CEoptim: Cross-Entropy R Package for Optimization | Zendy

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

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CEoptim: Cross-Entropy R Package for Optimization

Author(s) -

Tim Benham,

Qibin Duan,

Dirk P. Kroese,

Benoît Liquet

Publication year - 2017

Publication title -

journal of statistical software

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.636

H-Index - 145

ISSN - 1548-7660

DOI - 10.18637/jss.v076.i08

Subject(s) - cross entropy method , computer science , mathematical optimization , optimization problem , minification , continuous optimization , optimization algorithm , entropy (arrow of time) , r package , combinatorial optimization , cross entropy , algorithm , range (aeronautics) , simple (philosophy) , principle of maximum entropy , mathematics , quadratic assignment problem , multi swarm optimization , artificial intelligence , computational science , physics , quantum mechanics , philosophy , materials science , epistemology , composite material

The cross-entropy (CE) method is 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.

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