Systems Optimization Laboratory
Stanford, CA 94305-4121 USA
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covLSQR: Sparse Equations and Least Squares
- AUTHORS:
Ekaterina Kostina,
Michael Saunders,
Inga Schierle.
- CONTRIBUTORS: Chris Paige.
- CONTENTS: Implementation of a conjugate-gradient type method
for solving sparse linear equations and
sparse least-squares problems: Solve
Ax = b
or minimize || Ax - b ||2
or minimize || Ax - b ||2 + d2 ||x||2
the same as LSQR.
Special feature:
LSQR and covLSQR can estimate the diagonal of
C = (A'A + d2 I)
-1.
covLSQR can also estimate any principal submatrix
of C. It was developed to estimate the parameter covariance
matrix for large parameter estimation problems with nonlinear
equality constraints, as described in
Technical Report SOL 2009-1 (see below).
- REFERENCES:
E. Kostina, M. A. Saunders, and I. Schierle,
Computation of covariance matrices
for constrained parameter estimation problems using LSQR,
Report SOL 2009-1, 11 pages.
- RELEASE:
14 Jun 2009: Technical report.
24 Dec 2010: Matlab files.
DOWNLOADS:
- Matlab files
Contributed 24 Dec 2010 by Michael Saunders (saunders@stanford.edu)
and Inga Schierle (inga.schierle@gmx.de).
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