Work

Polytopes and machine learning

Year: 2023

Type: article

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, and reflexivity with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Plücker coordinates as input, which outperform the usual vertex representation.

Source: International Journal of Data Science in the Mathematical Sciences

DOI: 10.1142/s281093922350003x

Authors: Jiakang Bao, Yang-Hui He, Edward Hirst, Johannes Hofscheier, Alexander Kasprzyk, Suvajit Majumder

Institutions: Royal Institution of Great Britain, City, University of London, London Institute for Mathematical Sciences, Nankai University, University of Oxford, University of Nottingham

Language: English

Topic: Advanced Combinatorial Mathematics

Subfield: Discrete Mathematics and Combinatorics

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: China Scholarship Council, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council

Awards: G1549407930, G2089456827, G432590889, The Combinatorics of Mirror Symmetry, G5545648687, G6701890374, Theoretical Particle Physics at City University, G8842548244

OpenAlex ID: W4391011679

FWCI: 4.616

Cites: 30

Cited by: 12

Related to: 10