A randomized progressive iterative regularization method for data fitting problems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912412816375808 |
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| author | Cen, Dakang Zhang, Wenlong Zhong, Junbin |
| author_facet | Cen, Dakang Zhang, Wenlong Zhong, Junbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A randomized progressive iterative regularization method for data fitting problems Cen, Dakang Zhang, Wenlong Zhong, Junbin Numerical Analysis 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. |
| title | A randomized progressive iterative regularization method for data fitting problems |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2506.03526 |