Bai, Z.Z. and Wu, W.T. (2019) On Greedy Randomized Coordinate Descent Methods for Solving Large Linear Least-Squares Problems. Numerical Linear Algebra with Applications, 26, e2237.
https://doi.org/10.1002/nla.2237

has been cited by the following article:

• TITLE: Greedy Randomized Gauss-Seidel Method with Oblique Direction

  AUTHORS: Weifeng Li, Pingping Zhang

  KEYWORDS: Oblique Direction, Linear Least Squares Problem, Gauss-Seidel Method

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.4, April 27, 2023

  ABSTRACT: For the linear least squares problem with coefficient matrix columns being highly correlated, we develop a greedy randomized Gauss-Seidel method with oblique direction. Then the corresponding convergence result is deduced. Numerical examples demonstrate that our proposed method is superior to the greedy randomized Gauss-Seidel method and the randomized Gauss-Seidel method with oblique direction.