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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/2501.10607 |
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| _version_ | 1866914218093051904 |
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| author | Hoehner, Steven Kur, Gil |
| author_facet | Hoehner, Steven Kur, Gil |
| contents | Given $N$ geodesic caps on the unit sphere in $\mathbb{R}^d$, and whose total normalized surface area sums to one, what is the maximal surface area their union can cover? In this work, we provide an asymptotically sharp upper bound for an antipodal partial covering of the sphere by $N \in (ω(1),e^{o(\sqrt{d})})$ congruent caps, showing that the maximum proportion covered approaches $1 - e^{-1}$ as $d\to\infty$. We discuss the relation of this result to the optimality of random polytopes in high dimensions, the limitations of our technique via the Gaussian surface area bounds of K. Ball and F. Nazarov, and its applications in computer science theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Optimality of Random Partial Sphere Coverings in High Dimensions Hoehner, Steven Kur, Gil Metric Geometry Information Theory Functional Analysis 52C17 (52A27) Given $N$ geodesic caps on the unit sphere in $\mathbb{R}^d$, and whose total normalized surface area sums to one, what is the maximal surface area their union can cover? In this work, we provide an asymptotically sharp upper bound for an antipodal partial covering of the sphere by $N \in (ω(1),e^{o(\sqrt{d})})$ congruent caps, showing that the maximum proportion covered approaches $1 - e^{-1}$ as $d\to\infty$. We discuss the relation of this result to the optimality of random polytopes in high dimensions, the limitations of our technique via the Gaussian surface area bounds of K. Ball and F. Nazarov, and its applications in computer science theory. |
| title | On the Optimality of Random Partial Sphere Coverings in High Dimensions |
| topic | Metric Geometry Information Theory Functional Analysis 52C17 (52A27) |
| url | https://arxiv.org/abs/2501.10607 |