IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v269y2015icp87-104.html
   My bibliography  Save this article

Some new preconditioned generalized AOR methods for generalized least-squares problems

Author

Listed:
  • Huang, Zheng-Ge
  • Xu, Zhong
  • Lu, Quan
  • Cui, Jing-Jing

Abstract

In this paper, we present some new preconditioned GAOR methods for solving generalized least-squares problems and their comparison results. Comparison results show that the convergence rates of the new preconditioned GAOR methods are better than those of the preconditioned GAOR methods presented by Zhou et al. (2009) [19] and Wang et al. (2013) [18] whenever these methods are convergent. Lastly, numerical experiments are provided to confirm the theoretical results.

Suggested Citation

  • Huang, Zheng-Ge & Xu, Zhong & Lu, Quan & Cui, Jing-Jing, 2015. "Some new preconditioned generalized AOR methods for generalized least-squares problems," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 87-104.
  • Handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:87-104
    DOI: 10.1016/j.amc.2015.07.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300315009819
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2015.07.062?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.

    Citations

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


    Cited by:

    1. Chein-Shan Liu & Essam R. El-Zahar & Chih-Wen Chang, 2023. "Dynamical Optimal Values of Parameters in the SSOR, AOR, and SAOR Testing Using Poisson Linear Equations," Mathematics, MDPI, vol. 11(18), pages 1-21, September.
    2. Miao, Shu-Xin & Luo, Yu-Hua & Wang, Guang-Bin, 2018. "Two new preconditioned GAOR methods for weighted linear least squares problems," Applied Mathematics and Computation, Elsevier, vol. 324(C), pages 93-104.

    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:269:y:2015:i:c:p:87-104. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.