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Two new preconditioned GAOR methods for weighted linear least squares problems

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  • Miao, Shu-Xin
  • Luo, Yu-Hua
  • Wang, Guang-Bin

Abstract

In this paper, the preconditioned generalized accelerated overrelaxation (GAOR) methods for solving weighted linear least squares problems are considered. Two new preconditioners are proposed and the convergence rates of the new preconditioned GAOR methods are studied. Comparison results show that the convergence rates of the new preconditioned GAOR methods are better than those of the preconditioned GAOR methods in the previous literatures whenever these methods are convergent. A numerical example is given to confirm our theoretical results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:324:y:2018:i:c:p:93-104
    DOI: 10.1016/j.amc.2017.12.007
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    References listed on IDEAS

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    1. 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.
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