@inproceedings{44cd17431e2b49c18f2a95f7c1c082df,
title = "Incremental condition estimator for parallel sparse matrix factorization",
abstract = "There is often a trade-off between preserving sparsity and numerical stability in sparse matrix factorizations. In applications like the direct solution of Equality Constrained Least Squares problem, the accurate detection of the rank of a large and sparse constraint matrix is a key issue. Column pivoting is not suitable for distributed memory machines because it forces the program into a lock-step mode, preventing any overlapping of computations. So factorization algorithms on such machines need to use a reliable, yet inexpensive incremental condition estimator to decide on which columns to be included. We describe an incremental condition estimator that can be used during a sparse QR factorization. We show that it is quite reliable and is well suited for use on parallel machines. We supply experimental results to support its effectiveness as well as suitability for parallel architectures.",
author = "Barlow, {J. L.} and Vemulapati, {U. B.}",
year = "1990",
month = jan,
day = "1",
doi = "10.1109/DMCC.1990.555401",
language = "English (US)",
series = "Proceedings of the 5th Distributed Memory Computing Conference, DMCC 1990",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "322--327",
editor = "Walker, {David W.} and Stout, {Quentin F.}",
booktitle = "Applications",
address = "United States",
note = "5th Distributed Memory Computing Conference, DMCC 1990 ; Conference date: 08-04-1990 Through 12-04-1990",
}