Stochastic semidefinite programming: A new paradigm for stochastic optimization

被引：0

作者：

Ariyawansa K.A. ^{[1
]}

Zhu Y. ^{[1
]}

机构：

[1] Department of Mathematics, Washington State University, Pullman

来源：

4OR | 2006年 / 4卷 / 3期

关键词：

Linear programming; Recourse; Semidefinite programming; Stochastic programming;

D O I：

10.1007/s10288-006-0016-2

中图分类号：

学科分类号：

摘要：

Semidefinite programs are a class of optimization problems that have been studied extensively during the past 15 years. Semidefinite programs are naturally related to linear programs, and both are defined using deterministic data. Stochastic programs were introduced in the 1950s as a paradigm for dealing with uncertainty in data defining linear programs. In this paper, we introduce stochastic semidefinite programs as a paradigm for dealing with uncertainty in data defining semidefinite programs.

引用

页码：65 / 79

页数：14

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