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Unifying Optimization Algorithms to Aid Software System Users: optimx for R

Published on Aug 24, 2011in Journal of Statistical Software11.65
· DOI :10.18637/jss.v043.i09
John C. Nash8
Estimated H-index: 8
(U of O: University of Ottawa),
Ravi Varadhan24
Estimated H-index: 24
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.
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