2,127 research outputs found
Anisotropic KPZ growth in 2+1 dimensions: fluctuations and covariance structure
In [arXiv:0804.3035] we studied an interacting particle system which can be
also interpreted as a stochastic growth model. This model belongs to the
anisotropic KPZ class in 2+1 dimensions. In this paper we present the results
that are relevant from the perspective of stochastic growth models, in
particular: (a) the surface fluctuations are asymptotically Gaussian on a
sqrt(ln(t)) scale and (b) the correlation structure of the surface is
asymptotically given by the massless field.Comment: 13 pages, 4 figure
A CLT for Plancherel representations of the infinite-dimensional unitary group
We study asymptotics of traces of (noncommutative) monomials formed by images
of certain elements of the universal enveloping algebra of the
infinite-dimensional unitary group in its Plancherel representations. We prove
that they converge to (commutative) moments of a Gaussian process that can be
viewed as a collection of simply yet nontrivially correlated two-dimensional
Gaussian Free Fields. The limiting process has previously arisen via the global
scaling limit of spectra for submatrices of Wigner Hermitian random matrices.
This note is an announcement, proofs will appear elsewhere.Comment: 12 page
Non-intersecting squared Bessel paths: critical time and double scaling limit
We consider the double scaling limit for a model of non-intersecting
squared Bessel processes in the confluent case: all paths start at time
at the same positive value , remain positive, and are conditioned to end
at time at . After appropriate rescaling, the paths fill a region in
the --plane as that intersects the hard edge at at a
critical time . In a previous paper (arXiv:0712.1333), the scaling
limits for the positions of the paths at time were shown to be
the usual scaling limits from random matrix theory. Here, we describe the limit
as of the correlation kernel at critical time and in the
double scaling regime. We derive an integral representation for the limit
kernel which bears some connections with the Pearcey kernel. The analysis is
based on the study of a matrix valued Riemann-Hilbert problem by
the Deift-Zhou steepest descent method. The main ingredient is the construction
of a local parametrix at the origin, out of the solutions of a particular
third-order linear differential equation, and its matching with a global
parametrix.Comment: 53 pages, 15 figure
Statistics of layered zigzags: a two-dimensional generalization of TASEP
A novel discrete growth model in 2+1 dimensions is presented in three
equivalent formulations: i) directed motion of zigzags on a cylinder, ii)
interacting interlaced TASEP layers, and iii) growing heap over 2D substrate
with a restricted minimal local height gradient. We demonstrate that the
coarse-grained behavior of this model is described by the two-dimensional
Kardar-Parisi-Zhang equation. The coefficients of different terms in this
hydrodynamic equation can be derived from the steady state flow-density curve,
the so called `fundamental' diagram. A conjecture concerning the analytical
form of this flow-density curve is presented and is verified numerically.Comment: 5 pages, 4 figure
On a conjecture of Widom
We prove a conjecture of H.Widom stated in [W] (math/0108008) about the
reality of eigenvalues of certain infinite matrices arising in asymptotic
analysis of large Toeplitz determinants. As a byproduct we obtain a new proof
of A.Okounkov's formula for the (determinantal) correlation functions of the
Schur measures on partitions.Comment: 9 page
Finding the Median (Obliviously) with Bounded Space
We prove that any oblivious algorithm using space to find the median of a
list of integers from requires time . This bound also applies to the problem of determining whether the median
is odd or even. It is nearly optimal since Chan, following Munro and Raman, has
shown that there is a (randomized) selection algorithm using only
registers, each of which can store an input value or -bit counter,
that makes only passes over the input. The bound also implies
a size lower bound for read-once branching programs computing the low order bit
of the median and implies the analog of for length oblivious branching programs
A Measure of Space for Computing over the Reals
We propose a new complexity measure of space for the BSS model of
computation. We define LOGSPACE\_W and PSPACE\_W complexity classes over the
reals. We prove that LOGSPACE\_W is included in NC^2\_R and in P\_W, i.e. is
small enough for being relevant. We prove that the Real Circuit Decision
Problem is P\_R-complete under LOGSPACE\_W reductions, i.e. that LOGSPACE\_W is
large enough for containing natural algorithms. We also prove that PSPACE\_W is
included in PAR\_R
On Conceptually Simple Algorithms for Variants of Online Bipartite Matching
We present a series of results regarding conceptually simple algorithms for
bipartite matching in various online and related models. We first consider a
deterministic adversarial model. The best approximation ratio possible for a
one-pass deterministic online algorithm is , which is achieved by any
greedy algorithm. D\"urr et al. recently presented a -pass algorithm called
Category-Advice that achieves approximation ratio . We extend their
algorithm to multiple passes. We prove the exact approximation ratio for the
-pass Category-Advice algorithm for all , and show that the
approximation ratio converges to the inverse of the golden ratio
as goes to infinity. The convergence is
extremely fast --- the -pass Category-Advice algorithm is already within
of the inverse of the golden ratio.
We then consider a natural greedy algorithm in the online stochastic IID
model---MinDegree. This algorithm is an online version of a well-known and
extensively studied offline algorithm MinGreedy. We show that MinDegree cannot
achieve an approximation ratio better than , which is guaranteed by any
consistent greedy algorithm in the known IID model.
Finally, following the work in Besser and Poloczek, we depart from an
adversarial or stochastic ordering and investigate a natural randomized
algorithm (MinRanking) in the priority model. Although the priority model
allows the algorithm to choose the input ordering in a general but well defined
way, this natural algorithm cannot obtain the approximation of the Ranking
algorithm in the ROM model
From interacting particle systems to random matrices
In this contribution we consider stochastic growth models in the
Kardar-Parisi-Zhang universality class in 1+1 dimension. We discuss the large
time distribution and processes and their dependence on the class on initial
condition. This means that the scaling exponents do not uniquely determine the
large time surface statistics, but one has to further divide into subclasses.
Some of the fluctuation laws were first discovered in random matrix models.
Moreover, the limit process for curved limit shape turned out to show up in a
dynamical version of hermitian random matrices, but this analogy does not
extend to the case of symmetric matrices. Therefore the connections between
growth models and random matrices is only partial.Comment: 18 pages, 8 figures; Contribution to StatPhys24 special issue; minor
corrections in scaling of section 2.
Slow decorrelations in KPZ growth
For stochastic growth models in the Kardar-Parisi-Zhang (KPZ) class in 1+1
dimensions, fluctuations grow as t^{1/3} during time t and the correlation
length at a fixed time scales as t^{2/3}. In this note we discuss the scale of
time correlations. For a representant of the KPZ class, the polynuclear growth
model, we show that the space-time is non-trivially fibred, having slow
directions with decorrelation exponent equal to 1 instead of the usual 2/3.
These directions are the characteristic curves of the PDE associated to the
surface's slope. As a consequence, previously proven results for space-like
paths will hold in the whole space-time except along the slow curves.Comment: 22 pages, 9 figures, LaTeX; Minor language revision
- …