A Novel SDP Relaxation for the Quadratic Assignment Problem Using Cut Pseudo Bases

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9849)

Abstract

The quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems. Its range of applications is wide, including facility location, keyboard layout, and various other domains. The key success factor of specialized branch-and-bound frameworks for minimizing QAPs is an efficient implementation of a strong lower bound. In this paper, we propose a lower-bound-preserving transformation of a QAP to a different quadratic problem that allows for small and efficiently solvable SDP relaxations. This transformation is self-tightening in a branch-and-bound process.

Keywords

Quadratic assignment Semidefinite program Lower bound Branch and bound 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany

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