Global or Multiple Starting Point Search
Multiple starting point solvers for gradient-based
optimization, constrained or unconstrained
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.
Functions
Objects
Topics
Problem-Based Multiple Start
- Minimize Nonlinear Function Using Multiple-Start Solver, Problem-Based
Find a better solution to a nonlinear problem using a multiple-start solver. - Specify Start Points for MultiStart, Problem-Based
Specify start points forMultiStartin the problem-based approach. - Find Multiple Local Solutions Using MultiStart or GlobalSearch, Problem-Based
Use thelocalfield of theoutputstructure to examine the points whereGlobalSearchandMultiStartstart. - MultiStart with lsqnonlin, Problem-Based
Fit a function to data usingMultiStartandlsqnonlin.
GlobalSearch and MultiStart Optimization Basics
- Find Global or Multiple Local Minima
Example showing thatGlobalSearchreturns fewer solutions thanMultiStart, often with higher quality. - Maximizing Monochromatic Polarized Light Interference Patterns Using GlobalSearch and MultiStart
Find a global minimum in a problem having multiple local minima. - Optimize Using Only Feasible Start Points
Example showing how to avoid starting from infeasible points. - MultiStart Using lsqcurvefit or lsqnonlin
Shows how to use MultiStart to help find a global minimum to a least-squares problem.
Optimization Workflow
- Workflow for GlobalSearch and MultiStart
How to set up and run the solvers. - Create Problem Structure
Provides detailed steps for creating a problem structure. - Create Solver Object
Describes what a solver object is, and how to set its properties. - Set Start Points for MultiStart
Provides details on the ways to set the start points. - Run the Solver
Provides basic examples of the complete workflow for both GlobalSearch and MultiStart.
Techniques for Effective Search
- Parallel MultiStart
Shows how to compute in parallel for faster searches. - Isolated Global Minimum
An extended example showing ways to search for a global minimum. - Refine Start Points
Examples of how to search your space effectively and efficiently. - Change Options
Considerations in setting local solver options and global solver properties. - Reproduce Results
How to set random seeds to reproduce results.
Examine Results
- Iterative Display
Describes the two types of iterative display for monitoring solver progress. - Global Output Structures
Describes the types of output structures that GlobalSearch and MultiStart can return. - Visualize the Basins of Attraction
Example showing how to plot multiple initial and final points in a 2-D problem. - Output Functions for GlobalSearch and MultiStart
Provides details and an example of monitoring and halting solvers by using output functions. - Plot Functions for GlobalSearch and MultiStart
How to use both built-in and custom plot functions for monitoring solution progress.
Multiple Start Solver Background
- Problems That GlobalSearch and MultiStart Can Solve
GlobalSearch and MultiStart apply to smooth problems where there are multiple local solutions. - How GlobalSearch and MultiStart Work
Describes the solver algorithms. - Single Solution
Describes the first four outputs, usually calledx,fval,exitflag, andoutput, from bothGlobalSearchandMultiStart. - Multiple Solutions
Describes how to obtain multiple solutions from GlobalSearch and MultiStart, and how to change the definition of distinct solutions. - GlobalSearch and MultiStart Properties (Options)
Describes properties of GlobalSearch and MultiStart objects.
