The relationship between the features of sparse matrix and the matrix solving status

Authors:
Dianwei Han;Shuting Xu;Jun Zhang
Affiliations:
University of Kentucky, Lexington, KY;Virginia State University, Petersburg, VA;University of Kentucky, Lexington, KY
Venue:
Proceedings of the 46th Annual Southeast Regional Conference on XX
Year:
2008

Citing 4
Cited 0

Iterative methods for solving linear systems

Iterative methods for solving linear systems
Parallel threshold-based ILU factorization

SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Iterative Methods for Sparse Linear Systems

Iterative Methods for Sparse Linear Systems
A short survey on preconditioning techniques for large-scale dense complex linear systems in electromagnetics

International Journal of Computer Mathematics - Fast Iterative and Preconditioning Methods for Linear and Non-Linear Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Solving very large sparse linear systems are often encountered in many scientific and engineering applications. Generally there are two classes of methods available to solve the sparse linear systems. The first class is the direct solution methods, represented by the Gauss elimination method. The second class is the iterative solution methods, of which the preconditioned Krylov subspace methods are considered to be the most effective ones currently available in this field. The sparsity structure and the numerical value distribution which are considered as features of the sparse matrices may have important effect on the iterative solution of linear systems. We first extract the matrix features, and then preconditioned iterative methods are used to the linear system. Our experiments show that a few features that may affect, positively or negatively, the solving status of a sparse matrix with the level-based preconditioners.