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

John Wiley, 2003. — 616 p.Introduction to Stochastic Search and Optimization provides a broad survey many of the most important methods in stochastic search and optimization. These include random search, recursive least squares, stochastic approximation, simulated annealing, evolutionary computation (including genetic algorithms), and reinforcement learning. Also included is a discussion of closely related subjects such as mUltiple statistical comparisons, model selection, simulation-based optimization, Markov chain Monte Carlo, and experimental design. These subjects are covered in 17 chapters. Each chapter ends with some concluding remarks, including comments on the historical perspective and on the nexus with other topics in the book. Five appendices review essential background information in multivariate analysis, matrix theory, statistical testing, probability theory, pseudorandom number generation, and Markov chains. All chapters and appendices include exercises. The book concludes with an extensive list of references.Stochastic Search and Optimization: Motivation and Supporting Results
Direct Methods for Stochastic Search
Recursive Estimation for Linear Models
Stochastic Approximation for Nonlinear Root-Finding
Stochastic Gradient Form of Stochastic Approximation
Stochastic Approximation and the Finite-Difference Method
Simultaneous Perturbation Stochastic Approximation
Annealing-Type Algorithms
Evolutionary Computation I: Genetic Algorithms
Evolutionary Computation II: General Methods and Theory
Reinforcement Learning via Temporal Differences
Statistical Methods for Optimization in Discrete Problems
Model Selection and Statistical Information
Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods
Simulation-Based Optimization II: Stochastic Gradient and Sample
Markov Chain Monte Carlo
Optimal Design for Experimental InputsAppendix A: Selected Results from Multivariate Analysis
Appendix B: Some Basic Tests in Statistics
Appendix C: Probability Theory and Convergence
Appendix D: Random Number Generation
Appendix E: Markov Processes