global or multiple starting point search -凯发k8网页登录

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

problem-based solution

	create values for optimization problem
	solve optimization problem or equation problem

solver-based solution

	create optimization problem structure
	list start points
	run multiple-start solver

objects

solvers

	find global minimum
	find multiple local minima

supporting objects

	custom start points
	optimization solution
	random start points

topics

problem-based multiple start

find a better solution to a nonlinear problem using a multiple-start solver.
specify start points for multistart in the problem-based approach.
use the local field of the output structure to examine the points where globalsearch and multistart start.
fit a function to data using multistart and lsqnonlin.

globalsearch and multistart optimization basics

example showing that globalsearch returns 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.
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

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.
set start points for multistart
provides details on the ways to set the start points.
provides basic examples of the complete workflow for both globalsearch and multistart.

techniques for effective search

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.

examine 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.

multiple start solver background

globalsearch and multistart apply to smooth problems where there are multiple local solutions.
describes the solver algorithms.
describes the first four outputs, usually called x, fval, exitflag, and output, from both 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.