bayesopt matlab

Bayesian optimization is a technique used to optimize expensive black-box functions. In MATLAB, the bayesopt function can be used for Bayesian optimization.

Here is an example code for performing Bayesian optimization in MATLAB:

% Define objective function
fun = @(x) 1/(1.5*(1-x(1))^2 + 2*(2-x(2))^2 + 2*(3-x(3))^2 + 1*(4-x(4))^2 + 0.01*randn(1)); 

% Set bounds for input variables
lb = [0.5, 1.5, 2.5, 3.5];
ub = [1.5, 2.5, 3.5, 4.5];

% Set options for Bayesian optimization
opts = bayesoptOptions('AcquisitionFunctionName','expected-improvement-plus', 'MaxObjectiveEvaluations', 50);

% Perform Bayesian optimization
results = bayesopt(fun, lb, ub, opts);

% Print optimal solution
disp(results.XAtMinObjective)

In this code, the objective function is defined, along with the lower and upper bounds for the input variables. The bayesoptOptions function is used to set options for the Bayesian optimization algorithm, such as the acquisition function and the maximum number of objective evaluations. Then, the bayesopt function is called with the objective function and bounds as input, along with the options. Finally, the XAtMinObjective property of the results structure is used to print the optimal solution found by the algorithm.