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Amitabh Basu (basu.amitabhjhu.edu) Abstract: Finitedimensional linear programs satisfy strong duality (SD) and have the ``dual pricing" (DP) property. The (DP) property ensures that, given a sufficiently small perturbation of the righthandside vector, there exists a dual solution that correctly ``prices" the perturbation by computing the exact change in the optimal objective function value. These properties may fail in semiinfinite linear programming where the constraint vector space is infinite dimensional. Unlike the finitedimensional case, in semiinfinite linear programs the constraint vector space is a modeling choice. We show that, for a sufficiently restricted vector space, both (SD) and (DP) always hold, at the cost of restricting the perturbations to that space. The main goal of the paper is to extend this restricted space to the largest possible constraint space where (SD) and (DP) hold. Once (SD) or (DP) fail for a given constraint space, then these conditions fail for all larger constraint spaces. We give sufficient conditions for when (SD) and (DP) hold in an extended constraint space. Our results require the use of linear functionals that are singular or purely finitely additive and thus not representable as finite support vectors. The key to understanding these linear functionals is the extension of the FourierMotzkin elimination procedure to semiinfinite linear programs. Keywords: semiinfinite linear programming, duality, sensitivity analysis Category 1: Infinite Dimensional Optimization (Semiinfinite Programming ) Citation: Download: [PDF] Entry Submitted: 10/26/2015 Modify/Update this entry  
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