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A new bound-and-reduce approach of nonconvex quadratic programming problems

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  • Gao, Yuelin
  • Wei, Fei

Abstract

For the nonconvex quadratic programming problem, a new linear programming relaxation bound-and-reduce algorithm is proposed and its convergence is proved. In this algorithm, a new hyper-rectangle partition technique and a new linear programming relaxation tactics are used. At the same time, the hyper-rectangular reduction method is used to raise its convergent speed. The numerical results demonstrate the effectiveness and feasibility of the proposed algorithm.

Suggested Citation

  • Gao, Yuelin & Wei, Fei, 2015. "A new bound-and-reduce approach of nonconvex quadratic programming problems," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 298-308.
    • Handle: RePEc:eee:apmaco:v:250:y:2015:i:c:p:298-308
    DOI: 10.1016/j.amc.2014.10.077
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    References listed on IDEAS

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    1. Weldon A. Lodwick, 1992. "Preprocessing Nonlinear Functional Constraints with Applications to the Pooling Problem," INFORMS Journal on Computing, INFORMS, vol. 4(2), pages 119-131, May.
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