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Eigenvalue, quadratic programming, and semidefinite programming relaxations for a cut minimization problem

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Listed:
  • Ting Pong
  • Hao Sun
  • Ningchuan Wang
  • Henry Wolkowicz

Abstract

We consider the problem of partitioning the node set of a graph into k sets of given sizes in order to minimize the cut obtained using (removing) the kth set. If the resulting cut has value 0, then we have obtained a vertex separator. This problem is closely related to the graph partitioning problem. In fact, the model we use is the same as that for the graph partitioning problem except for a different quadratic objective function. We look at known and new bounds obtained from various relaxations for this NP-hard problem. This includes: the standard eigenvalue bound, projected eigenvalue bounds using both the adjacency matrix and the Laplacian, quadratic programming (QP) bounds based on recent successful QP bounds for the quadratic assignment problems, and semidefinite programming bounds. We include numerical tests for large and huge problems that illustrate the efficiency of the bounds in terms of strength and time. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Ting Pong & Hao Sun & Ningchuan Wang & Henry Wolkowicz, 2016. "Eigenvalue, quadratic programming, and semidefinite programming relaxations for a cut minimization problem," Computational Optimization and Applications, Springer, vol. 63(2), pages 333-364, March.
    • Handle: RePEc:spr:coopap:v:63:y:2016:i:2:p:333-364
    DOI: 10.1007/s10589-015-9779-8
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    References listed on IDEAS

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    1. Marti, Rafael & Campos, Vicente & Pinana, Estefania, 2008. "A branch and bound algorithm for the matrix bandwidth minimization," European Journal of Operational Research, Elsevier, vol. 186(2), pages 513-528, April.
    2. 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.
    3. Éva Tardos, 1986. "A Strongly Polynomial Algorithm to Solve Combinatorial Linear Programs," Operations Research, INFORMS, vol. 34(2), pages 250-256, April.
    4. 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.
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    Cited by:

    1. Hu, Hao & Sotirov, Renata & Wolkowicz, Henry, 2023. "Facial reduction for symmetry reduced semidefinite and doubly nonnegative programs," Other publications TiSEM 8dd3dbae-58fd-4238-b786-e, Tilburg University, School of Economics and Management.
    2. Fanz Rendl & Renata Sotirov, 2018. "The min-cut and vertex separator problem," Computational Optimization and Applications, Springer, vol. 69(1), pages 159-187, January.
    3. Xinxin Li & Ting Kei Pong & Hao Sun & Henry Wolkowicz, 2021. "A strictly contractive Peaceman-Rachford splitting method for the doubly nonnegative relaxation of the minimum cut problem," Computational Optimization and Applications, Springer, vol. 78(3), pages 853-891, April.

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