Storage and Solving of Large Sparse Matrix Linear Equations

Author

Liu, Chao ; Ye, Junmin ; Ma, Yining

Author_Institution

Comput. Sci. Dept., Central China Normal Univ., Wuhan, China

fYear

2012

fDate

17-19 Aug. 2012

Firstpage

673

Lastpage

677

Abstract

Solving of large sparse matrix linear equations is always the research focus of scientific and engineering calculation field. With the sparsity and symmetry characteristics of coefficient matrix, Compressed Sparse Row (CSR) is adopted in the storage of large sparse matrix linear equations. Under the condition of CSR, Symmetrica Successive Over Relaxations-Preconditioned Conjugate Gradient method (SSOR-PCG) is employed in the solution of large sparse matrix linear equations.

Keywords

conjugate gradient methods; finite element analysis; sparse matrices; CSR; SSOR-PCG; coefficient matrix sparsity characteristics; coefficient matrix symmetry characteristics; compressed sparse row; large sparse matrix linear equations; scientific and engineering calculation field; symmetrica successive over relaxations-preconditioned conjugate gradient method; Computational modeling; Equations; Finite element methods; Gradient methods; Mathematical model; Sparse matrices; Symmetric matrices; CSR; Finite element calculation; SSOR-PCG; Sparse matrix;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-2406-9

Type

conf

DOI

10.1109/ICCIS.2012.293

Filename

6300623