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On a class of minimax stochastic programs
Alexander Shapiro (ashapiro Abstract: For a particular class of minimax stochastic programming models, we show that the problem can be equivalently reformulated into a standard stochastic programming problem. This permits the direct use of standard decomposition and sampling methods developed for stochastic programming. We also show that this class of minimax stochastic programs subsumes a large family of mean-risk stochastic programs where risk is measured in terms of deviations from a quantile. Keywords: worst case distribution, problem of moments, Lagrangian duality, mean risk stochastic programs, deviation from a quantile Category 1: Stochastic Programming Category 2: Robust Optimization Category 3: Convex and Nonsmooth Optimization Citation: Technical report, School of Industrial & Systems Engineering, Georgia Institute of Technology Download: [PDF] Entry Submitted: 08/12/2003 Modify/Update this entry | ||
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