Accelerating the shift-splitting iteration algorithm

Zhizhi Li, Risheng Chu and Huai Zhang

Applied Mathematics and Computation, 2019, vol. 361, issue C, 421-429

Abstract: The dazzling property of shift-splitting iteration method is unconditional convergence for any parameters and Anderson mixing as a simple and classic method can greatly speed up convergence of fix point iterations. Inheriting the merits of them, we propose an accelerated preconditioning shift-splitting algorithm for generalized saddle point problems. Then, we verify its unconditional convergence. Besides, we discuss the spectrum distribution of iteration matrix and then provide the relationship of optimal parameters involved. Finally, numerical experiments underline its superiority both as a solver and preconditioner.

Keywords: Shift-splitting; Anderson mixing; Generalized saddle point problems; Relationship of optimal parameters (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:361:y:2019:i:c:p:421-429

DOI: 10.1016/j.amc.2019.05.056

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