Mathematical Programming

, Volume 89, Issue 3, pp 341–357

A new bound for the quadratic assignment problem based on convex quadratic programming

  • Kurt M. Anstreicher
  • Nathan W. Brixius

DOI: 10.1007/PL00011402

Cite this article as:
Anstreicher, K. & Brixius, N. Math. Program. (2001) 89: 341. doi:10.1007/PL00011402

Abstract.

We describe a new convex quadratic programming bound for the quadratic assignment problem (QAP). The construction of the bound uses a semidefinite programming representation of a basic eigenvalue bound for QAP. The new bound dominates the well-known projected eigenvalue bound, and appears to be competitive with existing bounds in the trade-off between bound quality and computational effort.

Key words: quadratic assignment problem – eigenvalue bounds – quadratic programming – semidefinite programming
Mathematics Subject Classification (1991): 90C27, 90C09, 90C20

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Kurt M. Anstreicher
    • 1
  • Nathan W. Brixius
    • 2
  1. 1.Department of Management Sciences, University of Iowa, Iowa City, IA 52242, e-mail: kurt-anstreicher@uiowa.eduUS
  2. 2.Department of Computer Science, University of Iowa, Iowa City, IA 52242, e-mail: brixius@cs.uiowa.eduUS