Year: 2021
Author: Yuting Chen, Mingyuan Cao, Yueting Yang, Qingdao Huang
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 3 : pp. 358–374
Abstract
For symmetric tensors, computing generalized eigenvalues is equivalent to a homogenous polynomial optimization over the unit sphere. In this paper, we present an adaptive trust-region method for generalized eigenvalues of symmetric tensors. One of the features is that the trust-region radius is automatically updated by the adaptive technique to improve the algorithm performance. The other one is that a projection scheme is used to ensure the feasibility of all iteratives. Global convergence and local quadratic convergence of our algorithm are established, respectively. The preliminary numerical results show the efficiency of the proposed algorithm.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2001-m2019-0017
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 3 : pp. 358–374
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 17
Keywords: Symmetric tensors Generalized eigenvalues Trust-region Global convergence Local quadratic convergence.
Author Details
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An accelerated conjugate gradient method for the Z-eigenvalues of symmetric tensors
Cao, Mingyuan
Yang, Yueting
Li, Chaoqian
Jiang, Xiaowei
AIMS Mathematics, Vol. 8 (2023), Iss. 7 P.15008
https://doi.org/10.3934/math.2023766 [Citations: 0]