Globally solving non-convex quadratic programs via linear integer programming techniques

Wei Xia, J. C. Vera, Luis F. Zuluaga

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We reformulate a (indefinite) quadratic program (QP) as a mixed-integer linear programming (MILP) problem by first reformulating a QP as a linear complementary problem, and then using binary variables and big-M constraints to model its complementary constraints. To obtain such reformulation, we use fundamental results on the solution of perturbed linear systems to impose bounds on the QP’s dual variables without eliminating any of its (globally) optimal primal solutions. Reformulating a nonconvex QP as a MILP problem allows the use of current state-of-the-art MILP solvers to find its global optimal solution. To illustrate this, we compare the performance of this MILP-based solution approach, labeled quadprogIP, with quadprogBB, BARON, and CPLEX. In practice, quadprogIP is shown to typically outperform by orders of magnitude quadprogBB, BARON, and CPLEX on standard QPs. Also, unlike quadprogBB, quadprogIP is able to solve QP instances in which the dual feasible set is unbounded. The MATLAB code quadprogIP and the instances used to perform the reported numerical experiments are publicly available at https://github.com/xiawei918/quadprogIP.
Original languageEnglish
Article numberC2
Pages (from-to)1-198
JournalINFORMS Journal on Computing
Volume32
Issue number1
Early online dateJul 2019
DOIs
Publication statusPublished - Jan 2020

Keywords

  • non-convex quadratic programming
  • global optimization
  • mixed-integer linear optimization
  • KKT conditions
  • branch and bound
  • Hoffman bound

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