Improved Relaxed Positive-Definite and Skew-Hermitian Splitting Preconditioners for Saddle Point Problems

Improved Relaxed Positive-Definite and Skew-Hermitian Splitting Preconditioners for Saddle Point Problems

Year:    2019

Author:    Yang Cao, Zhiru Ren, Linquan Yao

Journal of Computational Mathematics, Vol. 37 (2019), Iss. 1 : pp. 95–111

Abstract

We establish a class of improved relaxed positive-definite and skew-Hermitian splitting (IRPSS) preconditioners for saddle point problems. These preconditioners are easier to be implemented than the relaxed positive-definite and skew-Hermitian splitting (RPSS) preconditioner at each step for solving the saddle point problem. We study spectral properties and the minimal polynomial of the IRPSS preconditioned saddle point matrix. A theoretical optimal IRPSS preconditioner is also obtained. Numerical results show that our proposed IRPSS preconditioners are superior to the existing ones in accelerating the convergence rate of the GMRES method for solving saddle point problems.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1710-m2017-0065

Journal of Computational Mathematics, Vol. 37 (2019), Iss. 1 : pp. 95–111

Published online:    2019-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    17

Keywords:    Saddle point problems Preconditioning RPSS preconditioner Eigenvalues Krylov subspace method.

Author Details

Yang Cao

Zhiru Ren

Linquan Yao

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