|Spall, James C.|
Introduction to Stochastic Search and Optimization
Estimation, Simulation, and Control
Wiley-Interscience Series in Discrete Mathematics and Optimization
1. Edition April 2003
2003. 618 Pages, Hardcover
ISBN 978-0-471-33052-3 - John Wiley & Sons
E-Books are also available on all known E-Book shops.
A strongly interdisciplinary book with potential and actual applications of the material in branches of mathematics, engineering, science, and social sciences, this reference covers a broad range of the most popular stochastic algorithms, including random search, experimental design methods, stochastic approximation, simulated annealing, genetic and evolutionary methods, and machine learning.
From the contents
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.
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 Path Methods.
Markov Chain Monte Carlo.
Optimal Design for Experimental Inputs.
Appendix 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.
Answers to Selected Exercises.
Frequently Used Notation.