On solving large-scale limited-memory quasi-Newton equations
Abstract
We consider the problem of solving linear systems of equations arising with limited-memory members of the restricted Broyden class of updates and the symmetric rank-one (SR1) update. In this paper, we propose a new approach based on a practical implementation of the compact representation for the inverse of these limited-memory matrices. Numerical results suggest that the proposed method compares favorably in speed and accuracy to other algorithms and is competitive with several update-specific methods available to only a few members of the Broyden class of updates. Using the proposed approach has an additional benefit: The condition number of the system matrix can be computed efficiently.
Cite
@article{arxiv.1510.06378,
title = {On solving large-scale limited-memory quasi-Newton equations},
author = {Jennifer B. Erway and Roummel F. Marcia},
journal= {arXiv preprint arXiv:1510.06378},
year = {2016}
}
Comments
Technical Report 2015-2, Wake Forest University (2015)