Krylov subspace recycling with randomized sketching for matrix functions
A Krylov subspace recycling method for the efficient evaluation of a sequence of matrix
functions acting on a set of vectors is developed. The method improves over the recycling …
functions acting on a set of vectors is developed. The method improves over the recycling …
Randomized Implicitly Restarted Arnoldi method for the non-symmetric eigenvalue problem
JG de Damas, L Grigori - arXiv preprint arXiv:2407.03208, 2024 - arxiv.org
In this paper, we introduce a randomized algorithm for solving the non-symmetric
eigenvalue problem, referred to as randomized Implicitly Restarted Arnoldi (rIRA). This …
eigenvalue problem, referred to as randomized Implicitly Restarted Arnoldi (rIRA). This …
Convergence Estimate of Minimal Residual Methods and Random Sketching of Krylov Subspace Methods
P Westerbaan - 2024 - tigerprints.clemson.edu
This study concerns two main issues in numerical linear algebra: convergence estimate of
minimal residual methods based on explicit construction of approximate min-max …
minimal residual methods based on explicit construction of approximate min-max …
STABILIZED SHORT-TERM RECURRENCES VIA RANDOMIZED SKETCHING
V Simoncini, Y Wang - 2024 - hal.science
Recurrences building orthonormal bases for polynomial Krylov spaces have been
classically used for approximation purposes in various numerical linear algebra contexts …
classically used for approximation purposes in various numerical linear algebra contexts …
Randomized orthogonalization process with reorthogonalization
The dimension reduction technique of random sketching is advantageous in significantly
reducing computational complexity. In orthogonalization processes like the Gram-Schmidt …
reducing computational complexity. In orthogonalization processes like the Gram-Schmidt …