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

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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.

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Cite This Study

Paige et al. (Mon,) studied this question.

synapsesocial.com/papers/6a026b3c409fa9622d51273a https://doi.org/https://doi.org/10.1145/355984.355989

Discussion

Journals

ACM Transactions on Mathematical Software

Institutions

Stanford University

McGill University

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