Spall J.C. Introduction to Stochastic Search and Optimization. Estimation, Simulation and Control

New York : Chichester : Brisbane : Toronto : Singapore: John Wiley & Sons, 2003, 595 p.Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems.The text covers a broad range of today's most widely used stochastic algorithms, including:Random search
Recursive linear estimation
Stochastic approximation
Simulated annealing
Genetic and evolutionary methods
Machine (reinforcement) learning
Model selection
Simulation-based optimization
Markov chain Monte Carlo
Optimal experimental design
The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization.