CEoptim: cross-entropy R package for optimization

Tim Benham, Qibin Duan, Dirk P. Kroese, Benoît Liquet

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
2 Downloads (Pure)

Abstract

The cross-entropy (CE) method is a simple and versatile technique for optimization, based on Kullback-Leibler (or cross-entropy) minimization. The method can be applied to a wide range of optimization tasks, including continuous, discrete, mixed and constrained optimization problems. The new package CEoptim provides the R implementation of the CE method for optimization. We describe the general CE methodology for optimization and well as some useful modifications. The usage and efficacy of CEoptim is demonstrated through a variety of optimization examples, including model fitting, combinatorial optimization, and maximum likelihood estimation.

Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalJournal of Statistical Software
Volume76
Issue number8
DOIs
Publication statusPublished - Feb 2017
Externally publishedYes

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • constrained optimization
  • continuous optimization
  • cross-entropy
  • discrete optimization
  • Kullback-Leibler divergence
  • lasso
  • maximum likelihood
  • R
  • regression

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