Paper Info
Title
Spectral Relaxations And Branching Strategies For Global Optimization Of Mixed-Integer Quadratic Programs
Abstract
We consider the global optimization of nonconvex (mixed-integer) quadratic programs. We present a family of convex quadratic relaxations derived by convexifying nonconvex quadratic functions through perturbations of the quadratic matrix. We investigate the theoretical properties of these relaxations and show that they are equivalent to some particular semidefinite programs. We also introduce novel branching variable selection strategies motivated by the quadratic relaxations investigated in this paper. The proposed relaxation and branching techniques are implemented in the global optimization solver BARON and tested 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.
Year
DOI
Venue
2021
10.1137/19M1271762
SIAM JOURNAL ON OPTIMIZATION
Keywords
DocType
Volume
global optimization, mixed-integer quadratic programs, convex quadratic relaxations, branching strategies
Journal
31
Issue
ISSN
Citations 
1
1052-6234
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Carlos J. Nohra100.34
Arvind U. Raghunathan216320.63
Nikolaos V. Sahinidis31909136.89