Approximate Dynamic Programming

前两天收到孟同学的邮件，像我索要一本名为approximate dynamic programming的书籍。由于这两天一直在忙于博客网站域名转换的工作，脑袋大得很，实在没有抽出时间来查找，很幸运的是，虽然长河以往在博客中并没有发表过这个资源，但我的确是有这本书滴~

http://www.amazon.com/Approximate-Dynamic-Programming-Dimensionality-Probability/dp/0470171553，英文摘要见后文所示。

这是一本很不错的书，适合于有志于研习数值计算的朋友们，起码得到美国网友一致好评的资料，我印象里是不多的。要不是孟同学的求助，我差点就把它丢弃到某个不知名的文件夹里了，看来从某种角度讲，电子书多了也很累人啊

基本信息

Publisher:   Wiley-Interscience
Number Of Pages:   488
Publication Date:   2007-09-26
ISBN-10 / ASIN:   0470171553
ISBN-13 / EAN:   9780470171554
Binding:   Hardcover
 

英文摘要：

A complete and accessible introduction to the real-world applications of approximate dynamic programming

With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines-Markov design processes, mathematical programming, simulation, and statistics-to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems.

Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues.

With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming:
Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects
Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics
Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms
Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book

Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

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