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neldermead

The neldermead package provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex), the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made), and Box's complex method (constrained optimization with a variable shape simplex).

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Version

Install

install.packages('neldermead')

Monthly Downloads

387

Version

1.0-12

License

CeCILL-2

Maintainer

Sebastien Bihorel

Last Published

February 1st, 2022

Functions in neldermead (1.0-12)

fminsearch.function

fminsearch Cost Function Call
neldermead-package

R port of the Scilab neldermead module
fmin.gridsearch

Grid evaluation of an unconstrained cost function
costf.transposex

Cost Function Call
neldermead

S3 neldermead object
fminsearch.outputfun

fminsearch Output Function Call
fminbnd

Computation of the constrained minimimum of given function with the Nelder-Mead algorithm.
fminbnd.function

fminbnd Cost Function Call
fminbnd.outputfun

fminbnd Output Function Call
optimget

Queries an optimization option list
fminsearch

Computation of the unconstrained minimum of given function with the Nelder-Mead algorithm.
optimset

Configures and returns an optimization data structure.
Secondary search functions

Secondary functions for neldermead.search
neldermead.set

Neldermead Object Configuration
neldermead.algo

Nelder-Mead Algorithm
neldermead.destroy

Erase a neldermead object.
neldermead.function

Call Cost Function.
neldermead.get

Get the value for the given element
optimset.method

Default set of optimization options
neldermead.restart

Restart neldermead search.
neldermead.search

Starts the optimization