Introduction to Stochastic Search and Optimization (Hardcover)

by James C. Spall (Author) "Preparation is required before starting any journey..." (more)
Key Phrases: noisy loss measurements, loss function measurements, injected randomness, Monte Carlo, Easy Street, Batch Model (more...)

Editorial Reviews

Review
"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)

Review
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

See all Editorial Reviews

Product Details

• Hardcover: 618 pages
• Publisher: Wiley-Interscience; 1st edition (March 2003)
• Language: English
• ISBN-10: 0471330523
• ISBN-13: 978-0471330523
• Product Dimensions: 10.1 x 7.1 x 1.6 inches
• Shipping Weight: 2.8 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #472,234 in Books (See Bestsellers in Books)

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  #68 in	Books > Science > Mathematics > Applied > Linear Programming
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  #75 in	Books > Professional & Technical > Professional Science > Mathematics > Applied > Stochastic Modeling

Customer Reviews

Most Helpful Customer Reviews

19 of 21 people found the following review helpful:

5.0 out of 5 stars Recommended to scholars and graduate students, September 23, 2003

By A Customer

Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas.

1 of 1 people found the following review helpful:

5.0 out of 5 stars Great intro to optimization from stochastic perspective, May 15, 2009

By	Keiichi Ito (Japan) - See all my reviews

I stumbled upon this book searching for a Genetic Algorithm book. The coverage of topics are unique and very interesting. This is the first book I came across that treats both the evolutionary algorithms (GA) and the stochastic search methods. Recursive Linear Estimator (e.g. Kalman Filter), Markov Chain Monte Carlo (e.g. Metropolis-Hastings, Gibbs), and Reinforcement Learning, are some of the stochastic material discussed. Continuous and discrete parameters are treated as well as noisy data, but not so much on constrained optimization.

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.

4 of 9 people found the following review helpful:

5.0 out of 5 stars Great book!!!, December 6, 2004

By	Wagner F. Sacco (Atlanta, GA) - See all my reviews (REAL NAME)

A must have for anyone interested in otimization! Extremely well written and objective.