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A Global Optimization Algorithm for Generalized Quadratic Programming

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  • Hongwei Jiao
  • Yongqiang Chen

Abstract

We present a global optimization algorithm for solving generalized quadratic programming (GQP), that is, nonconvex quadratic programming with nonconvex quadratic constraints. By utilizing a new linearizing technique, the initial nonconvex programming problem (GQP) is reduced to a sequence of relaxation linear programming problems. To improve the computational efficiency of the algorithm, a range reduction technique is employed in the branch and bound procedure. The proposed algorithm is convergent to the global minimum of the (GQP) by means of the subsequent solutions of a series of relaxation linear programming problems. Finally, numerical results show the robustness and effectiveness of the proposed algorithm.

Suggested Citation

  • Hongwei Jiao & Yongqiang Chen, 2013. "A Global Optimization Algorithm for Generalized Quadratic Programming," Journal of Applied Mathematics, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnljam:v:2013:y:2013:i:1:n:215312
    DOI: 10.1155/2013/215312
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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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    Cited by:

    1. Hongwei Jiao & Yong-Qiang Chen & Wei-Xin Cheng, 2014. "A Novel Optimization Method for Nonconvex Quadratically Constrained Quadratic Programs," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).

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