Random matrices: Universality of local spectral statistics of non-Hermitian matrices

Abstract

It is a result of Ginibre that the normalized bulk $k$-point correlation functions of a complex $n\times n$ Gaussian matrix with independent entries of mean zero and unit variance are asymptotically given by the determinantal point process on $\mathbb{C}$ with kernel $K_{\infty }(z,w):=\frac{1}{\pi }{e}^{-|z{|}^2/2-|w{|}^2/2+z\bar{w}}$ in the limit $n\to \infty$. We show that this asymptotic law is universal among all random $n\times n$ matrices $M_n$ whose entries are jointly independent, exponentially decaying, have independent real and imaginary parts and whose moments match that of the complex Gaussian ensemble to fourth order. As an application, we extend a central limit theorem for the number of eigenvalues of complex Gaussian matrices in a small disk to these more general ensembles. These are non-Hermitian analogues of some recent universality results for Hermitian Wigner matrices. However, a new difficulty arises in the non-Hermitian case, due to the instability of the spectrum for such matrices. To resolve this, we work with the log-determinants $\log |\det (M_n-z_0)|$ rather than with the Stieltjes transform $\frac{1}{n}\mathrm{tr}(M_n-z_0{)}^{-1}$. Our main tools are a four moment theorem for these log-determinants, together with a strong concentration result for the log-determinants in the Gaussian case. The latter is established by studying the solutions of a certain nonlinear stochastic difference equation. With some extra consideration, we can extend our arguments to the real case, proving universality for correlation functions of real matrices which match the real Gaussian ensemble to the fourth order. As an application, we show that a real $n\times n$ matrix whose entries are jointly independent, exponentially decaying and whose moments match the real Gaussian ensemble to fourth order has $\sqrt{\frac{2n}{\pi }}+o(\sqrt{n})$ real eigenvalues asymptotically almost surely.

Published in at http://dx.doi.org/10.1214/13-AOP876 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org). This version notes an error in Proposition 34, and indicates a workaround so as not to impact the remainder of the paper