Semidefinite relaxations for non-convex quadratic mixed-integer programming

We present semidefinite relaxations for unconstrained non-convex quadratic mixed-integer optimization problems. These relaxations yield tight bounds and are computationally easy to solve for medium-sized instances, even if some of the variables are integer and unbounded. In this case, the problem co...

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Bibliographic Details
Published in:	Mathematical programming Vol. 141; no. 1-2; pp. 435 - 452
Main Authors:	Buchheim, Christoph, Wiegele, Angelika
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2013 Springer Nature B.V
Subjects:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Computation Computer programming Full Length Paper Integer programming Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical models Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Optimization Optimization techniques Programming Routines Studies Theoretical Variables United States > US 90C20 90C10 90C26 90C11 90C22
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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