Transcription of LSQR: An Algorithm for Sparse Linear Equations and …
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LSQR: An Algorithm for Sparse Linear Equations and …
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares CHRISTOPHER C. PAIGE McGill University, Canada and MICHAEL A. SAUNDERS Stanford University 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. Reliable stopping criteria are derived, along with estimates of standard errors for x and the condition number of A. These are used in the FORTRAN implementation of the method, subroutine LSQR. Numerical tests are described comparing I~QR with several other conjugate-gradient algori- thms, indicating that I~QR is the most reliable Algorithm when A is ill-conditioned.
pseudoinverse of A. The relative precision of floating-point arithmetic is e, the smallest machine-representable number such that 1 + e > 1. 2. MOTIVATION VIA THE LANCZOS PROCESS In this section we review the symmetric Lanczos process [13] and its use in solving symmetric linear equations Bx = b.
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