Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
  • Android
  • Windows Phone
  • Android

To get the free app, enter your mobile phone number.

Introduction to Stochastic Search and Optimization 1st Edition

5.0 out of 5 stars 5 customer reviews
ISBN-13: 978-0471330523
ISBN-10: 0471330523
Why is ISBN important?
ISBN
This bar-code number lets you verify that you're getting exactly the right version or edition of a book. The 13-digit and 10-digit formats both work.
Scan an ISBN with your phone
Use the Amazon App to scan ISBNs and compare prices.
Trade in your item
Get a $3.91
Gift Card.
Have one to sell? Sell on Amazon
Buy used On clicking this link, a new layer will be open
$109.30 On clicking this link, a new layer will be open
Buy new On clicking this link, a new layer will be open
$148.49 On clicking this link, a new layer will be open
More Buying Choices
27 New from $125.89 24 Used from $109.30
Free Two-Day Shipping for College Students with Prime Student Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student


Wiley Summer Savings Event.
Wiley Summer Savings Event.
Save up to 40% during Wiley's Summer Savings Event. Learn more.
$148.49 FREE Shipping. Only 10 left in stock (more on the way). Ships from and sold by Amazon.com. Gift-wrap available.
click to open popover

Frequently Bought Together

  • Introduction to Stochastic Search and Optimization
  • +
  • Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
Total price: $217.67
Buy the selected items together

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
NO_CONTENT_IN_FEATURE

The latest book club pick from Oprah
"The Underground Railroad" by Colson Whitehead is a magnificent novel chronicling a young slave's adventures as she makes a desperate bid for freedom in the antebellum South. See more

Product Details

  • 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: 5.0 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #1,450,601 in Books (See Top 100 in Books)

Customer Reviews

5 star
100%
4 star
0%
3 star
0%
2 star
0%
1 star
0%
See all 5 customer reviews
Share your thoughts with other customers

Top Customer Reviews

Format: Hardcover
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.
Comment 21 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover Verified Purchase
Stochastic optimization seems to be a "dark corner" for the fields of optimization and of Monte Carlo methods. Spall brings a quantitative engineering perspective to the problems, yet gives theory its proper dues. Moreover, he weaves a consistent interpretation among these algorithms which deserve greater attention and focus. While he clearly is a practitioner, his original work in algorithmic stochastic search and that of those he's inspired will, in my opinion, enable new theory to arise, whereby problems with horrible violations of continuity and the like will be embedded in some kind of mathematical manifold, and notions of stochastic gradients will be seen as exploring these.

I heartily applaud this book. It was, for me, one of those which changed the way I looked at things overall.
Comment 2 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover
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.
Comment 3 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover Verified Purchase
Very clearly written, and good coverage of subject by a practicing expert in the field (I may be biased because I am about to take the course he teaches).
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse
Format: Hardcover
A must have for anyone interested in otimization! Extremely well written and objective.
Comment 4 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback.
Sorry, we failed to record your vote. Please try again
Report abuse

Set up an Amazon Giveaway

Introduction to Stochastic Search and Optimization
Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more about Amazon Giveaway
This item: Introduction to Stochastic Search and Optimization