The goal of this package is to provide a building block for optimization algorithms based on a simplex. The optimsimplex package may be used in the following optimization methods:
the simplex method of Spendley et al.,
the method of Nelder and Mead,
the Box's algorithm for constrained optimization,
the multi-dimensional search by Torczon,
Features The following is a list of features currently provided:
Manage various simplex initializations
initial simplex given by user,
initial simplex computed with a length and along the coordinate axes,
initial regular simplex computed with Spendley et al. formula,
initial simplex computed by a small perturbation around the initial guess point,
initial simplex computed from randomized bounds.
sort the vertices by increasing function values,
compute the standard deviation of the function values in the simplex,
compute the simplex gradient with forward or centered differences,
shrink the simplex toward the best vertex,
vignette('optimsimplex',package='optimsimplex') for more
Author of Scilab optimsimplex module: Michael Baudin (INRIA - Digiteo)
Author of R adaptation: Sebastien Bihorel (firstname.lastname@example.org)
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