Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: John C. Nash, Ravi Varadhan
Title: Unifying Optimization Algorithms to Aid Software System Users: optimx for R
Abstract: R users can often solve optimization tasks easily using the tools in the optim function in the stats package provided by default on R installations. However, there are many other optimization and nonlinear modelling tools in R or in easily installed add-on packages. These present users with a bewildering array of choices. optimx is a wrapper to consolidate many of these choices for the optimization of functions that are mostly smooth with parameters at most bounds-constrained. We attempt to provide some diagnostic information about the function, its scaling and parameter bounds, and the solution characteristics. optimx runs a battery of methods on a given problem, thus facilitating comparative studies of optimization algorithms for the problem at hand. optimx can also be a useful pedagogical tool for demonstrating the strengths and pitfalls of different classes of optimization approaches including Newton, gradient, and derivative-free methods.

Page views:: 11333. Submitted: 2010-08-12. Published: 2011-08-24.
Paper: Unifying Optimization Algorithms to Aid Software System Users: optimx for R     Download PDF (Downloads: 12326)
optimx_2011-8.1.tar.gz: R source package Download (Downloads: 746; 26KB)
v43i09.R: R example code from the paper Download (Downloads: 874; 544B)

DOI: 10.18637/jss.v043.i09

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.