6 citations · 11 across the 3 of their papers we have counts for
4 papers
Randomized Block Low-Rank Matrix Compression by Tagging
Katherine J. Pearce, Anna Yesypenko, James Levitt +1
In this work, we present randomized compression algorithms for flat rank-structured matrices with shared bases, termed uniform Block Low-Rank (BLR) matrices. Our main contribution…
Randomized Compression of Rank-Structured Matrices Accelerated with Graph Coloring
James Levitt, Per-Gunnar Martinsson
A randomized algorithm for computing a data sparse representation of a given rank structured matrix (a.k.a. an -matrix) is presented. The algorithm draws on the randomized s…
Linear-Complexity Black-Box Randomized Compression of Rank-Structured Matrices
James Levitt, Per-Gunnar Martinsson
A randomized algorithm for computing a compressed representation of a given rank-structured matrix is presented. The algorithm interacts with onl…
Geometry-Oblivious FMM for Compressing Dense SPD Matrices
Chenhan D. Yu, James Levitt, Severin Reiz +1
We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, "compression," of an arbitrary dense symmetric positive definite (SPD)…