Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Timsit, Edouard, Grigori, Laura, Balabanov, Oleg
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
Awards: G434337789, G7683707404
OpenAlex ID: W4321162279