Two-level least squares methods in Krylov subspaces
type: Item
instance of: scholarly article
title: Two-level least squares methods in Krylov subspaces
zbMATH Open document ID: 1405.65045
author: V. P. Il'in
published in: Journal of Mathematical Sciences (New York)
publication date: 2018-08-17T00:00:00Z
review text: The author presents two-level methods for accelerating the convergence of restarted iterations based on the least squares method (LSM) for decreasing the Euclidean norm of the residual, for large, sparse and ill-conditioned linear systems. In this respect, he also considers applications of less expensive conjugate residual (CR) algorithms and the Chebyshev acceleration. Numerical experiments are presented on a set of model Dirichlet problems for the convection-diffusion equation.
reviewed by: Constantin Popa
Mathematics Subject Classification ID: 65F10
Mathematics Subject Classification ID: 65F22
Mathematics Subject Classification ID: 65F50
Mathematics Subject Classification ID: 41A50
zbMATH DE Number: 6920109
zbMATH Keywords: Krylov subspaces
zbMATH Keywords: Chebyshev acceleration
zbMATH Keywords: two-level method
zbMATH Keywords: least squares
zbMATH Keywords: restarted conjugate residual method
zbMATH Keywords: iterative methods
zbMATH Keywords: convection-diffusion equation
MaRDI profile type: Publication
full work available at URL: s10958-018-3916-8
OpenAlex ID: W2809990549
cites work: Yet another polynomial preconditioner for the conjugate gradient algorithm
Wikidata QID: Q129646910
DOI: 10.1007/S10958-018-3916-8
description: scientific article; zbMATH DE number 6920109