期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 40, 期 2, 页码 A1149-A1170出版社
SIAM PUBLICATIONS
DOI: 10.1137/17M1123080
关键词
Jacobi-Davidson; eigenvalue problem; low-rank approximation; Riemannian optimization
资金
- RFBR [16-31-00372-mol-a, 16-31-60095-mol-a-dk]
In this work we generalize the Jacobi-Davidson method to the case when eigenvector can be reshaped into a low-rank matrix. In this setting the proposed method inherits advantages of the original Jacobi-Davidson method, has lower complexity and requires less storage. We also introduce low-rank version of the Rayleigh quotient iteration which naturally arises in the Jacobi-Davidson method.
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