196 research outputs found
Probability of local bifurcation type from a fixed point: A random matrix perspective
Results regarding probable bifurcations from fixed points are presented in
the context of general dynamical systems (real, random matrices), time-delay
dynamical systems (companion matrices), and a set of mappings known for their
properties as universal approximators (neural networks). The eigenvalue spectra
is considered both numerically and analytically using previous work of Edelman
et. al. Based upon the numerical evidence, various conjectures are presented.
The conclusion is that in many circumstances, most bifurcations from fixed
points of large dynamical systems will be due to complex eigenvalues.
Nevertheless, surprising situations are presented for which the aforementioned
conclusion is not general, e.g. real random matrices with Gaussian elements
with a large positive mean and finite variance.Comment: 21 pages, 19 figure
Summing free unitary random matrices
I use quaternion free probability calculus - an extension of free probability
to non-Hermitian matrices (which is introduced in a succinct but self-contained
way) - to derive in the large-size limit the mean densities of the eigenvalues
and singular values of sums of independent unitary random matrices, weighted by
complex numbers. In the case of CUE summands, I write them in terms of two
"master equations," which I then solve and numerically test in four specific
cases. I conjecture a finite-size extension of these results, exploiting the
complementary error function. I prove a central limit theorem, and its first
sub-leading correction, for independent identically-distributed zero-drift
unitary random matrices.Comment: 17 pages, 15 figure
Random matrix model for QCD_3 staggered fermions
We show that the lowest part of the eigenvalue density of the staggered
fermion operator in lattice QCD_3 at small lattice coupling constant beta has
exactly the same shape as in QCD_4. This observation is quite surprising, since
universal properties of the QCD_3 Dirac operator are expected to be described
by a non-chiral matrix model. We show that this effect is related to the
specific nature of the staggered fermion discretization and that the eigenvalue
density evolves towards the non-chiral random matrix prediction when beta is
increased and the continuum limit is approached. We propose a two-matrix model
with one free parameter which interpolates between the two limits and very well
mimics the pattern of evolution with beta of the eigenvalue density of the
staggered fermion operator in QCD_3.Comment: 8 pages 4 figure
Moderate deviations for the determinant of Wigner matrices
We establish a moderate deviations principle (MDP) for the log-determinant
of a Wigner matrix matching four moments with
either the GUE or GOE ensemble. Further we establish Cram\'er--type moderate
deviations and Berry-Esseen bounds for the log-determinant for the GUE and GOE
ensembles as well as for non-symmetric and non-Hermitian Gaussian random
matrices (Ginibre ensembles), respectively.Comment: 20 pages, one missing reference added; Limit Theorems in Probability,
Statistics and Number Theory, Springer Proceedings in Mathematics and
Statistics, 201
Chiral Symmetry Breaking and the Dirac Spectrum at Nonzero Chemical Potential
The relation between the spectral density of the QCD Dirac operator at
nonzero baryon chemical potential and the chiral condensate is investigated. We
use the analytical result for the eigenvalue density in the microscopic regime
which shows oscillations with a period that scales as 1/V and an amplitude that
diverges exponentially with the volume . We find that the discontinuity
of the chiral condensate is due to the whole oscillating region rather than to
an accumulation of eigenvalues at the origin. These results also extend beyond
the microscopic regime to chemical potentials .Comment: 4 pages, 1 figur
The Ginibre ensemble and Gaussian analytic functions
We show that as changes, the characteristic polynomial of the
random matrix with i.i.d. complex Gaussian entries can be described recursively
through a process analogous to P\'olya's urn scheme. As a result, we get a
random analytic function in the limit, which is given by a mixture of Gaussian
analytic functions. This gives another reason why the zeros of Gaussian
analytic functions and the Ginibre ensemble exhibit similar local repulsion,
but different global behavior. Our approach gives new explicit formulas for the
limiting analytic function.Comment: 23 pages, 1 figur
Spectra of sparse non-Hermitian random matrices: an analytical solution
We present the exact analytical expression for the spectrum of a sparse
non-Hermitian random matrix ensemble, generalizing two classical results in
random-matrix theory: this analytical expression forms a non-Hermitian version
of the Kesten-Mckay law as well as a sparse realization of Girko's elliptic
law. Our exact result opens new perspectives in the study of several physical
problems modelled on sparse random graphs. In this context, we show
analytically that the convergence rate of a transport process on a very sparse
graph depends upon the degree of symmetry of the edges in a non-monotonous way.Comment: 5 pages, 5 figures, 12 pages supplemental materia
Spectrum of the Product of Independent Random Gaussian Matrices
We show that the eigenvalue density of a product X=X_1 X_2 ... X_M of M
independent NxN Gaussian random matrices in the large-N limit is rotationally
symmetric in the complex plane and is given by a simple expression
rho(z,\bar{z}) = 1/(M\pi\sigma^2} |z|^{-2+2/M} for |z|<\sigma, and is zero for
|z|> \sigma. The parameter \sigma corresponds to the radius of the circular
support and is related to the amplitude of the Gaussian fluctuations. This form
of the eigenvalue density is highly universal. It is identical for products of
Gaussian Hermitian, non-Hermitian, real or complex random matrices. It does not
change even if the matrices in the product are taken from different Gaussian
ensembles. We present a self-contained derivation of this result using a planar
diagrammatic technique for Gaussian matrices. We also give a numerical evidence
suggesting that this result applies also to matrices whose elements are
independent, centered random variables with a finite variance.Comment: 16 pages, 6 figures, minor changes, some references adde
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
Signal from noise retrieval from one and two-point Green's function - comparison
We compare two methods of eigen-inference from large sets of data, based on
the analysis of one-point and two-point Green's functions, respectively. Our
analysis points at the superiority of eigen-inference based on one-point
Green's function. First, the applied by us method based on Pad?e approximants
is orders of magnitude faster comparing to the eigen-inference based on
uctuations (two-point Green's functions). Second, we have identified the source
of potential instability of the two-point Green's function method, as arising
from the spurious zero and negative modes of the estimator for a variance
operator of the certain multidimensional Gaussian distribution, inherent for
the two-point Green's function eigen-inference method. Third, we have presented
the cases of eigen-inference based on negative spectral moments, for strictly
positive spectra. Finally, we have compared the cases of eigen-inference of
real-valued and complex-valued correlated Wishart distributions, reinforcing
our conclusions on an advantage of the one-point Green's function method.Comment: 14 pages, 8 figures, 3 table
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