Journal of Global Optimization

, Volume 70, Issue 4, pp 719–735 | Cite as

Quadratic convex reformulation for nonconvex binary quadratically constrained quadratic programming via surrogate constraint

Article

Abstract

We investigate in this paper nonconvex binary quadratically constrained quadratic programming (QCQP) which arises in various real-life fields. We propose a novel approach of getting quadratic convex reformulation (QCR) for this class of optimization problem. Our approach employs quadratic surrogate functions and convexifies all the quadratic inequality constraints to construct QCR. The price of this approach is the introduction of an extra quadratic inequality. The “best” QCR among the proposed family, in terms that the bound of the corresponding continuous relaxation is best, can be found via solving a semidefinite programming problem. Furthermore, we prove that the bound obtained by continuous relaxation of our best QCR is as tight as Lagrangian bound of binary QCQP. Computational experiment is also conducted to illustrate the solution efficiency improvement of our best QCR when applied in off-the-shell software.

Keywords

Binary QCQP Semidefinite programming Quadratic convex reformulation Global optimization 

Notes

Acknowledgements

The authors would like to thank three anonymous referees for their constructive suggestions and insightful comments, which helped improve the paper substantially.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementTongji UniversityShanghaiPeople’s Republic of China
  2. 2.School of MathematicsShanghai University of Finance and EconomicsShanghaiPeople’s Republic of China

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