Stochastic Optimization Book

	Preface
1	Stochastic Search and Optimization: Motivation and Supporting Results	1
2	Direct Methods for Stochastic Search	34
3	Recursive Estimation for Linear Models	65
4	Stochastic Approximation for Nonlinear Root-Finding	95
5	Stochastic Gradient Form of Stochastic Approximation	126
6	Stochastic Approximation and the Finite-Difference Method	150
7	Simultaneous Perturbation Stochastic Approximation	176
8	Annealing-Type Algorithms	208
9	Evolutionary Computation I: Genetic Algorithms	231
10	Evolutionary Computation II: General Methods and Theory	259
11	Reinforcement Learning via Temporal Differences	278
12	Statistical Methods for Optimization in Discrete Problems	300
13	Model Selection and Statistical Information	329
14	Simulation-Based Optimization I: Regeneration, Common Random Numbers, and Selection Methods	367
15	Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods	409
16	Markov Chain Monte Carlo	436
17	Optimal Design for Experimental Inputs	464
App. A	Selected Results from Multivariate Analysis	505
App. B	Some Basic Tests in Statistics	515
App. C	Probability Theory and Convergence	526
App. D	Random Number Generation	538
App. E	Markov Processes	547
	Answers to Selected Exercises	552
	References	558
	Frequently Used Notation	580
	Index	583