LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares

19824,341 citationsJournal Articlebronze Open Access

Authors

Christopher C. Paige · McGill University

Michael A. Saunders · Stanford University

Abstract

An iterative method is given for solving Ax ~ffi b and minU Ax -b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerical properties.

Topics & Keywords

Matrix Theory and Algorithms Numerical Methods and Algorithms Advanced Optimization Algorithms Research

Publication Details

Published in: ACM Transactions on Mathematical Software

Volume 8, Issue 1, pp. 43-71

DOI: 10.1145/355984.355989

Field-Weighted Citation Impact: 8.27

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LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares

Authors

Abstract

Topics & Keywords

Publication Details