The Linear Programming Approach to Approximate Dynamic Programming

This chapter addresses the issue of the ?>curse of dimensionality?> by treating ADP as the ?>dual?> of the linear programming problem and introduces the concept of approximate linear programming (ALP). It provides a brief introduction to the use of Markov Decision Process models. For a more comprehensive study of MDP models, and the techniques that can be used with them, read Chapters 11 and 12. This chapter discusses the performance of approximate LP policies, approximation error bounds, and provides an application to queueing networks. Another queueing network example can be found in Chapter 12. The chapter finishes with an efficient constraint sampling scheme.