图 书 馆
Fast algorithms from low-rank updates{来自低秩更新的快速算法}Fast algorithms from low-rank updates{来自低秩更新的快速算法} |
|
| 课程网址: | http://videolectures.net/8ecm2021_kressner_fast_updates/ |
| 主讲教师: | Daniel Kressner, |
| 开课单位: | 洛桑联邦理工学院 |
| 开课时间: | 2021-07-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The development of efficient numerical algorithms for solving large-scale linear systems is one of the success stories of numerical linear algebra that has had a tremendous impact on our ability to perform complex numerical simulations and large-scale statistical computations. Many of these developments are based on multilevel and domain decomposition techniques, which are closely linked to Schur complements and low-rank updates of matrices. In this talk, we explain how these tools carry over to other important linear algebra problems, including matrix functions and matrix equations. Fast algorithms are derived from combining divide-and-conquer strategies with low-rank updates of matrix functions. The convergence analysis of these algorithms is built on a multivariate extension of the celebrated CrouzeixPalencia result. The newly developed algorithms are capable of addressing a wide variety of matrix functions and matrix structures, including sparse matrices as well as matrices with hierarchical low rank and Toeplitz-like structures. Their versatility will be demonstrated with several applications and extensions. This talk is based on joint work with Bernhard Beckermann, Alice Cortinovis, Leonardo Robol, Stefano Massei, and Marcel Schweitzer. |
| 关 键 词: | 线性系统; 数值模拟; 矩阵函数 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-03-24：hqh |
| 最后编审: | 2022-03-25：liyy |
| 阅读次数: | 63 |