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LSQR
Solves large, sparse linear least squares problem, as well as unsymmetric
linear systems.
Main features
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LSQR solves any of the three problems below
or min ||b - Ax||_2 if damp = 0:
| Problem |
Comments |
| Ax = b |
Matrix A should be large, sparse and non-symmetric. |
| min ||b - Ax||_2 |
Large, sparse linear least squares problem. |
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min || (b ; 0) - ( A; damp*I ) x ||_2 |
Damped large, sparse linear least squares problem. |
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LSQR uses an iterative (conjugate-gradient-like) method.
For further information, see
the references in the table below:
| Authors |
Title |
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1. C. C. Paige and M. A. Saunders (1982a).
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LSQR: An algorithm for sparse linear equations and sparse least squares,
ACM TOMS 8(1), 43-71.
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2. C. C. Paige and M. A. Saunders (1982b).
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Algorithm 583. LSQR: Sparse linear equations and least squares problems,
ACM TOMS 8(2), 195-209.
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3. M. A. Saunders (1995).
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Solution of sparse rectangular systems using
LSQR and CRAIG, BIT 35, 588-604.
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Operations with both dense and sparse Matlab
matrices are efficiently implemented.
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