Fast Construction of Hierarchically Low-Rank Matrices Using Randomized Sketching
Computational Mathematics, Linear Algebra and seminar by Sherry Xiaoye Li, Lawrence Berkeley National Laboratory
Hosted by Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University
Tuesday 14:30 New York (GMT-5)
Recording available
Abstract
Sherry Xiaoye Li surveys randomized construction of hierarchically low-rank matrices, including H/H2, HODLR, HSS, and butterfly formats with different off-diagonal structures. Applications include integral equations, boundary elements, discretized PDEs, and statistical or machine-learning kernel matrices, using either iterative matrix-vector products or direct factorization and solves. Constructing these representations from an implicit dense operator is often the main cost. The talk offers a unified view of sketch distributions, sketch sizes, approximation error bounds, high-performance implementation, applications, and open questions.