@article{JSSv043i09,
title={Unifying Optimization Algorithms to Aid Software System Users: optimx for R},
volume={43},
url={https://www.jstatsoft.org/index.php/jss/article/view/v043i09},
doi={10.18637/jss.v043.i09},
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.},
number={9},
journal={Journal of Statistical Software},
author={Nash, John C. and Varadhan, Ravi},
year={2011},
pages={1–14}
}