Spectral relaxations and branching strategies for global optimization of mixed-integer quadratic programs

TL;DR

This paper introduces a new family of convex quadratic relaxations and branching strategies for global optimization of nonconvex and mixed-integer quadratic programs, significantly improving solver efficiency.

Contribution

It proposes novel convex relaxations derived from matrix perturbations and new branching variable selection strategies, integrated into the BARON solver.

Findings

Significant reduction in computational times for test problems.

Theoretical equivalence of relaxations to certain semidefinite programs.

Enhanced performance of BARON with the new techniques.

Abstract

We consider the global optimization of nonconvex quadratic programs and mixed-integer quadratic programs. We present a family of convex quadratic relaxations which are derived by convexifying nonconvex quadratic functions through perturbations of the quadratic matrix. We investigate the theoretical properties of these quadratic relaxations and show that they are equivalent to some particular semidefinite programs. We also introduce novel branching variable selection strategies which can be used in conjunction with the quadratic relaxations investigated in this paper. We integrate the proposed relaxation and branching techniques into the global optimization solver BARON, and test our implementation by conducting numerical experiments on a large collection of problems. Results demonstrate that the proposed implementation leads to very significant reductions in BARON's computational times to solve the test problems.