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).
|Author||Sebastien Bihorel, Michael Baudin (author of the original module)|
|Date of publication||2015-01-11 23:16:07|
|Maintainer||Sebastien Bihorel <firstname.lastname@example.org>|
costf.transposex: Cost Function Call
fminbnd: Computation of the constrained minimimum of given function...
fminbnd.function: fminbnd Cost Function Call
fminbnd.outputfun: fminsearch Output Function Call
fmin.gridsearch: Grid evaluation of an unconstrained cost function
fminsearch: Computation of the unconstrained minimum of given function...
fminsearch.function: fminsearch Cost Function Call
fminsearch.outputfun: fminbnd Output Function Call
neldermead: S3 neldermead object
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
neldermead-package: R port of the Scilab neldermead module
neldermead.restart: Restart neldermead search.
neldermead.search: Starts the optimization
neldermead.set: Neldermead Object Configuration
neldermead.startup: Secondary functions for neldermead.search
optimget: Queries an optimization option list
optimset: Configures and returns an optimization data structure.
optimset.method: Default set of optimization options