English

Wegner estimate and upper bound on the eigenvalue condition number of non-Hermitian random matrices

Probability 2023-06-06 v2

Abstract

We consider N×NN\times N non-Hermitian random matrices of the form X+AX+A, where AA is a general deterministic matrix and NX\sqrt{N}X consists of independent entries with zero mean, unit variance, and bounded densities. For this ensemble, we prove (i) a Wegner estimate, i.e. that the local density of eigenvalues is bounded by N1+o(1)N^{1+o(1)} and (ii) that the expected condition number of any bulk eigenvalue is bounded by N1+o(1)N^{1+o(1)}; both results are optimal up to the factor No(1)N^{o(1)}. The latter result complements the very recent matching lower bound obtained in [15] (arXiv:2301.03549) and improves the NN-dependence of the upper bounds in [5,6,32] (arXiv:1906.11819, arXiv:2005.08930, arXiv:2005.08908). Our main ingredient, a near-optimal lower tail estimate for the small singular values of X+AzX+A-z, is of independent interest.

Keywords

Cite

@article{arxiv.2301.04981,
  title  = {Wegner estimate and upper bound on the eigenvalue condition number of non-Hermitian random matrices},
  author = {László Erdős and Hong Chang Ji},
  journal= {arXiv preprint arXiv:2301.04981},
  year   = {2023}
}

Comments

38 pages; improved Corollary 2.4, and removed Theorem 2.10 (ii) for k=1 and Theorem 2.7 (iii)