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Auteurs principaux: Lu, Susie, Urschel, John
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2508.05760
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author Lu, Susie
Urschel, John
author_facet Lu, Susie
Urschel, John
contents Given a directed graph $G$, the spread of $G$ is the largest distance between any two eigenvalues of its adjacency matrix. In 2022, Breen, Riasanovsky, Tait, and Urschel asked what $n$-vertex directed graph maximizes spread, and whether this graph is undirected. We prove the more general result that the spread of any $n \times n$ non-negative matrix $A$ with $\|A\|_{\max} \le 1$ is at most $2n/\sqrt{3}$, which is tight up to an additive factor and exact when $n$ is a multiple of three. Furthermore, our results show that the matrix with maximum spread is always symmetric.
format Preprint
id arxiv_https___arxiv_org_abs_2508_05760
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On the Maximum Spread of Non-Negative Matrices
Lu, Susie
Urschel, John
Combinatorics
05C50
Given a directed graph $G$, the spread of $G$ is the largest distance between any two eigenvalues of its adjacency matrix. In 2022, Breen, Riasanovsky, Tait, and Urschel asked what $n$-vertex directed graph maximizes spread, and whether this graph is undirected. We prove the more general result that the spread of any $n \times n$ non-negative matrix $A$ with $\|A\|_{\max} \le 1$ is at most $2n/\sqrt{3}$, which is tight up to an additive factor and exact when $n$ is a multiple of three. Furthermore, our results show that the matrix with maximum spread is always symmetric.
title On the Maximum Spread of Non-Negative Matrices
topic Combinatorics
05C50
url https://arxiv.org/abs/2508.05760