The distribution of overlaps between eigenvectors of Ginibre matrices
Paul Bourgade, Guillaume Dubach

TL;DR
This paper investigates the distribution and correlations of eigenvector overlaps in Ginibre matrices, revealing their asymptotic behavior, fluctuations, and implications for spectral stability and dynamics.
Contribution
It extends previous work by deriving the distribution of overlaps, their correlations, and asymptotic properties, providing new insights into eigenvector behavior in nonnormal matrices.
Findings
Condition numbers converge to an inverse Gamma distribution.
Off-diagonal overlaps' expectation for separated eigenvalues.
Decorrelation of condition numbers at mesoscopic distances.
Abstract
We study the overlaps between eigenvectors of nonnormal matrices. They quantify the stability of the spectrum, and characterize the joint eigenvalues increments under Dyson-type dynamics. Well known work by Chalker and Mehlig calculated the expectation of these overlaps for complex Ginibre matrices. For the same model, we extend their results by deriving the distribution of diagonal overlaps (the condition numbers), and their correlations. We prove: (i) convergence of condition numbers for bulk eigenvalues to an inverse Gamma distribution; more generally, we decompose the quenched overlap (i.e. conditioned on eigenvalues) as a product of independent random variables; (ii) asymptotic expectation of off-diagonal overlaps, both for microscopic or mesoscopic separation of the corresponding eigenvalues; (iii) decorrelation of condition numbers associated to eigenvalues at mesoscopic…
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