Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control
|Author||:||James C. Spall|
|Remarks||:||Exclusively distributed by Ane Books|
Spall presents an introductory, comparative text on popular stochastic algorithms, which will appeal to both students and researchers in the field. The author is an award winning applied researcher at Johns Hopkins, and is an Associate Editor for the IEEE Transactions on Automatic Control, and a Contributing Editor for the Current Index to Statistics.
· 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.
Frequently Used Notation.