@article{oai:repository.nii.ac.jp:00000394,
author = {伊藤, 徳史 and Ito, Tokushi and 速水, 謙 and Hayami, Ken},
journal = {NIIテクニカル・レポート, NII Technical Report},
month = {May},
note = {For least squares problems of minimizing || b - A x ||_2 where A is a large sparse m x n (m >= n) matrix, the common method is to apply the conjugate gradient method to the normal equation A^T A x = A^T b. However, the condition number of A^T A is square of that of A, and convergence becomes problematic for severely ill-conditioned problems even with preconditioning. In this paper, we propose two methods for applying the GMRES method to the least squares problem by using a n x m matrix B. We give the necessary and sufficient condition that B should satisfy in order that the proposed methods give a least squares solution. Then, for implementations for B, we propose an incomplete QR decomposition IMGS(l). Numerical experiments show that the simplest case l=0, which is equivalent to B= ( diag (A^T A) )^(-1) A^T, gives best results, and converges faster than previous methods for severely ill-conditioned problems.},
pages = {1--29},
title = {NII Technical Report (NII-2004-006E)：Preconditioned GMRES Methods for Least Squares Problems},
year = {2004}
}