Room 526, Astronomy-Mathematics Building, NTU

(台灣大學天文數學館 526室)

Geometry-Oblivious FMM for Compressing Dense SPD Matrices

Chen-Han Yu (University of Texas at Austin)

Abstract

We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, or “compression,” of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in NlogN or even N time, where N is the matrix size. Compression requires NlogN storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term “geometry-oblivious.” In this presentation, we also present results of a variety of SPD matrices.