Circular law for sparse random regular digraphs

Alexander E. Litvak, Anna Lytova, Konstantin Tikhomirov, Nicole Tomczak-Jaegermann, Pierre Youssef

Research output: Contribution to journalArticlepeer-review

Abstract

Fix a constant C ≥ 1 and let d = d(n) satisfy d ≤ lnC n for every large integer n. Denote by An the adjacency matrix of a uniform random directed d-regular graph on n vertices. We show that if d → ∞ as n → ∞, the empirical spectral distribution of the appropriately rescaled matrix An converges weakly in probability to the circular law. This result, together with an earlier work of Cook, completely settles the problem of weak convergence of the empirical distribution in a directed d-regular setting with the degree tending to infinity. As a crucial element of our proof, we develop a technique of bounding intermediate singular values of An based on studying random normals to rowspaces and on constructing a product structure to deal with the lack of independence between matrix entries.

Original languageEnglish (US)
Pages (from-to)467-501
Number of pages35
JournalJournal of the European Mathematical Society
Volume23
Issue number2
DOIs
StatePublished - Oct 29 2021

Keywords

  • Circular law
  • Intermediate singular values
  • Logarithmic potential
  • Random graphs
  • Random matrices
  • Regular graphs
  • Sparse matrices

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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