26 research outputs found
Local Eigenvalue Density for General MANOVA Matrices
We consider random n\times n matrices of the form
(XX*+YY*)^{-1/2}YY*(XX*+YY*)^{-1/2}, where X and Y have independent entries
with zero mean and variance one. These matrices are the natural generalization
of the Gaussian case, which are known as MANOVA matrices and which have joint
eigenvalue density given by the third classical ensemble, the Jacobi ensemble.
We show that, away from the spectral edge, the eigenvalue density converges to
the limiting density of the Jacobi ensemble even on the shortest possible
scales of order 1/n (up to \log n factors). This result is the analogue of the
local Wigner semicircle law and the local Marchenko-Pastur law for general
MANOVA matrices.Comment: Several small changes made to the tex
On Eigenvalues of the sum of two random projections
We study the behavior of eigenvalues of matrix P_N + Q_N where P_N and Q_N
are two N -by-N random orthogonal projections. We relate the joint eigenvalue
distribution of this matrix to the Jacobi matrix ensemble and establish the
universal behavior of eigenvalues for large N. The limiting local behavior of
eigenvalues is governed by the sine kernel in the bulk and by either the Bessel
or the Airy kernel at the edge depending on parameters. We also study an
exceptional case when the local behavior of eigenvalues of P_N + Q_N is not
universal in the usual sense.Comment: 14 page
Spectrum of non-Hermitian heavy tailed random matrices
Let (X_{jk})_{j,k>=1} be i.i.d. complex random variables such that |X_{jk}|
is in the domain of attraction of an alpha-stable law, with 0< alpha <2. Our
main result is a heavy tailed counterpart of Girko's circular law. Namely,
under some additional smoothness assumptions on the law of X_{jk}, we prove
that there exists a deterministic sequence a_n ~ n^{1/alpha} and a probability
measure mu_alpha on C depending only on alpha such that with probability one,
the empirical distribution of the eigenvalues of the rescaled matrix a_n^{-1}
(X_{jk})_{1<=j,k<=n} converges weakly to mu_alpha as n tends to infinity. Our
approach combines Aldous & Steele's objective method with Girko's Hermitization
using logarithmic potentials. The underlying limiting object is defined on a
bipartized version of Aldous' Poisson Weighted Infinite Tree. Recursive
relations on the tree provide some properties of mu_alpha. In contrast with the
Hermitian case, we find that mu_alpha is not heavy tailed.Comment: Expanded version of a paper published in Communications in
Mathematical Physics 307, 513-560 (2011
Circular Law Theorem for Random Markov Matrices
Consider an nxn random matrix X with i.i.d. nonnegative entries with bounded
density, mean m, and finite positive variance sigma^2. Let M be the nxn random
Markov matrix with i.i.d. rows obtained from X by dividing each row of X by its
sum. In particular, when X11 follows an exponential law, then M belongs to the
Dirichlet Markov Ensemble of random stochastic matrices. Our main result states
that with probability one, the counting probability measure of the complex
spectrum of n^(1/2)M converges weakly as n tends to infinity to the uniform law
on the centered disk of radius sigma/m. The bounded density assumption is
purely technical and comes from the way we control the operator norm of the
resolvent.Comment: technical update via http://HAL.archives-ouvertes.f