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arXiv · 2506.03526

A randomized progressive iterative regularization method for data fitting problems

Dakang Cen·Wenlong Zhang·Junbin Zhong
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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.

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Dakang Cen, Wenlong Zhang, Junbin Zhong. 2025-06-04. A randomized progressive iterative regularization method for data fitting problems. https://arxiv.org/abs/2506.03526

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