Outliers and bounded rank perturbation for non-Hermitian random band matrices
Yi Han

TL;DR
This paper investigates the spectral properties of non-Hermitian random band matrices, showing how outliers and eigenvalue confinement are influenced by finite rank perturbations, extending previous results to more complex, sparse, and inhomogeneous models.
Contribution
It extends spectral outlier analysis and eigenvalue confinement results to a broad class of sparse, inhomogeneous non-Hermitian random matrices, including band matrices on regular graphs.
Findings
Eigenvalues are confined near the elliptic law support under mild conditions.
Finite rank perturbations induce outliers depending only on the perturbation and parameter .
Quantitative convergence rates for eigenvalue distributions are established.
Abstract
In this work we consider general non-Hermitian square random matrices that include a wide class of random band matrices with independent entries. Whereas the existence of limiting density is largely unknown for these inhomogeneous models, we show that spectral outliers can be determined under very general conditions when perturbed by a finite rank deterministic matrix. More precisely, we show that whenever and , and under mild conditions on sparsity and entry moments of , then with high possibility all eigenvalues of are confined in a neighborhood of the support of the elliptic law with parameter . Also, a finite rank perturbation property holds: when is perturbed by another deterministic matrix with bounded rank, then the perturbation induces outlying eigenvalues…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRandom Matrices and Applications · Matrix Theory and Algorithms · Advanced Topics in Algebra
