2,456 research outputs found
Signal and Noise in Correlation Matrix
Using random matrix technique we determine an exact relation between the
eigenvalue spectrum of the covariance matrix and of its estimator. This
relation can be used in practice to compute eigenvalue invariants of the
covariance (correlation) matrix. Results can be applied in various problems
where one experimentally estimates correlations in a system with many degrees
of freedom, like in statistical physics, lattice measurements of field theory,
genetics, quantitative finance and other applications of multivariate
statistics.Comment: 17 pages, 3 figures, corrected typos, revtex style changed to elsar
Commutative law for products of infinitely large isotropic random matrices
Ensembles of isotropic random matrices are defined by the invariance of the
probability measure under the left (and right) multiplication by an arbitrary
unitary matrix. We show that the multiplication of large isotropic random
matrices is spectrally commutative and self-averaging in the limit of infinite
matrix size . The notion of spectral commutativity means
that the eigenvalue density of a product ABC... of such matrices is independent
of the order of matrix multiplication, for example the matrix ABCD has the same
eigenvalue density as ADCB. In turn, the notion of self-averaging means that
the product of n independent but identically distributed random matrices, which
we symbolically denote by AAA..., has the same eigenvalue density as the
corresponding power A^n of a single matrix drawn from the underlying matrix
ensemble. For example, the eigenvalue density of ABCCABC is the same as of
A^2B^2C^3. We also discuss the singular behavior of the eigenvalue and singular
value densities of isotropic matrices and their products for small eigenvalues
. We show that the singularities at the origin of the
eigenvalue density and of the singular value density are in one-to-one
correspondence in the limit : the eigenvalue density of
an isotropic random matrix has a power law singularity at the origin with a power when and only when the density of
its singular values has a power law singularity with a
power . These results are obtained analytically in the limit
. We supplement these results with numerical simulations
for large but finite N and discuss finite size effects for the most common
ensembles of isotropic random matrices.Comment: 15 pages, 4 figure
New spectral relations between products and powers of isotropic random matrices
We show that the limiting eigenvalue density of the product of n identically
distributed random matrices from an isotropic unitary ensemble (IUE) is equal
to the eigenvalue density of n-th power of a single matrix from this ensemble,
in the limit when the size of the matrix tends to infinity. Using this
observation one can derive the limiting density of the product of n independent
identically distributed non-hermitian matrices with unitary invariant measures.
In this paper we discuss two examples: the product of n Girko-Ginibre matrices
and the product of n truncated unitary matrices. We also provide an evidence
that the result holds also for isotropic orthogonal ensembles (IOE).Comment: 8 pages, 3 figures (in version 2 we added a figure and discussion on
finite size effects for isotropic orthogonal ensemble
Perturbing General Uncorrelated Networks
This paper is a direct continuation of an earlier work, where we studied
Erd\"os-R\'enyi random graphs perturbed by an interaction Hamiltonian favouring
the formation of short cycles. Here, we generalize these results. We keep the
same interaction Hamiltonian but let it act on general graphs with uncorrelated
nodes and an arbitrary given degree distribution. It is shown that the results
obtained for Erd\"os-R\'enyi graphs are generic, at the qualitative level.
However, scale-free graphs are an exception to this general rule and exhibit a
singular behaviour, studied thoroughly in this paper, both analytically and
numerically.Comment: 7 pages, 7 eps figures, 2-column revtex format, references adde
Eigenvalues and Singular Values of Products of Rectangular Gaussian Random Matrices (The Extended Version)
We consider a product of an arbitrary number of independent rectangular
Gaussian random matrices. We derive the mean densities of its eigenvalues and
singular values in the thermodynamic limit, eventually verified numerically.
These densities are encoded in the form of the so called M-transforms, for
which polynomial equations are found. We exploit the methods of planar
diagrammatics, enhanced to the non-Hermitian case, and free random variables,
respectively; both are described in the appendices. As particular results of
these two main equations, we find the singular behavior of the spectral
densities near zero. Moreover, we propose a finite-size form of the spectral
density of the product close to the border of its eigenvalues' domain. Also,
led by the striking similarity between the two main equations, we put forward a
conjecture about a simple relationship between the eigenvalues and singular
values of any non-Hermitian random matrix whose spectrum exhibits rotational
symmetry around zero.Comment: 50 pages, 8 figures, to appear in the Proceedings of the 23rd Marian
Smoluchowski Symposium on Statistical Physics: "Random Matrices, Statistical
Physics and Information Theory," September 26-30, 2010, Krakow, Polan
Phase transition and topology in 4d simplicial gravity
We present data indicating that the recent evidence for the phase transition
being of first order does not result from a breakdown of the ergodicity of the
algorithm. We also present data showing that the thermodynamical limit of the
model is independent of topology.Comment: 3 latex pages + 4 ps fig. + espcrc2.sty. Talk presented at
LATTICE(gravity
Adaptive networks of trading agents
Multi-agent models have been used in many contexts to study generic
collective behavior. Similarly, complex networks have become very popular
because of the diversity of growth rules giving rise to scale-free behavior.
Here we study adaptive networks where the agents trade ``wealth'' when they are
linked together while links can appear and disappear according to the wealth of
the corresponding agents; thus the agents influence the network dynamics and
vice-versa. Our framework generalizes a multi-agent model of Bouchand and
Mezard, and leads to a steady state with fluctuating connectivities. The system
spontaneously self-organizes into a critical state where the wealth
distribution has a fat tail and the network is scale-free; in addition, network
heterogeneities lead to enhanced wealth condensation.Comment: 7 figure
A Random Matrix Approach to VARMA Processes
We apply random matrix theory to derive spectral density of large sample
covariance matrices generated by multivariate VMA(q), VAR(q) and VARMA(q1,q2)
processes. In particular, we consider a limit where the number of random
variables N and the number of consecutive time measurements T are large but the
ratio N/T is fixed. In this regime the underlying random matrices are
asymptotically equivalent to Free Random Variables (FRV). We apply the FRV
calculus to calculate the eigenvalue density of the sample covariance for
several VARMA-type processes. We explicitly solve the VARMA(1,1) case and
demonstrate a perfect agreement between the analytical result and the spectra
obtained by Monte Carlo simulations. The proposed method is purely algebraic
and can be easily generalized to q1>1 and q2>1.Comment: 16 pages, 6 figures, submitted to New Journal of Physic
Network Transitivity and Matrix Models
This paper is a step towards a systematic theory of the transitivity
(clustering) phenomenon in random networks. A static framework is used, with
adjacency matrix playing the role of the dynamical variable. Hence, our model
is a matrix model, where matrices are random, but their elements take values 0
and 1 only. Confusion present in some papers where earlier attempts to
incorporate transitivity in a similar framework have been made is hopefully
dissipated. Inspired by more conventional matrix models, new analytic
techniques to develop a static model with non-trivial clustering are
introduced. Computer simulations complete the analytic discussion.Comment: 11 pages, 7 eps figures, 2-column revtex format, print bug correcte
- …