Incremental condition estimator for parallel sparse matrix factorization

J. L. Barlow, U. B. Vemulapati

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationApplications
EditorsDavid W. Walker, Quentin F. Stout
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0818621133, 9780818621130
StatePublished - Jan 1 1990
Event5th Distributed Memory Computing Conference, DMCC 1990 - Charleston, United States
Duration: Apr 8 1990Apr 12 1990

Publication series

NameProceedings of the 5th Distributed Memory Computing Conference, DMCC 1990


Conference5th Distributed Memory Computing Conference, DMCC 1990
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Information Systems and Management
  • Computer Networks and Communications


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