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Conic approximation to nonconvex quadratic programming with convex quadratic constraints

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Abstract

In this paper, a conic reformulation and approximation is proposed for solving a nonconvex quadratic programming problem subject to several convex quadratic constraints. The original problem is transformed into a linear conic programming problem, which can be approximated by a sequence of linear conic programming problems over the dual cone of the cone of nonnegative quadratic functions. Since the dual cone of the cone of nonnegative quadratic functions has a linear matrix inequality representation, each linear conic programming problem in the sequence can be solved efficiently using the semidefinite programming techniques. In order to speed up the convergence of the approximation sequence and relieve the computational effort in solving the linear conic programming problems, an adaptive scheme is adopted in the proposed algorithm. We prove that the lower bounds generated by the linear conic programming problems converge to the optimal value of the original problem. Several numerical examples are used to illustrate how the algorithm works and the computational results demonstrate the efficiency of the proposed algorithm.

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Author information

Authors and Affiliations

  1. School of Management, University of Chinese Academy of Sciences, Beijing, China

    Zhibin Deng

  2. Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA

    Shu-Cherng Fang

  3. Department of Management Science and Engineering, Zhejiang University, Hangzhou, China

    Qingwei Jin

  4. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Cheng Lu

Authors
  1. Zhibin Deng
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  2. Shu-Cherng Fang
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  3. Qingwei Jin
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  4. Cheng Lu
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Corresponding author

Correspondence to Qingwei Jin.

Additional information

Deng’s research has been supported by the Edward P. Fitts Fellowship at NCSU, Fang’s research by the US National Science Foundation Grant No. DMI-0553310, Jin’s research by the National Natural Science Foundation of China Grant No. 11301479 and the project supported by Zhejiang Provincial Natural Science Foundation of China No. LQ13A010001, Lu’s research has been supported by the Doctoral Research Innovation Fund of Tsinghua University.

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Deng, Z., Fang, SC., Jin, Q. et al. Conic approximation to nonconvex quadratic programming with convex quadratic constraints. J Glob Optim 61, 459–478 (2015). https://doi.org/10.1007/s10898-014-0195-x

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  • DOI: https://doi.org/10.1007/s10898-014-0195-x

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