Approximating global quadratic optimization with convex quadratic constraints

被引：35

作者：

Ye, YY ^{[1
]}

机构：

[1] Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 1999年 / 15卷 / 01期

基金：

美国国家科学基金会;

关键词：

quadratic programming; global optimizer; approximation algorithm;

D O I：

10.1023/A:1008370723217

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We consider the problem of approximating the global maximum of a quadratic program (QP) subject to convex non-homogeneous quadratic constraints. We prove an approximation quality bound that is related to a condition number of the convex feasible set; and it is the currently best for approximating certain problems, such as quadratic optimization over the assignment polytope, according to the best of our knowledge.

引用

页码：1 / 17

页数：17

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