Polytopes and Machine Learning
Combinatorics
2024-02-16 v1 High Energy Physics - Theory
Algebraic Geometry
Abstract
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 Pl\"ucker coordinates as input, which out-perform the usual vertex representation.
Cite
@article{arxiv.2109.09602,
title = {Polytopes and Machine Learning},
author = {Jiakang Bao and Yang-Hui He and Edward Hirst and Johannes Hofscheier and Alexander Kasprzyk and Suvajit Majumder},
journal= {arXiv preprint arXiv:2109.09602},
year = {2024}
}
Comments
33 pages, 26 figures