On the relaxed greedy deterministic row and column iterative methods

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.

References in corpus (5)

• Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method
• Randomized Iterative Methods for Linear Systems
• Randomized Iterative Methods with Alternating Projections
• Greedy Block Gauss-Seidel Methods for Solving Large Linear Least Squares Problem
• Randomized extended block Kaczmarz for solving least squares

Cited by in corpus (1)

• On the Polyak momentum variants of the greedy deterministic single and multiple row-action methods