A sensitive-eigenvector based global algorithm for quadratically constrained quadratic programming

In this paper, we design an eigenvalue decomposition based branch-and-bound algorithm for finding global solutions of quadratically constrained quadratic programming (QCQP) problems. The hardness of nonconvex QCQP problems roots in the nonconvex components of quadratic terms, which are represented b...

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Vydané v:	Journal of global optimization Ročník 73; číslo 2; s. 371 - 388
Hlavní autori:	Lu, Cheng, Deng, Zhibin, Zhou, Jing, Guo, Xiaoling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 15.02.2019 Springer Springer Nature B.V
Predmet:	Algorithms Computer Science Decomposition Eigenvalues Eigenvectors Mathematics Mathematics and Statistics Mechanical properties Operations Research/Decision Theory Optimization Quadratic programming Real Functions Global optimization Semidefinite relaxation Quadratically constrained quadratic programming Branch-and-bound algorithm
ISSN:	0925-5001, 1573-2916
On-line prístup:	Získať plný text
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