Four algorithms for the the efficient computation of truncated pivoted QR approximations to a sparse matrix

被引：82

作者：

Stewart, GW ^{[1
]}

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

[2] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA

来源：

NUMERISCHE MATHEMATIK | 1999年 / 83卷 / 02期

关键词：

Mathematics Subject Classification (1991):65F20, 65F50;

D O I：

10.1007/s002110050451

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper we propose four algorithms to compute truncated pivoted QR approximations to a sparse matrix. Three are based on the Gram-Schmidt algorithm and the other on Householder triangularization. All four algorithms leave the original matrix unchanged, and the only additional storage requirements are arrays to contain the factorization itself, Thus, the: algorithms are particularly suited to determining tow-rank approximations to a sparse matrix.

引用

页码：313 / 323

页数：11