A randomized progressive iterative regularization method for data fitting problems
Numerical Analysis
2025-06-05 v1 Numerical Analysis
Abstract
In this work, we investigate data fitting problems with random noises. A randomized progressive iterative regularization method is proposed. It works well for large-scale matrix computations and converges in expectation to the least-squares solution. Furthermore, we present an optimal estimation for the regularization parameter, which inspires the construction of self-consistent algorithms without prior information. The numerical results confirm the theoretical analysis and show the performance in curve and surface fittings.
Cite
@article{arxiv.2506.03526,
title = {A randomized progressive iterative regularization method for data fitting problems},
author = {Dakang Cen and Wenlong Zhang and Junbin Zhong},
journal= {arXiv preprint arXiv:2506.03526},
year = {2025}
}
Comments
28 pages,31 figures