Journal of Theoretical Probability

, Volume 23, Issue 4, pp 945–950 | Cite as

Circular Law for Noncentral Random Matrices

  • Djalil Chafaï


Let (X jk )j,k1 be an infinite array of i.i.d. complex random variables with mean 0 and variance 1. Let λ n,1,…,λ n,n be the eigenvalues of \((\frac{1}{\sqrt{n}}X_{jk})_{1\leqslant j,k\leqslant n}\). The strong circular law theorem states that, with probability one, the empirical spectral distribution \(\frac{1}{n}(\delta _{\lambda _{n,1}}+\cdots+\delta _{\lambda _{n,n}})\) converges weakly as n→∞ to the uniform law over the unit disc {z∈ℂ,|z|1}. In this short paper, we provide an elementary argument that allows us to add a deterministic matrix M to (X jk )1 j,k n provided that Tr(MM *)=O(n 2) and rank(M)=O(n α ) with α<1. Conveniently, the argument is similar to the one used for the noncentral version of the Wigner and Marchenko–Pastur theorems.


Random matrices Circular law 

Mathematics Subject Classification (2000)



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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Laboratoire d’Analyse et de Mathématiques AppliquéesUMR 8050 CNRS, Université Paris-Est Marne-la-ValléeCedex 2, Champs-sur-MarneFrance

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