Speaker
Sherry Li
(LBNL)
Description
Hierarchically low-rank (H-LR) matrices have been widely used to design fast solvers for integral equations, boundary element methods, discretized PDEs, and kernel matrices in statistical and machine learning. The computational bottleneck in these solvers is often the construction algorithm which converts a standard dense matrix into an H-LR format. We will present two types of algorithms for fast construction of H-LR matrices. One type of algorithm exploits geometric information that works well with high dimensional data from ML applications. Another type of algorithm is purely algebraic, which works well for building general-purpose linear solvers. In both algorithms, randomization plays a significant role.
Author
Sherry Li
(LBNL)