W.B. Powell, I.O. Ryzhov, Optimal Learning, John Wiley and Sons, 2012

Optimal Learning addresses the challenge of collecting information, when observations are time consuming and/or expensive. This book grew out of an undergraduate course, and the first twelve chapters are accessible to an advanced undergraduate audience. For more on optimal learning (including table of contents, downloadable software, and a number of downloadable research papers), go to

W.B. Powell, Approximate Dynamic Programming, John Wiley and Sons, 2nd edition, 2011.

This is the first book to bridge the growing field of approximate dynamic programming with operations research. Dynamic programming has often been dismissed because it suffers from “the curse of dimensionality.” In fact, there are three curses of dimensionality when you deal with the high-dimensional problems that typically arise in operations research (the state space, the outcome space and the action space). This book shows how we can estimate value function approximations around the post-decision state variable to produce techniques that allow us to solve dynamic programs which exhibit states with millions of dimensions (approximately).

The book is aimed at an advanced undergraduate/masters level audience with a good course in probability and statistics, and linear programming (for some applications). For the advanced Ph.D., there is an introduction to fundamental proof techniques in “why does it work” sections. The book emphasizes solving real-world problems, and as a result there is considerable emphasis on proper modeling. The book includes dozens of algorithms written at a level that can be directly translated to code. The material in this book is motivated by numerous industrial applications undertaken at CASTLE Lab, as well as a number of undergraduate senior theses.

(for more information, click here for the ADP website)


J. Si, A. Barto, W.B. Powell and D. Wunsch (eds.), Learning and Approximate Dynamic Programming: Scaling up to the Real World, John-Wiley and Sons, New York, 2004.

This edited volume is based on the NSF workshop held in Playacar, Mexico in 2002. It represents the diversity of communities that are using, and contributing to, the general area of approximate dynamic programming.