## Approximate Dynamic Programming- I: Modeling

Citations: | 1 - 1 self |

### BibTeX

@MISC{Powell_approximatedynamic,

author = {Warren B. Powell},

title = {Approximate Dynamic Programming- I: Modeling},

year = {}

}

### OpenURL

### Abstract

The first step in solving a stochastic optimization problem is providing a mathematical model. How the problem is modeled can impact the solution strategy. In this chapter, we provide a flexible modeling framework that uses a classic control-theoretic framework, avoiding devices such as onestep transition matrices. We describe the five fundamental elements of any stochastic, dynamic program. Different notational conventions are introduced, and the types of policies that can be used to guide decisions are described in detail. This discussion puts approximate dynamic programming in the context of a variety of other algorithmic strategies by using the modeling framework to describe Stochastic optimization problems pose unique challenges in how they are represented mathematically. These problems arise in a number of different communities, often in the context of problems which introduce specific computational characteristics. As a result, a number of contrasting notational styles have evolved which complicate our ability to communicate research across communities. This