期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 32, 期 3, 页码 1217-1236出版社
SIAM PUBLICATIONS
DOI: 10.1137/090767911
关键词
dense linear least squares; randomized numerical linear algebra; randomized pre-conditioners
资金
- IBM
- Israel Science Foundation [1045/09]
Several innovative random-sampling and random-mixing techniques for solving problems in linear algebra have been proposed in the last decade, but they have not yet made a significant impact on numerical linear algebra. We show that by using a high-quality implementation of one of these techniques, we obtain a solver that performs extremely well in the traditional yardsticks of numerical linear algebra: it is significantly faster than high-performance implementations of existing state-of-the-art algorithms, and it is numerically backward stable. More specifically, we describe a least-squares solver for dense highly overdetermined systems that achieves residuals similar to those of direct QR factorization-based solvers (lapack), outperforms lapack by large factors, and scales significantly better than any QR-based solver.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据