Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
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.
"...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)