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Xinchen Hu

Publications and source records attributed to Xinchen Hu.

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Convergence rate of extreme eigenvalue of Ginibre ensembles to Gumbel distribution

Let $X$ be a real $(\beta=1)$ or complex $(\beta=2)$ Ginibre ensemble. Let $\{\sigma_i\}_{1\le i\le n}$ be the eigenvalues of $X,$ and $Z_n$ be some rescaled version of $\max_i \Re \sigma_i.$ It was proved that $Z_n$ converges weakly to the Gumbel distribution $\Lambda_{\beta}$ with distribution function $e^{-\frac{\beta}{2}e^{-x}}.$ We further prove that $$\sup_{x\in \mathbb{R}}|\mathbb{P}(Z_n \leq x)-e^{-\frac{\beta}{2}e^{-x}}|=\frac{25\log \log n}{4e \log n}(1+o(1))$$ and $$ W_1\left(\mathcal{L}(Z_n), \Lambda_{\beta}\right)=\frac{25\log \log n}{4\log n}(1+o(1))$$ 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 $\max_{i} |\sigma_i|.$ 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 $\{\sigma_i\}_{1\le i\le n}$ be the eigenvalues of $X$ and let $|\sigma_1|:=\max_{1\le i\le n}|\sigma_i|$ be the spectral radius. Set $Y_n=\sqrt{4 n \gamma_n}\left[|\sigma_1|-1-\sqrt{\frac{\gamma_n}{4 n}}\right],$ where $\gamma_{n}=\log{n}-2\log{\log{n}}-\log{2\pi}.$ As established in \cite{Cipolloni23Universality}, with specific moment-related conditions imposed on $x,$ the Gumbel distribution $\Lambda$ 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 $$W_1\left(\mathcal{L}(Y_n), \Lambda\right)=\frac{2\log\log n}{\log n}(1+o(1))$$ for sufficiently large $n$, where $\mathcal{L}(Y_n)$ is the distribution of $Y_n$.

math.PR