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Shapiro, Dentcheva, and Ruszdzynski look at optimization problems involving uncertain parameters for which stochastic models are available, Stochastic models have proven to be the best approach, they say, mainly because of their solid mathematical foundations and the theoretical richness of the theory of probability and stochastic processes, but also because of the sound statistical technique of using real data. They cover stochastic programming models, two-stage problems, multi-stage problems, optimization models with probabilistic constraints, statistical inference, risk averse optimization, and background material. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)
Number of Pages: 494
Publisher: Cambridge Univ Pr
Author: Alexander Shapiro & Darinka Dentcheva & Andrzej Ruszczynski
Street Date: September 25, 2018
Item Number (DPCI): 248-17-0090
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