Approximate Dynamic Programming I: Modeling

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 that 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 is particularly problematic in the general area of multistage, stochastic optimization problems, where different communities have made significant algorithmic contributions, which have applications to a wide variety of problems. The range of problems that can be modeled as stochastic, dynamic optimization problems is vast. Examples of major problem classes include: