Convex approximations in stochastic programming by semidefinite programming

István Deák (), Imre Pólik, András Prékopa and Tamás Terlaky

Annals of Operations Research, 2012, vol. 200, issue 1, 182 pages

Abstract: The following question arises in stochastic programming: how can one approximate a noisy convex function with a convex quadratic function that is optimal in some sense. Using several approaches for constructing convex approximations we present some optimization models yielding convex quadratic regressions that are optimal approximations in L 1 , L ∞ and L 2 norm. Extensive numerical experiments to investigate the behavior of the proposed methods are also performed. Copyright Springer Science+Business Media, LLC 2012

Keywords: Convex approximation; Stochastic optimization; Successive regression approximations; Semidefinite optimization (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-011-0986-0

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