Flexible modified LSMR for least squares problems

LSMR is a widely recognized method for solving least squares problems via the double QR decomposition. Various preconditioning techniques have been explored to improve its efficiency. One issue that arises when implementing these preconditioning techniques is the need to solve two linear systems per iterative step. In this paper, to tackle this issue, among others, a modified LSMR method (MLSMR), in which only one linear system per iterative step needs to be solved instead of two, is introduced, and then it is integrated with the idea of flexible GMRES to yield a flexible MLSMR method (FMLSMR). Numerical examples are presented to demonstrate the efficiency of the proposed FMLSMR method.

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Publication Details

Journal
Numerical Algebra Control and Optimization
Published
2025-01-01
DOI
https://doi.org/10.3934/naco.2025019
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Flexible modified LSMR for least squares problems

Ren‐Cang Li, Gül Karaduman, Mei Yang
Numerical Algebra Control and Optimization
Matrix Theory and Algorithms
article

Flexible modified LSMR for least squares problems

Ren‐Cang Li, Gül Karaduman, Mei Yang
article en

Abstract

LSMR is a widely recognized method for solving least squares problems via the double QR decomposition. Various preconditioning techniques have been explored to improve its efficiency. One issue that arises when implementing these preconditioning techniques is the need to solve two linear systems per iterative step. In this paper, to tackle this issue, among others, a modified LSMR method (MLSMR), in which only one linear system per iterative step needs to be solved instead of two, is introduced, and then it is integrated with the idea of flexible GMRES to yield a flexible MLSMR method (FMLSMR). Numerical examples are presented to demonstrate the efficiency of the proposed FMLSMR method.

Numerical Algebra Control and OptimizationVol. 15(4)
The University of Texas at Arlington (US), Karamanoğlu Mehmetbey University (TR)
National Science Foundation
Openalex Percentile: Top 83%
Matrix Theory and Algorithms
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