A simple proof of almost sure convergence for the largest singular value of a product of Gaussian matrices

Abstract: Let $m \geq 1$ and consider the product of $m$ independent $n \times n$ matrices $\mathbf{W} = \mathbf{W}<em>1 \dots \mathbf{W}_m$, each $\mathbf{W}</em>{i}$ with i.i.d. normalised $\mathcal{N}(0, n<sup>{-1/2})$ entries. It is shown in Penson et al. (2011) that the empirical distribution of the squared singular values of $\mathbf{W}$ converges to a deterministic distribution compactly supported on $[0, u_m]$, where $u_m = \frac{{(m+1)}<sup>{m+1}}{m<sup>m}$. This generalises the well-known case of $m=1$, corresponding to the Marchenko-Pastur distribution for square matrices. Moreover, for $m=1$, it was first shown by Geman (1980) that the largest squared singular value almost surely converges to the right endpoint (the so-called ``soft edge'') of the support, i.e. $s_1^{2(\mathbf{W})} \xrightarrow{a.s.} u_1$. Herein, we present a proof for the general case $s_1^{2(\mathbf{W})} \xrightarrow{a.s.} u_m$ for $m\geq 1$. Although we do not claim novelty for our result, the proof is simple and does not require familiarity with modern techniques of free probability.