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A preconditioned shift-splitting iteration method for solving indefinite least squares problem
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DOI：10.1007/s12190-025-02700-z.png)
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Abstract
En 中文
We study preconditioning techniques for the indefinite least squares problem and present the shift-splitting (SS) preconditioner. We prove that the shift-splitting method is unconditionally convergent, which leads to an excellent clustering property of the eigenvalues of the SS preconditioned matrix. Additionally, we propose a relaxed version of the SS preconditioner (RSS) that further improves computational efficiency. Numerical experiments demonstrate that our proposed SS and RSS preconditioners are superior to existing preconditioners in terms of CPU time and number of iterations, and the corresponding preconditioned matrices exhibit good spectral clustering properties.
Keywords:
Shift-splitting
Preconditioning techniques
Spectral clustering
Journal
J
IF:
2.7
Papers:
169
Citations:
0
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