Framework for modeling optimization problems that involve uncertainty
For the context of control theory, see Stochastic control.
In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions.[1][2] This framework contrasts with deterministic optimization, in which all problem parameters are assumed to be known exactly. The goal of stochastic programming is to find a decision which both optimizes some criteria chosen by the decision maker, and appropriately accounts for the uncertainty of the problem parameters. Because many real-world decisions involve uncertainty, stochastic programming has found applications in a broad range of areas ranging from finance to transportation to energy optimization.[3][4]
^Shapiro, Alexander; Dentcheva, Darinka; Ruszczyński, Andrzej (2009). Lectures on stochastic programming: Modeling and theory(PDF). MPS/SIAM Series on Optimization. Vol. 9. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM). pp. xvi+436. ISBN 978-0-89871-687-0. MR 2562798. Archived from the original (PDF) on 2020-03-24. Retrieved 2010-09-22. {{cite book}}: Unknown parameter |agency= ignored (help)
^Birge, John R.; Louveaux, François (2011). Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering. doi:10.1007/978-1-4614-0237-4. ISBN 978-1-4614-0236-7. ISSN 1431-8598.
^
Stein W. Wallace and William T. Ziemba (eds.). Applications of Stochastic Programming. MPS-SIAM Book Series on Optimization 5, 2005.
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Applications of stochastic programming are described at the following website, Stochastic Programming Community.
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