CEoptim: cross-entropy R package for optimization

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

doi:10.18637/jss.v076.i08

CEoptim: cross-entropy R package for optimization

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

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

67 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 language	English
Pages (from-to)	1-29
Number of pages	29
Journal	Journal of Statistical Software
Volume	76
Issue number	8
DOIs	https://doi.org/10.18637/jss.v076.i08
Publication status	Published - Feb 2017
Externally published	Yes

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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10.18637/jss.v076.i08Licence: CC BY

Publisher version (open access)Final published version, 738 KBLicence: CC BY

https://arxiv.org/abs/1503.01842

Cite this

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AU - Benham, Tim

AU - Duan, Qibin

AU - Kroese, Dirk P.

AU - Liquet, Benoît

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KW - Kullback-Leibler divergence

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CEoptim: cross-entropy R package for optimization

Abstract

Bibliographical note

Keywords

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