[R-pkgs] Release of optimbase, optimsimplex and neldermead packages

From: Sebastien Bihorel <sb.pmlab_at_gmail.com>
Date: Thu, 06 May 2010 08:36:49 -0400


Dear R users,

I am pleased to announce the release of three new R packages: optimbase, optimsimplex, and neldermead.
- optimbase provides a set of commands to manage an abstract optimization
method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...
- optimsimplex provides 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, Box's algorithm for constrained optimization, the multi-dimensional search by Torczon, etc...
- neldermead depends on optimbase and optimsimplex and 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 size simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed sized simplex), the variable size simplex method of Nelder and Mead (unconstrained optimization with a variable sized simplex), Box's complex method (constrained optimization with a variable sized simplex). This package includes an R-port of the fminsearch function (available in Matlab and Scilab) which is a specialized use of the more general neldermead package and computes the unconstrained minimimum of given function with the Nelder-Mead algorithm.

One important benefit offered by those packages (especially optimbase) is the possibly for the user to define functions that will be called at the each iteration of the optimization process. These functions may be used when debugging a specialized optimization algorithm, to write to one or several report files, or create/update optimization graphs. optimbase can accommodate both derivative-based or derivative-free algorithms.

The three packages are ports of original Scilab modules written by Michael Baudin at the Digiteo Consortium (and previously at Institut National de Recheche en Informatique et en Automatique).

All packages are available in version 1.0-1 on CRAN.

Any question, comment or feedback on those packages can be sent at: sb.pmlab_at_gmail.com.

Sebastien Bihorel

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