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Xinchen Hu

Publications and source records attributed to Xinchen Hu.

2 recordsLinked to original sources

Convergence rate of extreme eigenvalue of Ginibre ensembles to Gumbel distribution

Let $X$ be a real $(β=1)$ or complex $(β=2)$ Ginibre ensemble. Let $\{σ_i\}_{1\le i\le n}$ be the eigenvalues of $X,$ and $Z_n$ be some rescaled version of $\max_i \Re σ_i.$ It was proved that $Z_n$ converges weakly to the Gumbel distribution $Λ_β$ with distribution function $e^{-\fracβ{2}e^{-x}}.$ We further prove that $$\sup_{x\in \mathbb{R}}|\mathbb{P}(Z_n \leq x)-e^{-\fracβ{2}e^{-x}}|=\frac{25\log \log n}{4e \log n}(1+o(1))$$ and $$ W_1\left(\mathcal{L}(Z_n), Λ_β\right)=\frac{25\log \log n}{4\log n}(1+o(1))$$ for sufficiently large $n$, where $\mathcal{L}(Z_n)$ is the distribution of $Z_n$ and $W_1$ is the Wasserstein distance. Similar results hold for $\max_{i} |σ_i|.$ Furthermore, the convergence rates of the complex Ginibre ensemble are universal for complex iid random matrices under certain moment conditions on entries.

math.PR↗

Universality of the convergence rate for spectral radius of complex IID random matrices

Let $X$ be an $n\times n$ matrix with independent and identically distributed entries $x_{ij} \stackrel{\text { d }}{=} n^{-1 / 2} x$ for some complex random variable $x$ of mean zero and variance one. Let $\{σ_i\}_{1\le i\le n}$ be the eigenvalues of $X$ and let $|σ_1|:=\max_{1\le i\le n}|σ_i|$ be the spectral radius. Set $Y_n=\sqrt{4 n γ_n}\left[|σ_1|-1-\sqrt{\frac{γ_n}{4 n}}\right],$ where $γ_{n}=\log{n}-2\log{\log{n}}-\log{2π}.$ As established in \cite{Cipolloni23Universality}, with specific moment-related conditions imposed on $x,$ the Gumbel distribution $Λ$ is identified as the universal weak limit of $Y_n.$ Subsequently, we extend this line of research and rigorously prove that the convergence rate, previously obtained for complex Ginibre ensembles in \cite{MaMeng25}, also possesses the property of universality. Precisely, one gets $$\sup_{x\in \mathbb{R}}|\mathbb{P}(Y_n \leq x)-e^{-e^{-x}}|=\frac{2\log\log n}{e\log n}(1+o(1))$$ and $$W_1\left(\mathcal{L}(Y_n), Λ\right)=\frac{2\log\log n}{\log n}(1+o(1))$$ for sufficiently large $n$, where $\mathcal{L}(Y_n)$ is the distribution of $Y_n$.

math.PR↗