Use surrogate optimization for expensive (time-consuming) objective functions. The solver requires finite bounds on all variables, allows for nonlinear inequality constraints, and accepts integer constraints on selected variables. The solver can optionally save its state after each function evaluation, enabling recovery from premature stops.
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Basic example minimizing a multidimensional function in the problem-based approach.
Solve integer and mixed-integer problems using the problem-based approach and
Solve a feasibility problem using the problem-based approach and
Solve a multidimensional problem using
fmincon, and then compare
Search for the global minimum using
surrogateopt, and then modify
options of the function to revise the search.
How to interpret a
fmincon on a nonsmooth problem.
Solve an antenna design problem using surrogate optimization.
Shows how to use checkpoint files to restart, recover, analyze, or extend an optimization.
Solve a problem containing a nonlinear ODE with a nonlinear constraint using
Presents techniques for converting objective and nonlinear constraint functions for
other solvers to and from
Integer-constrained surrogate optimization.
Choose components from lists to best fit a response curve.
Solve a nonlinear problem with both integer and nonlinear constraints.
surrogateopt to solve a feasibility problem.
Fix some variables by setting their upper and lower bounds equal.
This example shows how to perform custom parallel optimization using the
Hints for obtaining a better solution or obtaining a solution more quickly.
Surrogate optimization attempts to find a global minimum of an objective function using few objective function evaluations.
Learn details of the surrogate optimization algorithm, when run in serial or parallel.
Explore the options for surrogate optimization, including algorithm control, stopping criteria, command-line display, and output and plot functions.