Mathematical Programming Computation

, Volume 10, Issue 3, pp 333–382 | Cite as

Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods

  • Pierre Bonami
  • Oktay Günlük
  • Jeff Linderoth
Full Length Paper


We present effective linear programming based computational techniques for solving nonconvex quadratic programs with box constraints (BoxQP). We first observe that known cutting planes obtained from the Boolean Quadric Polytope (BQP) are computationally effective at reducing the optimality gap of BoxQP. We next show that the Chvátal–Gomory closure of the BQP is given by the odd-cycle inequalities even when the underlying graph is not complete. By using these cutting planes in a spatial branch-and-cut framework, together with a common integrality-based preprocessing technique and a particular convex quadratic relaxation, we develop a solver that can effectively solve a well-known family of test instances. Our linear programming based solver is competitive with SDP-based state of the art solvers on small instances and sparse instances. Most of our computational techniques have been implemented in the recent version of CPLEX and have led to significant performance improvements on nonconvex quadratic programs with linear constraints.


Nonconvex quadratic programming Global optimization Boolean Quadric Polytope 

Mathematics Subject Classification

90C20 90C26 90C57 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature and The Mathematical Programming Society 2018

Authors and Affiliations

  1. 1.IBM SpainMadridSpain
  2. 2.IBM ResearchYorktown HeightsUSA
  3. 3.Department of Industrial and Systems Engineering, Wisconsin Institutes of DiscoveryUniversity of Wisconsin-MadisonMadisonUSA

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