On the relaxed greedy deterministic row and column iterative methods
作者：
Highlights：
• By utilizing the Petrov-Galerkin conditions and relaxed greedy index selection technique, we provide two relaxed greedy deterministic row (RGDR) and column (RGDC) iterative methods to solve the large-scale linear system.
• We prove that RGDR and RGDC can converge linearly to the least norm solution of overdetermined and consistent linear system.
• Numerical examples show that the proposed algorithms are more effective than the relaxed greedy randomized row and column iterative methods.
摘要
•By utilizing the Petrov-Galerkin conditions and relaxed greedy index selection technique, we provide two relaxed greedy deterministic row (RGDR) and column (RGDC) iterative methods to solve the large-scale linear system.•We prove that RGDR and RGDC can converge linearly to the least norm solution of overdetermined and consistent linear system.•Numerical examples show that the proposed algorithms are more effective than the relaxed greedy randomized row and column iterative methods.
论文关键词：Petrov-Galerkin conditions,Relaxed greedy selection,Row and column methods,Convergence analysis
论文评审过程：Received 18 September 2021, Revised 9 May 2022, Accepted 15 June 2022, Available online 4 July 2022, Version of Record 4 July 2022.
论文官网地址：https://doi.org/10.1016/j.amc.2022.127339
