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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04264 |
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| _version_ | 1866909770612473856 |
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| author | Machado, Ananias Sousa Fampa, Marcia Lee, Jon |
| author_facet | Machado, Ananias Sousa Fampa, Marcia Lee, Jon |
| contents | We give sparsity results and present algorithms for calculating minimum (vector) 1-norm universal solvers connected to least-squares problems. In particular, besides universal least-squares solvers, we consider minimum-rank universal least-squares solvers, and simultaneous universal minimum-norm/least-squares solvers. For all of these, we present and compare several new alternative linear-optimization formulations and very effective proximal-point algorithms. Overall, we found that our new Douglas-Rachford splitting algorithms for these problems performed best. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On computing sparse universal solvers for key problems in statistics Machado, Ananias Sousa Fampa, Marcia Lee, Jon Optimization and Control We give sparsity results and present algorithms for calculating minimum (vector) 1-norm universal solvers connected to least-squares problems. In particular, besides universal least-squares solvers, we consider minimum-rank universal least-squares solvers, and simultaneous universal minimum-norm/least-squares solvers. For all of these, we present and compare several new alternative linear-optimization formulations and very effective proximal-point algorithms. Overall, we found that our new Douglas-Rachford splitting algorithms for these problems performed best. |
| title | On computing sparse universal solvers for key problems in statistics |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.04264 |