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

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  • John C. Nash
  • Ravi Varadhan
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    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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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 43 ()
    Issue (Month): i09 ()
    Pages:

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    Handle: RePEc:jss:jstsof:43:i09

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    Web page: http://www.jstatsoft.org/

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    Cited by:
    1. Roger Bivand & Gianfranco Piras, 2013. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Working Papers 201301, Regional Research Institute, West Virginia University.
    2. Michal Engelman & Hal Caswell & Emily Agree, 2014. "Why do lifespan variability trends for the young and old diverge? A perturbation analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(48), pages 1367-1396, May.
    3. Herrera, Rodrigo, 2013. "Energy risk management through self-exciting marked point process," Energy Economics, Elsevier, vol. 38(C), pages 64-76.

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