A Restricted Dual Peaceman-Rachford Splitting Method for a Strengthened DNN Relaxation for QAP

被引:6
|
作者
Graham, Naomi [1 ]
Hu, Hao [2 ]
Im, Jiyoung [3 ]
Li, Xinxin [4 ]
Wolkowicz, Henry [3 ]
机构
[1] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1Z4, Canada
[2] Clemson Univ, Sch Math & Stat Sci, Clemson, SC 29634 USA
[3] Univ Waterloo, Dept Combinator & Optimizat, Waterloo, ON N2L 3G1, Canada
[4] Jilin Univ, Sch Math, Changchun 130012, Jilin, Peoples R China
基金
中国国家自然科学基金;
关键词
quadratic assignment problem; semidefinite relaxation; doubly nonnegative relaxation; facial reduction; Peaceman-Rachford splitting method; ALTERNATING DIRECTION METHOD; QUADRATIC ASSIGNMENT PROBLEM; LOCAL LINEAR CONVERGENCE; MULTIPLIERS; ALGORITHMS; LAYOUT; ADMM;
D O I
10.1287/ijoc.2022.1161
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Splitting methods in optimization arise when one can divide an optimization problem into two or more simpler subproblems. They have proven particularly successful for relaxations of problems involving discrete variables. We revisit and strengthen splitting methods for solving doubly nonnegative relaxations of the particularly difficult, NP-hard quadratic assignment problem. We use a modified restricted contractive splitting method approach. In particular, we show how to exploit redundant constraints in the subproblems. Our strengthened bounds exploit these new subproblems and new dual multiplier estimates to improve on the bounds and convergence results in the literature.
引用
收藏
页码:2125 / 2143
页数:19
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