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On improving convex quadratic programming relaxation for the quadratic assignment problem

Author

Listed:
  • Yong Xia

    (Beihang University)

  • Wajeb Gharibi

    (Jazan University)

Abstract

Relaxation techniques play a great role in solving the quadratic assignment problem, among which the convex quadratic programming bound (QPB) is competitive with existing bounds in the trade-off between cost and quality. In this article, we propose two new lower bounds based on QPB. The first dominates QPB at a high computational cost, which is shown equivalent to the recent second-order cone programming bound. The second is strictly tighter than QPB in most cases, while it is solved as easily as QPB.

Suggested Citation

  • Yong Xia & Wajeb Gharibi, 2015. "On improving convex quadratic programming relaxation for the quadratic assignment problem," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 647-667, October.
    • Handle: RePEc:spr:jcomop:v:30:y:2015:i:3:d:10.1007_s10878-013-9655-3
    DOI: 10.1007/s10878-013-9655-3
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    References listed on IDEAS

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    1. Loiola, Eliane Maria & de Abreu, Nair Maria Maia & Boaventura-Netto, Paulo Oswaldo & Hahn, Peter & Querido, Tania, 2007. "A survey for the quadratic assignment problem," European Journal of Operational Research, Elsevier, vol. 176(2), pages 657-690, January.
    2. S. W. Hadley & F. Rendl & H. Wolkowicz, 1992. "A New Lower Bound Via Projection for the Quadratic Assignment Problem," Mathematics of Operations Research, INFORMS, vol. 17(3), pages 727-739, August.
    3. Qing Zhao & Stefan E. Karisch & Franz Rendl & Henry Wolkowicz, 1998. "Semidefinite Programming Relaxations for the Quadratic Assignment Problem," Journal of Combinatorial Optimization, Springer, vol. 2(1), pages 71-109, March.
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