Unifying Optimization Algorithms to Aid Software System Users:<b>optimx</b>for<i>R</i>

Abstract

R users can often solve optimization tasks easily using the tools in the optim function in the <b>stats</b> 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. <b>optimx</b> 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. <b>optimx</b> runs a battery of methods on a given problem, thus facilitating comparative studies of optimization algorithms for the problem at hand. <b>optimx</b> 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.