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A new relaxed PSS preconditioner for nonsymmetric saddle point problems

Author

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  • Zhang, Ke
  • Zhang, Ju-Li
  • Gu, Chuan-Qing

Abstract

A new relaxed PSS-like iteration scheme for the nonsymmetric saddle point problem is proposed. As a stationary iterative method, the new variant is proved to converge unconditionally. When used for preconditioning, the preconditioner differs from the coefficient matrix only in the upper-right components. The theoretical analysis shows that the preconditioned matrix has a well-clustered eigenvalues around (1, 0) with a reasonable choice of the relaxation parameter. This sound property is desirable in that the related Krylov subspace method can converge much faster, which is validated by numerical examples.

Suggested Citation

  • Zhang, Ke & Zhang, Ju-Li & Gu, Chuan-Qing, 2017. "A new relaxed PSS preconditioner for nonsymmetric saddle point problems," Applied Mathematics and Computation, Elsevier, vol. 308(C), pages 115-129.
    • Handle: RePEc:eee:apmaco:v:308:y:2017:i:c:p:115-129
    DOI: 10.1016/j.amc.2017.03.022
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    Cited by:

    1. Shen, Hai-Long & Wu, Hong-Yu & Shao, Xin-Hui, 2021. "A simplified PSS preconditioner for non-Hermitian generalized saddle point problems," Applied Mathematics and Computation, Elsevier, vol. 394(C).

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