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New semidefinite relaxations for a class of complex quadratic programming problems

Author

Listed:
  • Yingzhe Xu

    (North China Electric Power University)

  • Cheng Lu

    (North China Electric Power University)

  • Zhibin Deng

    (University of Chinese Academy of Sciences
    MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS)

  • Ya-Feng Liu

    (Chinese Academy of Sciences)

Abstract

In this paper, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, which widely appear in the areas of signal processing and power system. By deriving new valid constraints to the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of the complex quadratic programming problems. Then, we compare the proposed semidefinite relaxations with existing ones, and show that the newly proposed semidefinite relaxations could be strictly tighter than the previous ones. Moreover, the proposed semidefinite relaxations can be applied to more general cases of complex quadratic programming problems, whereas the previous ones are only designed for special cases. Numerical results indicate that the proposed semidefinite relaxations not only provide tighter relaxation bounds, but also improve some existing approximation algorithms by finding better sub-optimal solutions.

Suggested Citation

  • Yingzhe Xu & Cheng Lu & Zhibin Deng & Ya-Feng Liu, 2023. "New semidefinite relaxations for a class of complex quadratic programming problems," Journal of Global Optimization, Springer, vol. 87(1), pages 255-275, September.
    • Handle: RePEc:spr:jglopt:v:87:y:2023:i:1:d:10.1007_s10898-023-01290-z
    DOI: 10.1007/s10898-023-01290-z
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    References listed on IDEAS

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    1. Cheng Lu & Zhibin Deng & Wei-Qiang Zhang & Shu-Cherng Fang, 2018. "Argument division based branch-and-bound algorithm for unit-modulus constrained complex quadratic programming," Journal of Global Optimization, Springer, vol. 70(1), pages 171-187, January.
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