Polytopes and Machine Learning
Fuente:
arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866913234596921344 |
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| author | Bao, Jiakang He, Yang-Hui Hirst, Edward Hofscheier, Johannes Kasprzyk, Alexander Majumder, Suvajit |
| author_facet | Bao, Jiakang He, Yang-Hui Hirst, Edward Hofscheier, Johannes Kasprzyk, Alexander Majumder, Suvajit |
| contents | We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Plücker coordinates as input, which out-perform the usual vertex representation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_09602 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Polytopes and Machine Learning Bao, Jiakang He, Yang-Hui Hirst, Edward Hofscheier, Johannes Kasprzyk, Alexander Majumder, Suvajit Combinatorics High Energy Physics - Theory Algebraic Geometry We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Plücker coordinates as input, which out-perform the usual vertex representation. |
| title | Polytopes and Machine Learning |
| topic | Combinatorics High Energy Physics - Theory Algebraic Geometry |
| url | https://arxiv.org/abs/2109.09602 |