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Stochastic Programming


The Stochastic Programming Society, here. Going to do some of the setup for Princeton Bank Consortium here. Pink I will stick with sorting through references and PBC will focus on the NIM Optimization numbers and commentary.

Stochastic programming is a framework for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters. When the parameters are known only within certain bounds, one approach to tackling such problems is called robust optimization. Here the goal is to find a solution which is feasible for all such data and optimal in some sense. Stochastic programming models are similar in style but take advantage of the fact that probability distributions governing the data are known or can be estimated. The goal here is to find some policy that is feasible for all (or almost all) the possible data instances and maximizes the expectation of some function of the decisions and the random variables. More generally, such models are formulated, solved analytically or numerically, and analyzed in order to provide useful information to a decision-maker.

To find out more about stochastic programming a good place to start is A Tutorial on Stochastic Programming by Alexander Shapiro and Andy Philpott. This tutorial is aimed at readers with some acquaintance with optimization and probability theory; for example graduate students in operations research, or academics/ practitioners from a different field of operations research.

The older Stochastic Programming Introduction by Andy  Philpott is aimed at readers with a less formal background in operations research, for example managers in industry who want to know more about what stochastic programming might offer them without delving too deeply into details.

In addition, tutorials on current research areas are being developed. The main idea is that those who’ve read the introduction and want to find out more about specific topics will have a good place to start. COSP will be inviting experts to write pages for each of these areas. This collection of introductions is edited by David Morton, Andy Philpott, and Maarten van der Vlerk.

Georgia Tech crew, here  Shapiro and Nemirovski

Rutgers, here. Ruszczynski, here.

Shapiro and Philpott, 2007, A Tutorial on Stochastic Programming, here. OK bibliography at the conclusion of the tutorial.

Powell, Warren B., Computational Stochastic Programming, here.  See also Castle Labs, here. Princeton OR.

van der Vlerk, Stochastic Programming Bibliography, here.

neos Guide, here. Wisconson folks

Pioneers of Stochastic Programming, here.  Prekopa, Rockafellar, Ziemba, Danzig links.

Stochastic Programming Links, here. old 2002 lots of dead links


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