Function to choose next evaluation point, specified as one of the listed choices.

Acquisition functions whose names includeper-seconddo not yield reproducible results because the optimization depends on the runtime of the objective function. Acquisition functions whose names includeplusmodify their behavior when they are overexploiting an area. For more details, seeAcquisition Function Types.

Example:'AcquisitionFunctionName','expected-improvement-per-second'

Specify deterministic objective function, specified asfalseortrue. Iffunis stochastic (that is,fun(x)can return different values for the samex), then setIsObjectiveDeterministictofalse. In this case,bayesoptestimates a noise level during optimization.

Example:'IsObjectiveDeterministic',true

Data Types:logical

Propensity to explore, specified as a positive real. Applies to the'expected-improvement-plus'and'expected-improvement-per-second-plus'acquisition functions. SeePlus.

Example:“ExplorationRatio”,0.2

Data Types:double

Fit Gaussian Process model toGPActiveSetSizeor fewer points, specified as a positive integer. Whenbayesopthas visited more thanGPActiveSetSizepoints, subsequent iterations that use a GP model fit the model toGPActiveSetSizepoints.bayesoptchooses points uniformly at random without replacement among visited points. Using fewer points leads to faster GP model fitting, at the expense of possibly less accurate fitting.

Example:'GPActiveSetSize',80

Data Types:double

Compute in parallel, specified asfalse(do not compute in parallel) ortrue(compute in parallel). Computing in parallel requires Parallel Computing Toolbox™.

bayesopt执行并行目标函数评价concurrently on parallel workers. For algorithmic details, seeParallel Bayesian Optimization.

Example:'UseParallel',true

Data Types:logical

Imputation method for parallel worker objective function values, specified as'clipped-model-prediction','model-prediction','max-observed', or'min-observed'. To generate a new point to evaluate,bayesoptfits a Gaussian process to all points, including the points being evaluated on workers. To fit the process,bayesoptimputes objective function values for the points that are currently on workers.ParallelMethodspecifies the method used for imputation.

Example:'ParallelMethod','max-observed'

Tolerance on the number of active parallel workers, specified as a positive integer. Afterbayesoptassigns a point to evaluate, and before it computes a new point to assign, it checks whether fewer thanMinWorkerUtilizationworkers are active. If so,bayesopt分配随机点within bounds to all available workers. Otherwise,bayesoptcalculates the best point for one worker.bayesoptcreates random points much faster than fitted points, so this behavior leads to higher utilization of workers, at the cost of possibly poorer points. For details, seeParallel Bayesian Optimization.

Example:'MinWorkerUtilization',3

Data Types:double

Objective function evaluation limit, specified as a positive integer.

Example:'MaxObjectiveEvaluations',60

Data Types:double

Time limit, specified as a positive real. The time limit is in seconds, as measured byticandtoc.

Run time can exceedMaxTimebecausebayesoptdoes not interrupt function evaluations.

Example:'MaxTime',3600

Data Types:double

Number of initial evaluation points, specified as a positive integer.bayesoptchooses these points randomly within the variable bounds, according to the setting of theTransformsetting for each variable (uniform for'none', logarithmically spaced for'log').

Example:'NumSeedPoints',10

Data Types:double

Deterministic constraints on variables, specified as a function handle.

For details, seeDeterministic Constraints — XConstraintFcn.

Example:'XConstraintFcn',@xconstraint

Data Types:function_handle

Conditional variable constraints, specified as a function handle.

For details, seeConditional Constraints — ConditionalVariableFcn.

Example:'ConditionalVariableFcn',@condfun

Data Types:function_handle

Number of coupled constraints, specified as a positive integer. For details, seeCoupled Constraints.

Example:'NumCoupledConstraints',3

Data Types:double

Indication of whether coupled constraints are deterministic, specified as a logical vector of lengthNumCoupledConstraints. For details, seeCoupled Constraints.

Example:'AreCoupledConstraintsDeterministic',[true,false,true]

Data Types:logical

Command-line display level, specified as0,1, or2.

Example:'Verbose',2

Data Types:double

Function called after each iteration, specified as a function handle or cell array of function handles. An output function can halt the solver, and can perform arbitrary calculations, including creating variables or plotting. Specify several output functions using a cell array of function handles.

There are two built-in output functions:

You can write your own output functions. For details, seeBayesian Optimization Output Functions.

Example:'OutputFcn',{@saveToFile @myOutputFunction}

Data Types:cell|function_handle

File name for the@saveToFileoutput function, specified as a character vector or string scalar. The file name can include a path, such as'../optimizations/September2.mat'.

Example:'SaveFileName','September2.mat'

Data Types:char|string

Variable name for the@assignInBaseoutput function, specified as a character vector or string scalar.

Example:'SaveVariableName','September2Results'

Data Types:char|string

Plot function called after each iteration, specified as'all', a function handle, or a cell array of function handles. A plot function can halt the solver, and can perform arbitrary calculations, including creating variables, in addition to plotting.

Specify no plot function as[].

'all'calls all built-in plot functions. Specify several plot functions using a cell array of function handles.

The built-in plot functions appear in the following tables.

You can write your own plot functions. For details, seeBayesian Optimization Plot Functions.

Example:'PlotFcn','all'

Data Types:char|string|cell|function_handle

Initial evaluation points, specified as anN-by-D表格, whereNis the number of evaluation points, andDis the number of variables.

Data Types:表格

Objective values corresponding toInitialX, specified as a length-Nvector, whereNis the number of evaluation points.

Example:'InitialObjective',[17;-3;-12.5]

Data Types:double

约束违反的耦合约束,规范ified as anN-by-Kmatrix, whereNis the number of evaluation points andKis the number of coupled constraints. For details, seeCoupled Constraints.

Data Types:double

Errors forInitialX, specified as a length-Nvector with entries-1or1, whereNis the number of evaluation points. Specify-1for no error, and1for an error.

Example:'InitialErrorValues',[-1,-1,-1,-1,1]

Data Types:double

Initial data corresponding toInitialX, specified as a length-Ncell vector, whereNis the number of evaluation points.

Example:'InitialUserData',{2,3,-1}

Data Types:cell

Evaluation times of objective function atInitialX, specified as a length-Nvector, whereNis the number of evaluation points. Time is measured in seconds.

Data Types:double

Times for the firstNiterations, specified as a length-Nvector, whereNis the number of evaluation points. Time is measured in seconds.

Data Types:double