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On the relaxed greedy deterministic row and column iterative methods

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  • Wu, Nian-Ci
  • Cui, Ling-Xia
  • Zuo, Qian

Abstract

For solving the large-scale linear system by iteration methods, we utilize the Petrov-Galerkin conditions and relaxed greedy index selection technique, and provide two relaxed greedy deterministic row (RGDR) and column (RGDC) iterative methods, in which one special case of RGDR reduces to the fast deterministic block Kaczmarz method proposed in Chen and Huang (Numer. Algor., 89: 1007-1029, 2021). Our convergence analyses reveal that the resulting algorithms all have the linear convergence rates, which are bounded by the explicit expressions. Numerical examples show that the proposed algorithms are more effective than the relaxed greedy randomized row and column iterative methods.

Suggested Citation

  • Wu, Nian-Ci & Cui, Ling-Xia & Zuo, Qian, 2022. "On the relaxed greedy deterministic row and column iterative methods," Applied Mathematics and Computation, Elsevier, vol. 432(C).
    • Handle: RePEc:eee:apmaco:v:432:y:2022:i:c:s0096300322004131
    DOI: 10.1016/j.amc.2022.127339
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    References listed on IDEAS

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    1. D. Leventhal & A. S. Lewis, 2010. "Randomized Methods for Linear Constraints: Convergence Rates and Conditioning," Mathematics of Operations Research, INFORMS, vol. 35(3), pages 641-654, August.
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