Spall Introduction to Stochastic Search and Optimization

Estimation, Simulation, and Control
1. Auflage 2005
ISBN: 978-0-471-44190-8
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 618 Seiten, E-Book

Reihe: Wiley-Interscience Series in Discrete Mathematics and Optimization

A unique interdisciplinary foundation for real-world problemsolving
Stochastic search and optimization techniques are used in a vastnumber of areas, including aerospace, medicine, transportation, andfinance, to name but a few. Whether the goal is refining the designof a missile or aircraft, determining the effectiveness of a newdrug, developing the most efficient timing strategies for trafficsignals, or making investment decisions in order to increaseprofits, stochastic algorithms can help researchers andpractitioners devise optimal solutions to countless real-worldproblems.
Introduction to Stochastic Search and Optimization: Estimation,Simulation, and Control is a graduate-level introduction to theprinciples, algorithms, and practical aspects of stochasticoptimization, including applications drawn from engineering,statistics, and computer science. The treatment is both rigorousand broadly accessible, distinguishing this text from much of thecurrent literature and providing students, researchers, andpractitioners with a strong foundation for the often-daunting taskof solving real-world problems.
The text covers a broad range of today's most widely usedstochastic algorithms, including:
* Random search
* Recursive linear estimation
* Stochastic approximation
* Simulated annealing
* Genetic and evolutionary methods
* Machine (reinforcement) learning
* Model selection
* Simulation-based optimization
* Markov chain Monte Carlo
* Optimal experimental design
The book includes over 130 examples, Web links to software anddata sets, more than 250 exercises for the reader, and an extensivelist of references. These features help make the text an invaluableresource for those interested in the theory or practice ofstochastic search and optimization.

Autoren/Hrsg.

Spall, James C.

Inhaltsverzeichnis

Preface.
Stochastic Search and Optimization: Motivation and SupportingResults.
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 RandomNumbers, and Selection Methods.
Simulation-Based Optimization II: Stochastic Gradient and SamplePath 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.

Über Autor(innen)

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).