Randomized low-rank Runge-Kutta methods
This work proposes and analyzes a new class of numerical integrators for computing low-
rank approximations to solutions of matrix differential equation. We combine an explicit …
rank approximations to solutions of matrix differential equation. We combine an explicit …
Distributed Local Sketching for£ 2 Embeddings
N Charalambides, A Mazumdar - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In this work, we show that if local datasets in a distributed network are appropriately
compressed and then aggregated, it can result in a compressed version of the union of the …
compressed and then aggregated, it can result in a compressed version of the union of the …
Algorithm xxx: Faster Randomized SVD with Dynamic Shifts
Aiming to provide a faster and convenient truncated SVD algorithm for large sparse matrices
from real applications (ie for computing a few of largest singular values and the …
from real applications (ie for computing a few of largest singular values and the …
Distributed Hybrid Sketching for -Embeddings
N Charalambides, A Mazumdar - arXiv preprint arXiv:2412.20301, 2024 - arxiv.org
Linear algebraic operations are ubiquitous in engineering applications, and arise often in a
variety of fields including statistical signal processing and machine learning. With …
variety of fields including statistical signal processing and machine learning. With …