Circular Law for Random Block Band Matrices with Genuinely Sublinear Bandwidth
Vishesh Jain, Indrajit Jana, Kyle Luh, Sean O'Rourke

TL;DR
This paper establishes the circular law for a class of non-Hermitian random block band matrices with sublinear bandwidth, showing eigenvalue distribution convergence under specific conditions.
Contribution
It proves the circular law for non-Hermitian band matrices with genuinely sublinear bandwidth, extending previous results with new singular value bounds.
Findings
Eigenvalue distribution converges to the uniform distribution on the unit disk.
Established a least singular value bound for shifted band matrices.
Extended circular law results to matrices with sublinear bandwidth.
Abstract
We prove the circular law for a class of non-Hermitian random block band matrices with genuinely sublinear bandwidth. Namely, we show there exists so that if the bandwidth of the matrix is at least and the nonzero entries are iid random variables with mean zero and slightly more than four finite moments, then the limiting empirical eigenvalue distribution of , when properly normalized, converges in probability to the uniform distribution on the unit disk in the complex plane. The key technical result is a least singular value bound for shifted random band block matrices with genuinely sublinear bandwidth, which improves on a result of Cook in the band matrix setting.
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