These solvers apply to problems with smooth objective functions and constraints. They run Optimization Toolbox™ solvers repeatedly to try to locate a global solution or multiple local solutions.
Example showing that GlobalSearch returns fewer solutions than MultiStart, often with higher quality.
Find a global minimum in a problem having multiple local minima.
Example showing how to avoid starting from infeasible points.
Shows how to use MultiStart to help find a global minimum to a least-squares problem.
How to set up and run the solvers.
Provides detailed steps for creating a problem structure.
Describes what a solver object is, and how to set its properties.
Provides details on the ways to set the start points.
Provides basic examples of the complete workflow for both GlobalSearch and MultiStart.
Shows how to compute in parallel for faster searches.
An extended example showing ways to search for a global minimum.
Examples of how to search your space effectively and efficiently.
Considerations in setting local solver options and global solver properties.
How to set random seeds to reproduce results.
Describes the two types of iterative display for monitoring solver progress.
Describes the types of output structures that GlobalSearch and MultiStart can return.
Example showing how to plot multiple initial and final points in a 2-D problem.
Provides details and an example of monitoring and halting solvers by using output functions.
How to use both built-in and custom plot functions for monitoring solution progress.
GlobalSearch and MultiStart apply to smooth problems where there are multiple local solutions.
Describes the solver algorithms.
Describes the first output, usually called x, from GlobalSearch and MultiStart.
Describes how to obtain multiple solutions from GlobalSearch and MultiStart, and how to change the definition of distinct solutions.
Describes properties of GlobalSearch and MultiStart objects.