Reuven Y. Rubinstein, Dirk P. Kroese.
The Cross-Entropy Method
A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning.
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)
generate a random data sample (trajectories, vectors, etc.) according to a
specified mechanism; (b) update the parameters of the random mechanism based
on this data in order to produce a "better" sample in the next iteration. The
simplicity and versatility of the method is illustrated via a diverse
collection of optimization and estimation problems. The book is aimed at a
broad audience of engineers, computer scientists, mathematicians,
statisticians and in general anyone, theorist or practitioner, who is
interested in fast simulation, including rare-event probability estimation,
efficient combinatorial and continuous multi-extremal optimization, and
machine learning algorithms.
Reuven Y. Rubinstein is the Milford Bohm Professor of Management at the Faculty of Industrial Engineering and Management at the Technion (Israel Institute of Technology).