Randomized Orthogonal Projection Methods for Krylov Subspace Solvers
Numerical Analysis
2023-03-14 v3 Numerical Analysis
Abstract
Randomized orthogonal projection methods (ROPMs) can be used to speed up the computation of Krylov subspace methods in various contexts. Through a theoretical and numerical investigation, we establish that these methods produce quasi-optimal approximations over the Krylov subspace. Our numerical experiments outline the convergence of ROPMs for all matrices in our test set, with occasional spikes, but overall with a convergence rate similar to that of standard OPMs.
Cite
@article{arxiv.2302.07466,
title = {Randomized Orthogonal Projection Methods for Krylov Subspace Solvers},
author = {Edouard Timsit and Laura Grigori and Oleg Balabanov},
journal= {arXiv preprint arXiv:2302.07466},
year = {2023}
}