Accession Number:

ADA063334

Title:

A Bidiagonalization Algorithm for Sparse Linear Equations and Least-Squares Problems.

Personal Author(s):

Corporate Author:

STANFORD UNIV CALIF SYSTEMS OPTIMIZATION LAB

Report Date:

1978-10-01

Abstract:

A method is given for solving Ax b and min value of Ax-b sub 2 where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytical equivalent to the method of conjugate gradients CG but possesses more favorable numerical properties. The Fortran implementation of the method subroutine LSQR incorporates reliable stopping criteria and provides estimates of various quantities including standard errors for x and the condition number of A. Numerical tests are described comparing LSQR with several other CG algorithms. Further results for a large practical problem illustrate the effect of pre-conditioning least-squares problems using a sparse LU factorization of A. Author

Descriptive Note:

Technical rept.,

Supplementary Note:

Sponsored in part by Grants NSF-ENG77-06761, EY-76-S-03-0326.

Pages:

0095

Descriptors:

Identifiers:

Subject Categories:

Communities Of Interest:

Contract Number:

N00014-75-C-0267

Contract Number 2:

NSF-MCS76-20019

File Size:

28.87MB

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