Introduction to stochastic search and optimization estimation, simulation, and control (Wiley-Interscience, 2003)
Formerly CIP.
Includes bibliographical references (p. 558-579) and index
"Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems." "The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization."--Jacket
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