Regularization of non-normal matrices by Gaussian noise
classification
🧮 math.PR
keywords
gammanoiseadditivegaussianjordanmatricesregularizationblock
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We consider the regularization of matrices $M^N$ written in Jordan form by additive Gaussian noise $N^{-\gamma}G^N$, where $G^N$ is a matrix of i.i.d. standard Gaussians and $\gamma>1/2$ so that the operator norm of the additive noise tends to $0$ with $N$. Under mild conditions on the structure of $M^N$ we evaluate the limit of the empirical measure of eigenvalues of $M^N+N^{-\gamma} G^N$ and show that it depends on $\gamma$, in contrast with the case of a single Jordan block.
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