Faster, but weaker, relaxations for quadratically constrained quadratic programs

We introduce a new relaxation framework for nonconvex quadratically constrained quadratic programs (QCQPs). In contrast to existing relaxations based on semidefinite programming (SDP), our relaxations incorporate features of both SDP and second order cone programming (SOCP) and, as a result, solve m...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 59; no. 1-2; pp. 27 - 45
Main Authors:	Burer, Samuel, Kim, Sunyoung, Kojima, Masakazu
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.10.2014 Springer Nature B.V
Subjects:	Analysis Blocking Computation Computer science Constraints Convex and Discrete Geometry Linear programming Management Science Mathematical analysis Mathematical programming Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Programming Quadratic programming Semidefinite programming Statistics Studies Semidefinite programming Nonconvex quadratic programming Second-order cone programming Difference of convex
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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