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AI, OR and Control Theory: A Rosetta Stone for Stochastic Optimization

Stochastic optimization arises in a wide range of problems, and as a result the ideas have been developed in different communities, creating fragmented styles in terms of notation, modeling and algorithms. Some of these variations can be explained by differences in application, as well as differences in research styles. We attempt to bridge these communities by describing how to translate notational systems, while contrasting modeling and algorithmic strategies. We also touch on differences in research styles which reflect expectations for a publishable contribution as well as the style of publication outlets used by different communities. This version of the paper has been written largely without references (more references may be added later). At this point, I think it is fair to say that all of the concepts contained in this paper have been expressed somewhere in the literature, or are at least known among groups of researchers. This article synthesizes these ideas in a coherent way, and while multiple notational systems are reviewed, there is also an attempt to develop some common notational principles that will help to foster communication across communities. This article is intended as a discussion piece and is not intended for journal publication. The goal is to help foster communication between the different communities. Constructive thoughts and comments are warmly appreciated. Since this document will evolve over time, please email your comments to powell@princeton.edu. You may put notes directly on the pdf (this is best), but if you put them in email, please include the date on the front page so I know which version you are using.