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

2024/08/21 by Sk. Safique Ahmad, Ahmad, Sk. Safique, Pinki Khatun +1 · 1 citation
Computer Science · Engineering · #65F08 #65F10 #65F50 #Aerospace Engineering and Control Systems #FOS: Mathematics #Matrix Theory and Algorithms #Nuclear reactor physics and engineering #Numerical Analysis (math.NA)

paper · pdf · doi:10.48550/arxiv.2408.11750

openalex publication_date 2024/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 problem and the leaky lid-driven cavity problem demonstrate the effectiveness and robustness of the proposed preconditioners compared with existing state-of-the-art preconditioners.

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