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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Vydané v:	Computational optimization and applications Ročník 59; číslo 1-2; s. 27 - 45
Hlavní autori:	Burer, Samuel, Kim, Sunyoung, Kojima, Masakazu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.10.2014 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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