Global optimization of a class of nonconvex quadratically constrained quadratic programming problems

被引：2
作者：
Xia, Yong [1 ,2 ]
机构：
[1] Minist Educ, LMIB, Beijing 100191, Peoples R China
[2] Beihang Univ, Sch Math & Syst Sci, Beijing 100191, Peoples R China
来源：
ACTA MATHEMATICA SINICA-ENGLISH SERIES | 2011年 / 27卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Nonconvex programming; quadratically constrained quadratic programming; quadratic assignment problem; polynomial solvability; strong duality; RELAXATION;
D O I：
10.1007/s10114-011-8351-4
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper we study a class of nonconvex quadratically constrained quadratic programming problems generalized from relaxations of quadratic assignment problems. We show that each problem is polynomially solved. Strong duality holds if a redundant constraint is introduced. As an application, a new lower bound is proposed for the quadratic assignment problem.
引用
收藏
页码：1803 / 1812
页数：10
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