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.
|Create values for optimization problem|
|Solve optimization problem or equation problem|
|Create optimization problem structure|
|List start points|
|Run multiple-start solver|
|Custom start points|
|Random start points|
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 for
MultiStartin the problem-based approach.
- Find Multiple Local Solutions Using MultiStart or GlobalSearch, Problem-Based
localfield of the
outputstructure to examine the points where
- MultiStart with lsqnonlin, Problem-Based
Fit a function to data using
GlobalSearch and MultiStart Optimization Basics
- Find Global or Multiple Local Minima
Example showing that
GlobalSearchreturns fewer solutions than
MultiStart, 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.
- 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.
- 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 called
output, from both
- 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.