On the real spectrum of a product of Gaussian random matrices

Abstract

Let $X_m=G_1\dots G_m$ denote the product of $m$ independent random matrices of size $N\times N$, with each matrix in the product consisting of independent standard Gaussian variables. Denoting by $N_{\mathbb{R}}(m)$ the total number of real eigenvalues of $X_m$, we show that for $m$ fixed \begin{equation*} \mathbb{E}(N_{\mathbb{R}}(m)) = \sqrt{\frac{2Nm}π}+O(\log(N)), \qquad N \to \infty. \end{equation*} This generalizes a well-known result of Edelman et al. \cite{EKS94} to all $m>1$. Furthermore, we show that the normalized global density of real eigenvalues converges weakly in expectation to the density of the random variable $|U{|}^{m}B$ where $U$ is uniform on $[-1,1]$ and $B$ is Bernoulli on $\{-1,1\}$. This proves a conjecture of Forrester and Ipsen \cite{FI16}. The results are obtained by the asymptotic analysis of a certain Meijer G-function.

11 pages

References in corpus (1)

• Probability that product of real random matrices have all eigenvalues real tend to 1

Cited by in corpus (1)

• How many eigenvalues of a product of truncated orthogonal matrices are real?