James C. Spall

Stochastic Optimization

• Gebundenes Buch

Produktbeschreibung

A strongly interdisciplinary book with potential and actual applications of the material in branches of mathematics, engineering, science, and social sciences, this reference covers a broad range of the most popular stochastic algorithms, including random search, experimental design methods, stochastic approximation, simulated annealing, genetic and evolutionary methods, and machine learning.

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_ Unique in its survey of the range of topics.
_ Contains a strong, interdisciplinary format that will appeal to both students and researchers.
_ Features exercises and web links to software and data sets.

Produktdetails

• Wiley-Interscience Series in Discrete Mathematics and Optimization
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 618
• Erscheinungstermin: 26. März 2003
• Englisch
• Abmessung: 260mm x 183mm x 38mm
• Gewicht: 1330g
• ISBN-13: 9780471330523
• ISBN-10: 0471330523
• Artikelnr.: 11451963

Autorenporträt

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).

Inhaltsangabe

Preface. 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.
References. Frequently Used Notation. Index.

Rezensionen

"This volume deserves a prominent role not only as a textbook, but also as a desk reference for anyone who must cope with noisy data..." ( Computing Reviews.com , January 6, 2006)
"...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)