A graphic structure based branch-and-bound algorithm for complex quadratic optimization and applications to magnitude least-square problem

Cheng Lu (), Jitao Ma (), Zhibin Deng () and Wenxun Xing ()
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Cheng Lu: North China Electric Power University
Jitao Ma: North China Electric Power University
Zhibin Deng: University of Chinese Academy of Sciences
Wenxun Xing: Tsinghua University

Journal of Global Optimization, 2024, vol. 88, issue 1, No 5, 115-137

Abstract: Abstract In this paper, we propose a semidefinite relaxation based branch-and-bound algorithm to the unit-modulus constrained complex quadratic programming problem, which has broad applications in signal processing and wireless communications. Our research is motivated from the potential application of the magnitude least-square problem, which possesses the so-called block-arrow sparsity pattern. We discuss how to reduce the worst-case complexity of the proposed branch-and-bound algorithm by exploiting this special sparsity pattern. Numerical results are presented to show the effectiveness of the proposed algorithm.

Keywords: Quadratic optimization; Semidefinite relaxation; Sparsity pattern; Magnitude least-square problem (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10898-023-01305-9

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