A CONSTRAINED OPTIMIZATION PROBLEM UNDER UNCERTAINTY

被引：0

作者：

Quaeghebeur, Erik ^{[1
]}

Shariatmadar, Keivan ^{[1
]}

De Cooman, Gert ^{[1
]}

机构：

[1] Univ Ghent, EESA Dept, SYST Res Grp, B-9052 Zwijnaarde, Belgium

来源：

COMPUTATIONAL INTELLIGENCE: FOUNDATIONS AND APPLICATIONS: PROCEEDINGS OF THE 9TH INTERNATIONAL FLINS CONFERENCE | 2010年 / 4卷

关键词：

constrained optimization; maximinity; maximality; linear prevision; vacuous prevision; possibility distribution;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We investigate a constrained optimization problem for which there is uncertainty about a constraint parameter. Our aim is to reformulate it as a (constrained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models-linear and vacuous previsions, and possibility distributions-and for two different optimality criteria for decision problems under uncertainty-maximinity and maximality.

引用

页码：791 / 796

页数：6

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