154 research outputs found
Size of nodal domains of the eigenvectors of a G(n,p) graph
Consider an eigenvector of the adjacency matrix of a G(n, p) graph. A nodal
domain is a connected component of the set of vertices where this eigenvector
has a constant sign. It is known that with high probability, there are exactly
two nodal domains for each eigenvector corresponding to a non-leading
eigenvalue. We prove that with high probability, the sizes of these nodal
domains are approximately equal to each other
Weak Convergence of the Scaled Median of Independent Brownian Motions
We consider the median of n independent Brownian motions, and show that this
process, when properly scaled, converges weakly to a centered Gaussian process.
The chief difficulty is establishing tightness, which is proved through direct
estimates on the increments of the median process. An explicit formula is given
for the covariance function of the limit process. The limit process is also
shown to be Holder continuous with exponent gamma for all gamma < 1/4.Comment: to appear in Probability Theory and Related Field
Cutoff for the Ising model on the lattice
Introduced in 1963, Glauber dynamics is one of the most practiced and
extensively studied methods for sampling the Ising model on lattices. It is
well known that at high temperatures, the time it takes this chain to mix in
on a system of size is . Whether in this regime there is
cutoff, i.e. a sharp transition in the -convergence to equilibrium, is a
fundamental open problem: If so, as conjectured by Peres, it would imply that
mixing occurs abruptly at for some fixed , thus providing
a rigorous stopping rule for this MCMC sampler. However, obtaining the precise
asymptotics of the mixing and proving cutoff can be extremely challenging even
for fairly simple Markov chains. Already for the one-dimensional Ising model,
showing cutoff is a longstanding open problem.
We settle the above by establishing cutoff and its location at the high
temperature regime of the Ising model on the lattice with periodic boundary
conditions. Our results hold for any dimension and at any temperature where
there is strong spatial mixing: For this carries all the way to the
critical temperature. Specifically, for fixed , the continuous-time
Glauber dynamics for the Ising model on with periodic boundary
conditions has cutoff at , where is
the spectral gap of the dynamics on the infinite-volume lattice. To our
knowledge, this is the first time where cutoff is shown for a Markov chain
where even understanding its stationary distribution is limited.
The proof hinges on a new technique for translating to mixing
which enables the application of log-Sobolev inequalities. The technique is
general and carries to other monotone and anti-monotone spin-systems.Comment: 34 pages, 3 figure
A Rigorous Path Integral for Supersymmetric Quantum Mechanics and the Heat Kernel
In a rigorous construction of the path integral for supersymmetric quantum
mechanics on a Riemann manifold, based on B\"ar and Pf\"affle's use of
piecewise geodesic paths, the kernel of the time evolution operator is the heat
kernel for the Laplacian on forms. The path integral is approximated by the
integral of a form on the space of piecewise geodesic paths which is the
pullback by a natural section of Mathai and Quillen's Thom form of a bundle
over this space.
In the case of closed paths, the bundle is the tangent space to the space of
geodesic paths, and the integral of this form passes in the limit to the
supertrace of the heat kernel.Comment: 14 pages, LaTeX, no fig
On a stochastic partial differential equation with non-local diffusion
In this paper, we prove existence, uniqueness and regularity for a class of
stochastic partial differential equations with a fractional Laplacian driven by
a space-time white noise in dimension one. The equation we consider may also
include a reaction term
Analysis of equilibrium states of Markov solutions to the 3D Navier-Stokes equations driven by additive noise
We prove that every Markov solution to the three dimensional Navier-Stokes
equation with periodic boundary conditions driven by additive Gaussian noise is
uniquely ergodic. The convergence to the (unique) invariant measure is
exponentially fast.
Moreover, we give a well-posedness criterion for the equations in terms of
invariant measures. We also analyse the energy balance and identify the term
which ensures equality in the balance.Comment: 32 page
Cut Points and Diffusions in Random Environment
In this article we investigate the asymptotic behavior of a new class of
multi-dimensional diffusions in random environment. We introduce cut times in
the spirit of the work done by Bolthausen, Sznitman and Zeitouni, see [4], in
the discrete setting providing a decoupling effect in the process. This allows
us to take advantage of an ergodic structure to derive a strong law of large
numbers with possibly vanishing limiting velocity and a central limit theorem
under the quenched measure.Comment: 44 pages; accepted for publication in "Journal of Theoretical
Probability
Self-intersection local time of planar Brownian motion based on a strong approximation by random walks
The main purpose of this work is to define planar self-intersection local
time by an alternative approach which is based on an almost sure pathwise
approximation of planar Brownian motion by simple, symmetric random walks. As a
result, Brownian self-intersection local time is obtained as an almost sure
limit of local averages of simple random walk self-intersection local times. An
important tool is a discrete version of the Tanaka--Rosen--Yor formula; the
continuous version of the formula is obtained as an almost sure limit of the
discrete version. The author hopes that this approach to self-intersection
local time is more transparent and elementary than other existing ones.Comment: 36 pages. A new part on renormalized self-intersection local time has
been added and several inaccuracies have been corrected. To appear in Journal
of Theoretical Probabilit
Convergence of the stochastic Euler scheme for locally Lipschitz coefficients
Stochastic differential equations are often simulated with the Monte Carlo
Euler method. Convergence of this method is well understood in the case of
globally Lipschitz continuous coefficients of the stochastic differential
equation. The important case of superlinearly growing coefficients, however,
has remained an open question. The main difficulty is that numerically weak
convergence fails to hold in many cases of superlinearly growing coefficients.
In this paper we overcome this difficulty and establish convergence of the
Monte Carlo Euler method for a large class of one-dimensional stochastic
differential equations whose drift functions have at most polynomial growth.Comment: Published at http://www.springerlink.com/content/g076w80730811vv3 in
the Foundations of Computational Mathematics 201
Random fields on model sets with localized dependency and their diffraction
For a random field on a general discrete set, we introduce a condition that
the range of the correlation from each site is within a predefined compact set
D. For such a random field omega defined on the model set Lambda that satisfies
a natural geometric condition, we develop a method to calculate the diffraction
measure of the random field. The method partitions the random field into a
finite number of random fields, each being independent and admitting the law of
large numbers. The diffraction measure of omega consists almost surely of a
pure-point component and an absolutely continuous component. The former is the
diffraction measure of the expectation E[omega], while the inverse Fourier
transform of the absolutely continuous component of omega turns out to be a
weighted Dirac comb which satisfies a simple formula. Moreover, the pure-point
component will be understood quantitatively in a simple exact formula if the
weights are continuous over the internal space of Lambda Then we provide a
sufficient condition that the diffraction measure of a random field on a model
set is still pure-point.Comment: 21 page
- …