Eigenvector delocalization for non‐Hermitian random matrices and applications. Issue 1 (12th March 2020)

• Record Type: Journal Article
• Authors: Luh, Kyle; O'Rourke, Sean
• Is Part Of: Random structures & algorithms. Volume 57:Issue 1(2020)
• Page Start: 169
• Page End: 210
• Publication Date: 2020-03-12
• Subjects: non‐Hermitian random matrices -- eigenvectors -- delocalization -- least singular value; Random graphs -- Periodicals; Mathematical analysis -- Periodicals; 519
• DOI: 10.1002/rsa.20917
• Languages: English
• ISSNs: 1042-9832

Abstract

Abstract : Improving upon results of Rudelson and Vershynin, we establish delocalization bounds for eigenvectors of independent‐entry random matrices. In particular, we show that with high probability every eigenvector is delocalized, meaning any subset of its coordinates carries an appropriate proportion of its mass. Our results hold for random matrices with genuinely complex as well as real entries. As an application of our methods, we also establish delocalization bounds for normal vectors to random hyperplanes. The proofs of our main results rely on a least singular value bound for genuinely complex rectangular random matrices, which generalizes a previous bound due to the first author, and may be of independent interest.