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Nonconvex quadratically constrained quadratic programming: best D.C. decompositions and their SDP representations

Author

Listed:
  • X. Zheng

    ()

  • X. Sun

    ()

  • D. Li

    ()

Abstract

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Suggested Citation

  • X. Zheng & X. Sun & D. Li, 2011. "Nonconvex quadratically constrained quadratic programming: best D.C. decompositions and their SDP representations," Journal of Global Optimization, Springer, vol. 50(4), pages 695-712, August.
  • Handle: RePEc:spr:jglopt:v:50:y:2011:i:4:p:695-712
    DOI: 10.1007/s10898-010-9630-9
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    Citations

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    Cited by:

    1. Marcia Fampa & Jon Lee & Wendel Melo, 0. "On global optimization with indefinite quadratics," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 0, pages 1-29.
    2. Samuel Burer & Sunyoung Kim & Masakazu Kojima, 2014. "Faster, but weaker, relaxations for quadratically constrained quadratic programs," Computational Optimization and Applications, Springer, vol. 59(1), pages 27-45, October.
    3. Marandi, Ahmadreza, 2017. "Aspects of quadratic optimization - nonconvexity, uncertainty, and applications," Other publications TiSEM d2b9c576-7128-4ee4-939a-7, Tilburg University, School of Economics and Management.
    4. Santi, √Čverton & Aloise, Daniel & Blanchard, Simon J., 2016. "A model for clustering data from heterogeneous dissimilarities," European Journal of Operational Research, Elsevier, vol. 253(3), pages 659-672.
    5. repec:spr:eurjco:v:5:y:2017:i:3:d:10.1007_s13675-016-0079-6 is not listed on IDEAS

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