resume

Resume a Bayesian optimization

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Syntax

newresults = resume(results,Name,Value)

Description

example

newresults = resume(results,Name,Value) resumes the optimization that produced results with additional options specified by one or more Name,Value pair arguments.

Examples

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Open Script

This example shows how to resume a Bayesian optimization. The optimization is for a deterministic function known as Rosenbrock's function, which is a well-known test case for nonlinear optimization. The function has a global minimum value of 0 at the point [1,1].

Create two real variables bounded by -5 and 5.

x1 = optimizableVariable('x1',[-5,5]);
x2 = optimizableVariable('x2',[-5,5]);
vars = [x1,x2];

Create the objective function.

function f = rosenbrocks(x)

f = 100*(x.x2 - x.x1^2)^2 + (1 - x.x1)^2;


fun = @rosenbrocks;

For reproducibility, set the random seed, and set the acquisition function to 'expected-improvement-plus' in the optimization.

rng default
results = bayesopt(fun,vars,'Verbose',0,...
    'AcquisitionFunctionName','expected-improvement-plus');

View the best point found and the best modeled objective.

results.XAtMinObjective
results.MinEstimatedObjective

ans =

  1x2 table

      x1        x2  
    ______    ______

    1.2421    1.5299


ans =

   -9.5402

The best point is somewhat close to the optimum, but the function model is inaccurate. Resume the optimization for 30 more points (a total of 60 points), this time telling the optimizer that the objective function is deterministic.

newresults = resume(results,'IsObjectiveDeterministic',true,'MaxObjectiveEvaluations',30);
newresults.XAtMinObjective
newresults.MinEstimatedObjective

ans =

  1x2 table

      x1        x2  
    ______    ______

    1.0473    1.1002


ans =

   -0.0066

The objective function model is much closer to the true function this time. The best point is closer to the true optimum.

Input Arguments

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Bayesian optimization results, specified as a BayesianOptimization object.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: resume(results,'MaxObjectiveEvaluations',60)

You can use any name-value pair accepted by bayesopt except for those beginning with Initial. See the bayesopt Input Arguments.

Note

The MaxObjectiveEvaluations and MaxTime name-value pairs mean additional time or evaluations, above the numbers stored in results. So, for example, the default number of evaluations is 30 in addition to the original specification.

Additionally, you can use the following name-value pair.

Modify variable, specified as an OptimizableVariable object.

You can change only the following properties of a variable in an optimization.

  • Range of real or integer variables. For example,

    xvar = optimizableVariable('x',[-10,10]);
% Modify the range:
xvar.Range = [1,5];

  • Type between 'integer' and 'real'. For example,

    xvar.Type = 'integer';

  • Transform of real or integer variables between 'log' and 'none'. For example,

    xvar.Transform = 'log';

Output Arguments

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Optimization results, returned as a BayesianOptimization object.

See Also

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Introduced in R2016b

Statistics and Machine Learning Toolbox Documentation

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