CEoptim: Cross-Entropy R Package for Optimization

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.

[1]  Karline Soetaert,et al.  Solving Differential Equations in R: Package deSolve , 2010 .

[2]  Dirk P. Kroese,et al.  The cross-entropy method for estimation , 2013 .

[3]  SK Mishra,et al.  Global Optimization by Differential Evolution and Particle Swarm Methods: Evaluation on Some Benchmark Functions , 2006 .

[4]  Katharine M. Mullen.  Continuous Global Optimization in R , 2014 .

[5]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[6]  Dirk P. Kroese,et al.  Optimal generation expansion planning via the Cross-Entropy method , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[7]  R. Rubinstein.  The Cross-Entropy Method for Combinatorial and Continuous Optimization , 1999 .

[8]  Reuven Y. Rubinstein,et al.  Optimization of computer simulation models with rare events , 1997 .

[9]  Donald E. Knuth,et al.  The Stanford GraphBase - a platform for combinatorial computing , 1993 .

[10]  Yang Xiang,et al.  Generalized Simulated Annealing for Global Optimization: The GenSA Package , 2013, R J..

[11]  J. NAGUMOt,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 2006 .

[12]  Dirk P. Kroese,et al.  Chapter 3 – The Cross-Entropy Method for Optimization , 2013 .

[13]  Imbi Traat,et al.  Simulation and the Monte Carlo Method, 2nd Edition by Reuven Y. Rubinstein, Dirk P. Kroese , 2009 .

[14]  Dirk P. Kroese,et al.  Application of the Cross-Entropy Method to the Buffer Allocation Problem in a Simulation-Based Environment , 2005, Ann. Oper. Res..

[15]  下田 吉之,et al.  PSO(Particle Swarm Optimization)手法による最適熱源探索 , 2012 .

[16]  Volker Schmidt,et al.  Inverting Laguerre Tessellations , 2014, Comput. J..

[17]  Shie Mannor,et al.  A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..

[18]  Dirk P. Kroese,et al.  Network Reliability Optimization via the Cross-Entropy Method , 2007, IEEE Transactions on Reliability.

[19]  Jiguo Cao,et al.  Parameter estimation for differential equations: a generalized smoothing approach , 2007 .

[20]  David Ardia,et al.  DEoptim: An R Package for Global Optimization by Differential Evolution , 2009 .

[21]  J. Hammersley.  SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[22]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[23]  A. M. Hilliard.  AFFILIATION , 1910 .

[24]  Marin Kobilarov,et al.  Cross-entropy motion planning , 2012, Int. J. Robotics Res..

[25]  Dirk P. Kroese,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning , 2004 .

[26]  Dirk P. Kroese,et al.  Controlling the number of HIV infectives in a mobile population. , 2008, Mathematical biosciences.

[27]  Ravi Varadhan.  Numerical Optimization in R: Beyond optim , 2014 .

[28]  T. Minka.  Estimating a Dirichlet distribution , 2012 .