Year: 2023
Author: Siru Gong, Yangfeng Su
Journal of Computational Mathematics, Vol. 41 (2023), Iss. 6 : pp. 1117–1136
Abstract
Implicit determinant method is an effective method for some linear eigenvalue optimization problems since it solves linear systems of equations rather than eigenpairs. In this paper, we generalize the implicit determinant method to solve an Hermitian eigenvalue optimization problem for smooth case and non-smooth case. We prove that the implicit determinant method converges locally and quadratically. Numerical experiments confirm our theoretical results and illustrate the efficiency of implicit determinant method.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2203-m2020-0303
Journal of Computational Mathematics, Vol. 41 (2023), Iss. 6 : pp. 1117–1136
Published online: 2023-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 20
Keywords: Eigenvalue optimization Multiple eigenvalue Non-smooth optimization Implicit determinant method Crawford number.