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

  • We present effective linear programming based computational techniquesfor 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.

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Metadaten
Author:Pierre Bonami, Oktay Günlük, Jeff Linderoth
DOI:https://doi.org/10.1007/s12532-018-0133-x
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2018
Tag:Software; Theoretical Computer Science
Volume:10
Issue:3
Page Number:50
First Page:333
Last Page:382
Mathematical Programming Computation :MPC 2018 - Issue 3
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