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.
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Received: February 2000 / Accepted: November 2000¶Published online January 17, 2001
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Anstreicher, K., Brixius, N. A new bound for the quadratic assignment problem based on convex quadratic programming. Math. Program. 89, 341–357 (2001). https://doi.org/10.1007/PL00011402
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DOI: https://doi.org/10.1007/PL00011402