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Set-completely-positive representations and cuts for the max-cut polytope and the unit modulus lifting

Author

Listed:
  • Florian Jarre

    (Mathematisches Institut, Heinrich Heine University)

  • Felix Lieder

    (Mathematisches Institut, Heinrich Heine University)

  • Ya-Feng Liu

    (Chinese Academy of Sciences)

  • Cheng Lu

    (School of Economics and Management, North China Electric Power University)

Abstract

This paper considers a generalization of the “max-cut-polytope” $$\hbox {conv}\{\ xx^T\mid x\in {\mathbb {R}}^n, \ \ |x_k| = 1 \ \hbox {for} \ 1\le k\le n\}$$conv{xxT∣x∈Rn,|xk|=1for1≤k≤n} in the space of real symmetric $$n\times n$$n×n-matrices with all-one diagonal to a complex “unit modulus lifting” $$\hbox {conv}\{xx^*\mid x\in {\mathbb {C}}^n, \ \ |x_k| = 1 \ \hbox {for} \ 1\le k\le n\}$$conv{xx∗∣x∈Cn,|xk|=1for1≤k≤n} in the space of complex Hermitian $$n\times n$$n×n-matrices with all-one diagonal. The unit modulus lifting arises in applications such as digital communications and shares similar symmetry properties as the max-cut-polytope. Set-completely positive representations of both sets are derived and the relation of the complex unit modulus lifting to its semidefinite relaxation is investigated in dimensions 3 and 4. It is shown that the unit modulus lifting coincides with its semidefinite relaxation in dimension 3 but not in dimension 4. In dimension 4 a family of deep valid cuts for the unit modulus lifting is derived that could be used to strengthen the semidefinite relaxation. It turns out that the deep cuts are also implied by a second lifting that could be used alternatively. Numerical experiments are presented comparing the first lifting, the second lifting, and the unit modulus lifting for $$n=4$$n=4.

Suggested Citation

  • Florian Jarre & Felix Lieder & Ya-Feng Liu & Cheng Lu, 2020. "Set-completely-positive representations and cuts for the max-cut polytope and the unit modulus lifting," Journal of Global Optimization, Springer, vol. 76(4), pages 913-932, April.
  • Handle: RePEc:spr:jglopt:v:76:y:2020:i:4:d:10.1007_s10898-019-00813-x
    DOI: 10.1007/s10898-019-00813-x
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    References listed on IDEAS

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    1. NESTEROV, Yu., 1998. "Semidefinite relaxation and nonconvex quadratic optimization," LIDAM Reprints CORE 1362, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Makoto Yamashita & Katsuki Fujisawa & Mituhiro Fukuda & Kazuhiro Kobayashi & Kazuhide Nakata & Maho Nakata, 2012. "Latest Developments in the SDPA Family for Solving Large-Scale SDPs," International Series in Operations Research & Management Science, in: Miguel F. Anjos & Jean B. Lasserre (ed.), Handbook on Semidefinite, Conic and Polynomial Optimization, chapter 0, pages 687-713, Springer.
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    4. Felix Lieder & Fatemeh Rad & Florian Jarre, 2015. "Unifying semidefinite and set-copositive relaxations of binary problems and randomization techniques," Computational Optimization and Applications, Springer, vol. 61(3), pages 669-688, July.
    5. Yongwei Huang & Shuzhong Zhang, 2007. "Complex Matrix Decomposition and Quadratic Programming," Mathematics of Operations Research, INFORMS, vol. 32(3), pages 758-768, August.
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    Cited by:

    1. Sinjorgo, Lennart & Sotirov, Renata & Anjos, M.F., 2024. "Cuts and semidefinite liftings for the complex cut polytope," Other publications TiSEM e99ba505-f4f2-4b3c-a6b5-2, Tilburg University, School of Economics and Management.
    2. Cheng Lu & Zhibin Deng & Shu-Cherng Fang & Wenxun Xing, 2023. "A New Global Algorithm for Max-Cut Problem with Chordal Sparsity," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 608-638, May.
    3. Cheng Lu & Zhibin Deng, 2021. "A branch-and-bound algorithm for solving max-k-cut problem," Journal of Global Optimization, Springer, vol. 81(2), pages 367-389, October.

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