Pseudoinverse preconditioners and iterative methods for large dense linear least-squares problems
type: Item
instance of: scholarly article
title: Pseudoinverse preconditioners and iterative methods for large dense linear least-squares problems
zbMATH Open document ID: 1392.65025
author: Oskar Cahueñas
author: Luis M. Hernádez-Ramos
author: M. Raydan
published in: Bulletin of Computational Applied Mathematics
publication date: 2018-08-07T00:00:00Z
full work available at URL: http://drive.google.com/open
review text: Summary: We address the issue of approximating the pseudoinverse of the coefficient matrix for dynamically building preconditioning strategies for the numerical solution of large dense linear least-squares problems. The new preconditioning strategies are embedded into simple and well-known iterative schemes that avoid the use of the, usually ill-conditioned, normal equations. We analyze a scheme to approximate the pseudoinverse, based on Schulz iterative method, and also different iterative schemes, based on extensions of Richardson's method, and the conjugate gradient method, that are suitable for preconditioning strategies. We present preliminary numerical results to illustrate the advantages of the proposed schemes.
Mathematics Subject Classification ID: 65F08
Mathematics Subject Classification ID: 65F10
Mathematics Subject Classification ID: 15A09
zbMATH DE Number: 6914238
zbMATH Keywords: Schulz method
zbMATH Keywords: pseudoinverse
zbMATH Keywords: linear least-squares problems
zbMATH Keywords: preconditioned Richardson's method
zbMATH Keywords: conjugate gradient method
describes a project that uses: Matlab
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6914238