New semidefinite relaxations for a class of complex quadratic programming problems

Yingzhe Xu (), Cheng Lu (), Zhibin Deng () and Ya-Feng Liu ()
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Yingzhe Xu: North China Electric Power University
Cheng Lu: North China Electric Power University
Zhibin Deng: University of Chinese Academy of Sciences
Ya-Feng Liu: Chinese Academy of Sciences

Journal of Global Optimization, 2023, vol. 87, issue 1, No 8, 255-275

Abstract: 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.

Keywords: Quadratic optimization; Semidefinite relaxation; Approximation algorithm; Phase quantized waveform design; Discrete transmit beamforming (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10898-023-01290-z

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