Convex approximations in stochastic programming by semidefinite programming

被引：0
|
作者：
István Deák
Imre Pólik
András Prékopa
Tamás Terlaky
机构：
[1] Corvinus University of Budapest,Department of Computer Science
[2] SAS Institute,Operations Research R&D
[3] Eötvös University,Department of Operations Research
[4] Lehigh University,Department of Industrial & Systems Engineering
来源：
Annals of Operations Research | 2012年 / 200卷
关键词：
Convex approximation; Stochastic optimization; Successive regression approximations; Semidefinite optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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 L1, L∞ and L2 norm. Extensive numerical experiments to investigate the behavior of the proposed methods are also performed.
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页码：171 / 182
页数：11
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