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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.06525 |
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| _version_ | 1866911428983652352 |
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| author | Kravtsova, Natalia |
| author_facet | Kravtsova, Natalia |
| contents | This note addresses computational difficulty of the Gromov-Wasserstein distance frequently mentioned in the literature. We provide details on the structure of the Gromov-Wasserstein distance optimization problem that show its non-convex quadratic nature for any instance of an input data. We further illustrate the non-convexity of the problem with several explicit examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Note on computational complexity of the Gromov-Wasserstein distance Kravtsova, Natalia Machine Learning This note addresses computational difficulty of the Gromov-Wasserstein distance frequently mentioned in the literature. We provide details on the structure of the Gromov-Wasserstein distance optimization problem that show its non-convex quadratic nature for any instance of an input data. We further illustrate the non-convexity of the problem with several explicit examples. |
| title | Note on computational complexity of the Gromov-Wasserstein distance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.06525 |