A branch-and-cut algorithm using polar cuts for solving nonconvex quadratic programming problems

In this paper, we propose a branch-and-cut algorithm for solving a nonconvex quadratically constrained quadratic programming (QCQP) problem with a nonempty bounded feasible domain. The problem is first transformed into a linear conic programming problem, and then approximated by semidefinite program...

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Veröffentlicht in:	Optimization Jg. 67; H. 2; S. 359 - 375
Hauptverfasser:	Deng, Zhibin, Fang, Shu-Cherng, Lu, Cheng, Guo, Xiaoling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia Taylor & Francis 01.02.2018 Taylor & Francis LLC
Schlagworte:	Algorithms Branch & bound algorithms Branch-and-cut algorithm Feasibility Lower bounds nonconvex quadratically constrained quadratic programming Optimization polar cut Quadratic programming Semidefinite programming semidefinite relaxation
ISSN:	0233-1934, 1029-4945
Online-Zugang:	Volltext
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