Introduction to Stochastic Search and Optimization

JAMES C. SPALL is a member of the Principal Professional Staff at the Johns Hopkins University, Applied Physics Laboratory, and is the Chair of the Applied and Computational Mathematics Program within the Johns Hopkins School of Engineering. Dr. Spall has published extensively in the areas of control and statistics and holds two U.S. patents. Among other appointments, he is Associate Editor at Large for the IEEE Transactions on Automatic Control and Contributing Editor for the Current Index to Statistics. Dr. Spall has received numerous research and publications awards and is an elected Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

"This volume deserves a prominent role not only as a textbook, but also as a desk reference for anyone who must cope with noisy data…" (Computing Reviews.com, January 6, 2006) "...well written and accessible to a wide audience...a welcome addition to the control and optimization community." (IEEE Control Systems Magazine, June 2005)"…a step toward learning more about optimization techniques that often are not part of a statistician's training." (Journal of the American Statistical Association, December 2004)“…provides easy access to a very broad, but related, collection of topics…” (Short Book Reviews, August 2004)"Rather than simply present various stochastic search and optimization algorithms as a collection of distinct techniques, the book compares and contrasts the algorithms within a broader context of stochastic methods." (Technometrics, August 2004, Vol. 46, No. 3)

• Preface. 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 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. References. Frequently Used Notation. Index.