数学
栏(排版)
趋同(经济学)
贪婪算法
迭代法
数学优化
有界函数
块(置换群论)
贪婪随机自适应搜索过程
应用数学
算法
组合数学
连接(主束)
数学分析
几何学
经济
经济增长
作者
Nianci Wu,Ling-Xia,Qian Zuo
标识
DOI:10.1016/j.amc.2022.127339
摘要
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.
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