IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v376y2020ics0096300320300795.html
   My bibliography  Save this article

An efficient preconditioned variant of the PSS preconditioner for generalized saddle point problems

Author

Listed:
  • Huang, Zheng-Ge
  • Wang, Li-Gong
  • Xu, Zhong
  • Cui, Jing-Jing

Abstract

For the generalized saddle point problems from the Stokes equations, we propose an efficient preconditioned variant of the positive semidefinite and skew-Hermitian (PVPSS) preconditioner. The new preconditioner is established by adopting matrix preconditioning strategy and relaxation technique for the PSS one derived by Pan et al. (2006). Compared with the PSS one, the PVPSS preconditioner is much closer to the coefficient matrix and easier to be implemented if proper preconditioning matrices are adopted. We prove the convergence of the PVPSS iteration method under some restrictions and discuss the spectral properties of the PVPSS preconditioned matrix. Meanwhile, the implementation and a practical way to choose the parameter of the PVPSS preconditioner are discussed. Comparisons between the PVPSS preconditioner and some existing ones are also given. Numerical experiments are carried out to illustrate that the proposed preconditioner is effective for the generalized saddle point problems and outperforms several other commonly used ones.

Suggested Citation

  • Huang, Zheng-Ge & Wang, Li-Gong & Xu, Zhong & Cui, Jing-Jing, 2020. "An efficient preconditioned variant of the PSS preconditioner for generalized saddle point problems," Applied Mathematics and Computation, Elsevier, vol. 376(C).
  • Handle: RePEc:eee:apmaco:v:376:y:2020:i:c:s0096300320300795
    DOI: 10.1016/j.amc.2020.125110
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300320300795
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2020.125110?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Fang, 2015. "On choices of iteration parameter in HSS method," Applied Mathematics and Computation, Elsevier, vol. 271(C), pages 832-837.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maia, A.A.G. & Cavalca, D.F. & Tomita, J.T. & Costa, F.P. & Bringhenti, C., 2022. "Evaluation of an effective and robust implicit time-integration numerical scheme for Navier-Stokes equations in a CFD solver for compressible flows," Applied Mathematics and Computation, Elsevier, vol. 413(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kai Jiang & Jianghao Su & Juan Zhang, 2022. "A Data-Driven Parameter Prediction Method for HSS-Type Methods," Mathematics, MDPI, vol. 10(20), pages 1-24, October.
    2. Ling, Si-Tao & Liu, Qing-Bing, 2017. "New local generalized shift-splitting preconditioners for saddle point problems," Applied Mathematics and Computation, Elsevier, vol. 302(C), pages 58-67.
    3. Huang, Zheng-Ge & Wang, Li-Gong & Xu, Zhong & Cui, Jing-Jing, 2017. "The generalized modified shift-splitting preconditioners for nonsymmetric saddle point problems," Applied Mathematics and Computation, Elsevier, vol. 299(C), pages 95-118.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:376:y:2020:i:c:s0096300320300795. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.