- Hardcover: 618 pages
- Publisher: Wiley-Interscience; 1 edition (March 2003)
- Language: English
- ISBN-10: 0471330523
- ISBN-13: 978-0471330523
- Product Dimensions: 7.3 x 1.4 x 10.2 inches
- Shipping Weight: 2.8 pounds (View shipping rates and policies)
- Average Customer Review: 5 customer reviews
- Amazon Best Sellers Rank: #1,072,187 in Books (See Top 100 in Books)
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Introduction to Stochastic Search and Optimization 1st Edition
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"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)
This book should be on the desk of anyone interested in the theory and application of stochastic search and optimization.
--Kevin Passino, Department of Electrical Engineering, The Ohio State University
Top customer reviews
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I heartily applaud this book. It was, for me, one of those which changed the way I looked at things overall.
The algorithms presented are very practical and theoretically well founded. When I learned about SPSA, I was most impressed to find out that it is possible to estimate the gradient by just two objective function calls (instead of finite differencing every dimension of the parameter vector to be optimized), and this is regardless of the number of dimensions of the parameter vector!
The book is aimed at rather general audiences in science and engineering. Rigorous mathematical details are avoided.