1 citations · 1 across the 10 of their papers we have counts for
9 papers · 1 filter
Make the most of what you have: Resource-efficient randomized algorithms for matrix computations
Ethan N. Epperly
In recent years, randomized algorithms have established themselves as fundamental tools in computational linear algebra, with applications in scientific computing, machine learning…
Does block size matter in randomized block Krylov low-rank approximation?
Tyler Chen, Ethan N. Epperly, Raphael A. Meyer +2
We study the problem of computing a rank- approximation of a matrix using randomized block Krylov iteration. Prior work has shown that, for block size or , a $(1…
Robust, randomized preconditioning for kernel ridge regression
Mateo DÃaz, Mateo Díaz, Ethan N. Epperly +3
We investigate preconditioned conjugate gradient methods for kernel ridge regression (KRR) problems with a moderate to large number of data points (). We dev…
Faster Linear Algebra Algorithms with Structured Random Matrices
Chris Camaño, Ethan N. Epperly, Raphael A. Meyer +1
To achieve the greatest possible speed, practitioners regularly implement randomized algorithms for low-rank approximation and least-squares regression with structured dimension re…
Fast randomized least-squares solvers can be just as accurate and stable as classical direct solvers
Ethan N. Epperly, Maike Meier, Yuji Nakatsukasa
One of the greatest success stories of randomized algorithms for linear algebra has been the development of fast, randomized algorithms for highly overdetermined linear least-squar…
Superfast direct inversion of the nonuniform discrete Fourier transform via hierarchically semi-separable least squares
Heather Wilber, Ethan N. Epperly, Alex H. Barnett
A direct solver is introduced for solving overdetermined linear systems involving nonuniform discrete Fourier transform matrices. Such matrices can be transformed into a Cauchy-lik…