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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.23731 |
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| _version_ | 1866916039646773248 |
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| author | Bidoia, Andrea |
| author_facet | Bidoia, Andrea |
| contents | Caffarelli's contraction theorem and the analogous Laplacian result in [arXiv:2411.12109, arXiv:2501.11382] are two examples of how log-Hessian bounds on probability densities yield estimates on the derivative of the corresponding Brenier map with optimal dimensional dependence. The main goal of this paper is to extend such phenomenon to a broader class of convex estimates such as norms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Global estimates on the Brenier map Bidoia, Andrea Analysis of PDEs Probability Caffarelli's contraction theorem and the analogous Laplacian result in [arXiv:2411.12109, arXiv:2501.11382] are two examples of how log-Hessian bounds on probability densities yield estimates on the derivative of the corresponding Brenier map with optimal dimensional dependence. The main goal of this paper is to extend such phenomenon to a broader class of convex estimates such as norms. |
| title | Global estimates on the Brenier map |
| topic | Analysis of PDEs Probability |
| url | https://arxiv.org/abs/2605.23731 |