Data Mining Algorithms In R/Packages/optimsimplex/optimsimplex-package
Description edit
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,
- etc ...
Features edit
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,
- etc...
Details edit
| Package: | optimsimplex |
| Type: | Package |
| Version: | 1.0-2 |
| Date: | 2010-05-11 |
| License: | CeCILL-2 |
| LazyLoad: | yes |
Authors edit
Author of Scilab optimsimplex module: Michael Baudin (INRIA - Digiteo)
Author of R adaptation: Sebastien Bihorel (sb.pmlab@gmail.com)