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Optimal Learning Hardcover – April 17, 2012

ISBN-13: 978-0470596692 ISBN-10: 0470596694 Edition: 1st

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Product Details

  • Hardcover: 404 pages
  • Publisher: Wiley; 1 edition (April 17, 2012)
  • Language: English
  • ISBN-10: 0470596694
  • ISBN-13: 978-0470596692
  • Product Dimensions: 6.4 x 1 x 9.5 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #892,916 in Books (See Top 100 in Books)

Editorial Reviews


“He concludes, "This book collects a number of interesting ideas in optimal learning, allows for connections to be made across disciplines, and is a welcome addition to my bookshelf.”  (Informs Journal on Computing, 1 October 2012)


From the Back Cover

Learn the science of collecting information to make effective decisions

Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business.

This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication:

  • Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems

  • Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems

  • Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements

Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB® and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.

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5 of 6 people found the following review helpful By Steve on July 6, 2012
Format: Hardcover Verified Purchase
Traditionally, optimization problems are divided into two categories: deterministic, where the costs are fixed, and stochastic, where the costs are a probability distribution.

In this groundbreaking book, Powell and Ryzhov lucidly explore the situations where the costs are not fully known, but the cost of collecting that information is non-trivial, so the important optimization problem includes how much to invest in getting more data vs. simply using what we already know - the "exploration vs. exploitation" tradeoff.

This book presents excellent terminology and insights into these problems, and should be a good addition to anyone interested in applied optimization. I highly recommend it.

Disclaimer: I have worked with Warren Powell professionally and am also on the advisory board of Princeton's Department of Operations Research and Financial Engineering, where he is a tenured professor.
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0 of 6 people found the following review helpful By Choat Inthawongse on December 19, 2012
Format: Hardcover Verified Purchase
Interesting book to digest,

Including an exciting topics.

This book is well organized and provides ton of online resources for further research
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