Unifying optimization algorithms to aid software system users: Optimx for R

John C. Nash, Ravi Varadhan

Research output: Contribution to journalArticlepeer-review

151 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalJournal of Statistical Software
Issue number9
StatePublished - Sep 2011


  • Gradient
  • Maximization
  • Minimization
  • Newton
  • R
  • Scaling
  • Wrapper

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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