Quadratic convex reformulations for a class of complex quadratic programming problems

Cheng Lu (), Gaojian Kang (), Guangtai Qu () and Zhibin Deng ()
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Cheng Lu: North China Electric Power University
Gaojian Kang: North China Electric Power University
Guangtai Qu: North China Electric Power University
Zhibin Deng: University of Chinese Academy of Sciences

Computational Optimization and Applications, 2025, vol. 91, issue 1, No 4, 125-144

Abstract: Abstract We investigate a class of complex quadratic programming problems characterized by unit-modulus and discrete argument constraints. This problem can be reformulated as a mixed-integer quadratic programming problem, which could be addressed using a commercial solver such as Gurobi. However, the solver’s efficiency is often unsatisfying if the problem formulation is inadequately designed. In this paper, we introduce several quadratic convex reformulations aimed at enhancing the solver’s performance. We extend the classical diagonal perturbation-based reformulation technique to this problem. Additionally, by leveraging the unique structure of the problem, we derive a new quadratic convex reformulation that provides a tighter continuous relaxation compared to the diagonal perturbation-based approach. The numerical tests on random instances and the unimodular code design problem demonstrate the superiority of the newly proposed reformulation.

Keywords: Mixed-integer quadratic optimization; Convex quadratic reformulation; Semidefinite relaxation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10589-025-00672-1

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