Search from over 60,000 research works

Advanced Search

A null-space approach for large-scale symmetric saddle point systems with a small and non zero (2,2) block

[thumbnail of Open Access]
Preview
Available under license: Creative Commons Attribution
[thumbnail of null_final_springer.pdf]
null_final_springer.pdf - Accepted Version (6MB)
Restricted to Repository staff only
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Scott, J. orcid id iconORCID: https://orcid.org/0000-0003-2130-1091 and Tuma, M. (2022) A null-space approach for large-scale symmetric saddle point systems with a small and non zero (2,2) block. Numerical Algorithms, 90. pp. 1639-1667. ISSN 1572-9265 doi: 10.1007/s11075-021-01245-z

Abstract/Summary

Null-space methods have long been used to solve large sparse n x n symmetric saddle point systems of equations in which the (2,2) block is zero. This paper focuses on the case where the (1,1) block is ill conditioned or rank deficient and the k x k (2,2) block is non zero and small (k << n). Additionally, the (2,1) block may be rank deficient. Such systems arise in a range of practical applications. A novel null-space approach is proposed that transforms the system matrix into a nicer symmetric saddle point matrix of order n that has a non zero (2,2) block of order at most 2k and, importantly, the(1,1)$ block is symmetric positive definite. Success of any null-space approach depends on constructing a suitable null-space basis. We propose methods for wide matrices having far fewer rows than columns with the aim of balancing stability of the transformed saddle point matrix with preserving sparsity in the (1,1) block. Linear least squares problems that contain a small number of dense rows are an important motivation and are used to illustrate our ideas and to explore their potential for solving large-scale systems.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/102016
Item Type Article
Refereed Yes
Divisions Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher Springer
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar