Large and Moderate Deviations for Entries of Orthogonal Matrices and the Stiefel Manifold

Abstract

Let $A_N$ be distributed according to the Haar probability measure on the orthogonal group $\mathscr{O}(N)$ for each $N\in\mathbb{N}$. It is well-known that the upper left $m_N\times k_N$ block of $\sqrt{N}A_N$ with $m_Nk_N = o(N)$ converges in total variation distance to a matrix of same size consisting of i.i.d. standard normal entries as $N\to\infty$. In this work, we characterize this convergence on the scale of large deviations. More precisely, we show that under the same condition $m_Nk_N = o(N)$ the empirical measure of entries of this block satisfies a large deviation principle with speed $m_Nk_N$ and rate function given by the relative entropy with respect to the standard normal distribution. Further, we complement the large deviation principle (LDP) obtained by Kabluchko and Prochno in [Large deviations for random matrices in the orthogonal group and Stiefel manifold with applications to random projections of product distributions, Annales de l'Institut Henri Poincar\'e. 60 (2024), 990 -- 1024] for the whole block $A_N$ with a moderate deviation principle (MDP). Concretely, we show an MDP for the sequence of matrices $\beta_N A_N$ in the product topology, where $\beta_N\to\infty$ is a sequence of real numbers such that $\beta_N = o(\sqrt{N})$. Here, in contrast to the LDP, the Gaussian behavior of the entries is reflected in the rate function.