Weaker Convergence of Global Relaxed Multisplitting USAOR Methods for an H-matrix

被引：0
|
作者：
Li-Tao Zhang
Ding-De Jiang
Xian-Yu Zuo
Ying-Chao Zhao
机构：
[1] Zhengzhou University of Aeronautics,School of Mathematics
[2] Henan Normal University,College of Mathematics Information Science
[3] Henan Province Synergy Innovation Center of Aviation Economic Development,School of Astronautics Aeronautic
[4] University of Electronic Science and Technology of China,Institute of Data Knowledge Engineering, School of Computer Information Engineering
[5] Henan University,Ministry of Sports and Public Art
[6] Zhengzhou University of Aeronautics,undefined
来源：
Mobile Networks and Applications | 2021年 / 26卷
关键词：
USAOR multisplitting; Relaxed parallel multisplitting method; -matrix; Convergence; Linear systems;
D O I：
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中图分类号：
学科分类号：
摘要：
In this paper, based on global relaxed parallel multisplitting USAOR (GUSAOR) method presented by Zhang et al. (Appl Math Comput 20:121–132, 2008) and similar ideas used by Zhang and Li (Comput Math Appl 67:1954–195, 2014), we further analyze global relaxed parallel multisplitting USAOR (GUSAOR) method and obtain the weaker convergence results compared to Zhang et al.’s when the system matrix is an H-matrix. Moreover, convergence graph and numerical examples clearly show that our new convergence domain is wider.
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页码：755 / 765
页数：10
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