opt.MethodOptions

ans = 
  lsqnonlin options:

   Options used by current Algorithm ('levenberg-marquardt'):
   (Other available algorithms: 'interior-point', 'trust-region-reflective')

   Set properties:
                   Algorithm: 'levenberg-marquardt'
                     Display: 'iter'
           FunctionTolerance: 1.0000e-03
      MaxFunctionEvaluations: Inf
               MaxIterations: 100
    SpecifyObjectiveGradient: 1
               StepTolerance: 1.0000e-03

   Default properties:
    FiniteDifferenceStepSize: 'sqrt(eps)'
        FiniteDifferenceType: 'forward'
                   OutputFcn: []
                     PlotFcn: []
                    TypicalX: 'ones(numberOfVariables,1)'
                 UseParallel: 0

   Show options not used by current Algorithm ('levenberg-marquardt')

Any property values you do not specify remain at their default values.

Version History

Introduced in R2011a

R2025a: paretosearch solver support for multiobjective tradeoff analysis

You can now obtain the pareto-optimal designs by specifying the Method property of sdo.OptimizeOptions as 'paretosearch'.

For information on the paretosearch algorithm, see paretosearch Algorithm (Global Optimization Toolbox).

R2025a: Save and retrieve iteration data

You can now save the parameter iteration data while solving a parameter estimation or design optimization problem in a .mat file by specifying the BackupFilename property. If the computer shuts down during estimation or optimization, you can retrieve the data using this .mat file.