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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
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    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.

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