A Class of Generalized Shift-Splitting Preconditioners for Double Saddle Point Problems

In this paper, we propose a generalized shift-splitting (GSS) preconditioner, along with its two relaxed variants to solve the double saddle point problem (DSPP). The convergence of the associated GSS iterative method is analyzed, and sufficient conditions for its convergence are established. Spectr...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Ahmad, Sk Safique, Khatun, Pinki
Format: Article
Language:English
Subjects:
Convergence
Eigenvalues
Iterative methods
Parameterization
Saddle points
Splitting
Online Access:Get full text
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Summary:In this paper, we propose a generalized shift-splitting (GSS) preconditioner, along with its two relaxed variants to solve the double saddle point problem (DSPP). The convergence of the associated GSS iterative method is analyzed, and sufficient conditions for its convergence are established. Spectral analyses are performed to derive sharp bounds for the eigenvalues of the preconditioned matrices. Numerical experiments based on examples arising from the PDE-constrained optimization problems demonstrate the effectiveness and robustness of the proposed preconditioners compared with existing state-of-the-art preconditioners.
ISSN:2331-8422