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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Bibliographic Details
Published in:	Journal of global optimization Vol. 73; no. 2; pp. 371 - 388
Main Authors:	Lu, Cheng, Deng, Zhibin, Zhou, Jing, Guo, Xiaoling
Format:	Journal Article
Language:	English
Published:	New York Springer US 15.02.2019 Springer Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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