arXiv:1211.5406

The equivalence between doubly nonnegative relaxation and semidefinite relaxation for binary quadratic programming problems

Chuan-Hao Guo, Yanqin Bai, Tang, Li-Ping

Published Nov 23, 2012• 0 citations

Abstract

It has recently been shown (Burer, Math. Program Ser. A 120:479-495, 2009) that a large class of NP-hard nonconvex quadratic programming problems can be modeled as so called completely positive programming problems, which are convex but still NP-hard in general. A basic tractable relaxation is gotten by doubly nonnegative relaxation, resulting in a doubly nonnegative programming. In this paper, we prove that doubly nonnegative relaxation for binary quadratic programming (BQP) problem is equivalent to a tighter semidifinite relaxation for it. When problem (BQP) reduces to max-cut (MC) problem, doubly nonnegative relaxation for it is equivalent to the standard semidifinite relaxation. Furthermore, some compared numerical results are reported.

DOI: 10.48550/arxiv.1211.5406

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