Random symmetric matrices: rank distribution and irreducibility of the characteristic polynomial

Asaf Ferber, Vishesh Jain, A. Sah, Mehtaab Sawhney
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引用次数: 9

Abstract

Abstract Conditional on the extended Riemann hypothesis, we show that with high probability, the characteristic polynomial of a random symmetric $\{\pm 1\}$ -matrix is irreducible. This addresses a question raised by Eberhard in recent work. The main innovation in our work is establishing sharp estimates regarding the rank distribution of symmetric random $\{\pm 1\}$ -matrices over $\mathbb{F}_p$ for primes $2 < p \leq \exp(O(n^{1/4}))$ . Previously, such estimates were available only for $p = o(n^{1/8})$ . At the heart of our proof is a way to combine multiple inverse Littlewood–Offord-type results to control the contribution to singularity-type events of vectors in $\mathbb{F}_p^{n}$ with anticoncentration at least $1/p + \Omega(1/p^2)$ . Previously, inverse Littlewood–Offord-type results only allowed control over vectors with anticoncentration at least $C/p$ for some large constant $C > 1$ .
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随机对称矩阵:秩分布和特征多项式的不可约性
摘要在扩展Riemann假设的条件下,我们证明了随机对称$\{\pm 1\}$ -矩阵的特征多项式在大概率下是不可约的。这解决了Eberhard在最近的工作中提出的一个问题。我们工作中的主要创新是建立关于对称随机$\{\pm 1\}$ -矩阵在$\mathbb{F}_p$上对质数$2 < p \leq \exp(O(n^{1/4}))$的秩分布的尖锐估计。以前,只有$p = o(n^{1/8})$才能得到这种估计。我们证明的核心是一种结合多个逆littlewood - ford型结果的方法,以控制$\mathbb{F}_p^{n}$中向量的奇异型事件的贡献,至少具有$1/p + \Omega(1/p^2)$的反集中。以前,逆littlewood - ford型结果只允许控制至少$C/p$对于一些大常数$C > 1$具有反浓度的向量。
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Papers which advance knowledge of mathematics, either pure or applied, will be considered by the Editorial Committee. The work must be original and not submitted to another journal.
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