, Volume 89, Issue 3, pp 341-357

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

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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.

Received: February 2000 / Accepted: November 2000¶Published online January 17, 2001