67 citations · 107 across the 27 of their papers we have counts for
6 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…
Adaptive randomized pivoting and volume sampling
Ethan N. Epperly
Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connectio…
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…
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…
Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product
Chris Camaño, Ethan N. Epperly, Joel A. Tropp
Tensor networks like matrix product states (MPSs) and matrix product operators (MPOs) are powerful tools for representing exponentially large states and operators, with application…
Stable algorithms for general linear systems by preconditioning the normal equations
Ethan N. Epperly, Anne Greenbaum, Yuji Nakatsukasa
This paper studies the solution of nonsymmetric linear systems by preconditioned Krylov methods based on the normal equations, LSQR in particular. On some examples, preconditioned…