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Suppose $A$ is a $n \times n$ random matrix with centered Gaussian (real) i.i.d entries with variance $\sigma^2/n$.

What to we know about the spectral norm $s(A)$ of $A$, that is $\sqrt{\rho(A^t A)}$ ? (where $\rho(.)$ denotes the largest eigenvalue of a matrix)

In particular, if $A$ is symmetric, we know that $s(A)$ is precisely equal to $\rho(A)$ and from the circular law it implies that $s(A)$ converges to $\sigma$ as $n\to \infty$.

Is this last statement true for non-symmetric $A$ ?

Thanks !

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up vote 1 down vote accepted

Actually, neither statement is true. The circular law does not control the spectral radius: there could be as many $o(n)$ eigenvalues lying outside the circle, and so it is not necessarily true that $\rho(A)$ is a.s. equal to $\sigma$ in the limit.

I believe that almost surely $s(A) \to 2\sigma$ in both the symmetric and non-symmetric cases. For the non-symmetric case in particular, see Theorem 2.1 in this survey article by Rudelson and Vershynin (from their ICM invited talk).

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