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The Cross-Entropy Method : a Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning / by Reuven Y. Rubinstein, Dirk P. Kroese.

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The cross-entropy (CE) method is one of the most significant developments in stochastic optimization and simulation in recent years. This book explains in detail how and why the CE method works. The CE method involves an iterative procedure where each iteration can be broken down into two phases: (a...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Author: Rubinstein, Reuven Y.
Other Authors: Kroese, Dirk P.
Format: eBook
Language:English
Published: New York, NY : Springer New York, 2004.
Series:Information science and statistics.
Subjects:
Computer science.
Computer simulation.
Engineering.
Operations research.
Table of Contents:
  • 1 Preliminaries
  • 2 A Tutorial Introduction to the Cross-Entropy Method
  • 3 Efficient Simulation via Cross-Entropy
  • 4 Combinatorial Optimization via Cross-Entropy
  • 5 Continuous Optimization and Modifications
  • 6 Noisy Optimization with CE
  • 7 Applications of CE to COPs
  • 8 Applications of CE to Machine Learning
  • A Example Programs
  • A.1 Rare Event Simulation
  • A.2 The Max-Cut Problem
  • A.3 Continuous Optimization via the Normal Distribution
  • A.4 FACE
  • A.5 Rosenbrock
  • A.6 Beta Updating
  • A.7 Banana Data
  • References.

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