Machine Learning Mutation-Acyclicity of Quivers

TL;DR

This paper demonstrates that machine learning models, including neural networks and support vector machines, can effectively classify mutation-acyclicity in 4-vertex quivers, aiding mathematical research in algebra and combinatorics.

Contribution

It introduces a novel ML-based approach to classify mutation-acyclicity in quivers with more than 3 vertices, providing a computational tool for a previously difficult problem.

Findings

ML models accurately classify mutation-acyclicity in 4-vertex quivers

Support vector machines provide interpretable decision boundaries

ML approaches offer a promising computational method for algebraic classification

Abstract

Machine learning (ML) has emerged as a powerful tool in mathematical research in recent years. This paper applies ML techniques to the study of quivers -- a type of directed multigraph with significant relevance in algebra, combinatorics, computer science, and mathematical physics. Specifically, we focus on the challenging problem of determining the mutation-acyclicity of a quiver on 4 vertices, a property that is pivotal since mutation-acyclicity is often a necessary condition for theorems involving path algebras and cluster algebras. Although this classification is known for quivers with at most 3 vertices, little is known about quivers on more than 3 vertices. We give a computer-assisted proof of a theorem to prove that mutation-acyclicity is decidable for quivers on 4 vertices with edge weight at most 2. By leveraging neural networks (NNs) and support vector machines (SVMs), we then accurately classify more general 4-vertex quivers as mutation-acyclic or non-mutation-acyclic. Our results demonstrate that ML models can efficiently detect mutation-acyclicity, providing a promising computational approach to this combinatorial problem, from which the trained SVM equation provides a starting point to guide future theoretical development.