Scalable Binary CUR Low-Rank Approximation Algorithm
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917944396611584 |
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| author | Su, Bowen |
| author_facet | Su, Bowen |
| contents | This paper proposes a scalable binary CUR low-rank approximation algorithm that leverages parallel selection of representative rows and columns within a deterministic framework. By employing a blockwise adaptive cross approximation strategy, the algorithm efficiently identifies dominant components in large-scale matrices, thereby reducing computational costs. Numerical experiments on $16,384 \times 16,384$ matrices demonstrate a good speed-up, with execution time decreasing from $12.37$ seconds using $2$ processes to $1.02$ seconds using $64$ processes. The tests on Hilbert matrices and synthetic low-rank matrices of different size across various sizes demonstrate an near-optimal reconstruction accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Binary CUR Low-Rank Approximation Algorithm Su, Bowen Numerical Analysis Performance This paper proposes a scalable binary CUR low-rank approximation algorithm that leverages parallel selection of representative rows and columns within a deterministic framework. By employing a blockwise adaptive cross approximation strategy, the algorithm efficiently identifies dominant components in large-scale matrices, thereby reducing computational costs. Numerical experiments on $16,384 \times 16,384$ matrices demonstrate a good speed-up, with execution time decreasing from $12.37$ seconds using $2$ processes to $1.02$ seconds using $64$ processes. The tests on Hilbert matrices and synthetic low-rank matrices of different size across various sizes demonstrate an near-optimal reconstruction accuracy. |
| title | Scalable Binary CUR Low-Rank Approximation Algorithm |
| topic | Numerical Analysis Performance |
| url | https://arxiv.org/abs/2502.11017 |