CEoptim: Cross-Entropy R Package for Optimization

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.

Keywords

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