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arXiv · 2109.09602

Polytopes and Machine Learning

Jiakang Bao·Yang-Hui He·Edward Hirst·Johannes Hofscheier·Alexander Kasprzyk·Suvajit Majumder
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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 Plücker coordinates as input, which out-perform the usual vertex representation.

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Jiakang Bao, Yang-Hui He, Edward Hirst, Johannes Hofscheier, Alexander Kasprzyk, Suvajit Majumder. 2021-09-15. Polytopes and Machine Learning. https://doi.org/10.1142/s281093922350003x

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