345 research outputs found
Explicit expanders with cutoff phenomena
The cutoff phenomenon describes a sharp transition in the convergence of an
ergodic finite Markov chain to equilibrium. Of particular interest is
understanding this convergence for the simple random walk on a bounded-degree
expander graph. The first example of a family of bounded-degree graphs where
the random walk exhibits cutoff in total-variation was provided only very
recently, when the authors showed this for a typical random regular graph.
However, no example was known for an explicit (deterministic) family of
expanders with this phenomenon. Here we construct a family of cubic expanders
where the random walk from a worst case initial position exhibits
total-variation cutoff. Variants of this construction give cubic expanders
without cutoff, as well as cubic graphs with cutoff at any prescribed
time-point.Comment: 17 pages, 2 figure
A temporal Central Limit Theorem for real-valued cocycles over rotations
We consider deterministic random walks on the real line driven by irrational
rotations, or equivalently, skew product extensions of a rotation by
where the skewing cocycle is a piecewise constant mean zero function with a
jump by one at a point . When is badly approximable and
is badly approximable with respect to , we prove a Temporal Central
Limit theorem (in the terminology recently introduced by D.Dolgopyat and
O.Sarig), namely we show that for any fixed initial point, the occupancy random
variables, suitably rescaled, converge to a Gaussian random variable. This
result generalizes and extends a theorem by J. Beck for the special case when
is quadratic irrational, is rational and the initial point is
the origin, recently reproved and then generalized to cover any initial point
using geometric renormalization arguments by Avila-Dolgopyat-Duryev-Sarig
(Israel J., 2015) and Dolgopyat-Sarig (J. Stat. Physics, 2016). We also use
renormalization, but in order to treat irrational values of , instead of
geometric arguments, we use the renormalization associated to the continued
fraction algorithm and dynamical Ostrowski expansions. This yields a suitable
symbolic coding framework which allows us to reduce the main result to a CLT
for non homogeneous Markov chains.Comment: a few typos corrected, 28 pages, 4 figure
Consistency of Markov chain quasi-Monte Carlo on continuous state spaces
The random numbers driving Markov chain Monte Carlo (MCMC) simulation are
usually modeled as independent U(0,1) random variables. Tribble [Markov chain
Monte Carlo algorithms using completely uniformly distributed driving sequences
(2007) Stanford Univ.] reports substantial improvements when those random
numbers are replaced by carefully balanced inputs from completely uniformly
distributed sequences. The previous theoretical justification for using
anything other than i.i.d. U(0,1) points shows consistency for estimated means,
but only applies for discrete stationary distributions. We extend those results
to some MCMC algorithms for continuous stationary distributions. The main
motivation is the search for quasi-Monte Carlo versions of MCMC. As a side
benefit, the results also establish consistency for the usual method of using
pseudo-random numbers in place of random ones.Comment: Published in at http://dx.doi.org/10.1214/10-AOS831 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
How quickly can we sample a uniform domino tiling of the 2L x 2L square via Glauber dynamics?
TThe prototypical problem we study here is the following. Given a square, there are approximately ways to tile it with
dominos, i.e. with horizontal or vertical rectangles, where
is Catalan's constant [Kasteleyn '61, Temperley-Fisher '61]. A
conceptually simple (even if computationally not the most efficient) way of
sampling uniformly one among so many tilings is to introduce a Markov Chain
algorithm (Glauber dynamics) where, with rate , two adjacent horizontal
dominos are flipped to vertical dominos, or vice-versa. The unique invariant
measure is the uniform one and a classical question [Wilson
2004,Luby-Randall-Sinclair 2001] is to estimate the time it takes to
approach equilibrium (i.e. the running time of the algorithm). In
[Luby-Randall-Sinclair 2001, Randall-Tetali 2000], fast mixin was proven:
for some finite . Here, we go much beyond and show that . Our result applies to rather general domain
shapes (not just the square), provided that the typical height
function associated to the tiling is macroscopically planar in the large
limit, under the uniform measure (this is the case for instance for the
Temperley-type boundary conditions considered in [Kenyon 2000]). Also, our
method extends to some other types of tilings of the plane, for instance the
tilings associated to dimer coverings of the hexagon or square-hexagon
lattices.Comment: to appear on PTRF; 42 pages, 9 figures; v2: typos corrected,
references adde
Tight Bounds for Randomized Load Balancing on Arbitrary Network Topologies
We consider the problem of balancing load items (tokens) in networks.
Starting with an arbitrary load distribution, we allow nodes to exchange tokens
with their neighbors in each round. The goal is to achieve a distribution where
all nodes have nearly the same number of tokens.
For the continuous case where tokens are arbitrarily divisible, most load
balancing schemes correspond to Markov chains, whose convergence is fairly
well-understood in terms of their spectral gap. However, in many applications,
load items cannot be divided arbitrarily, and we need to deal with the discrete
case where the load is composed of indivisible tokens. This discretization
entails a non-linear behavior due to its rounding errors, which makes this
analysis much harder than in the continuous case.
We investigate several randomized protocols for different communication
models in the discrete case. As our main result, we prove that for any regular
network in the matching model, all nodes have the same load up to an additive
constant in (asymptotically) the same number of rounds as required in the
continuous case. This generalizes and tightens the previous best result, which
only holds for expander graphs, and demonstrates that there is almost no
difference between the discrete and continuous cases. Our results also provide
a positive answer to the question of how well discrete load balancing can be
approximated by (continuous) Markov chains, which has been posed by many
researchers.Comment: 74 pages, 4 figure
Profile cut-off phenomenon for the ergodic Feller root process
The present manuscript is devoted to the study of the convergence to
equilibrium as the noise intensity tends to zero for ergodic
random systems out of equilibrium of the type \begin{align*} \mathrm{d}
X^{\varepsilon}_t(x) = (\mathfrak{b}-\mathfrak{a}
X^{\varepsilon}_t(x))\mathrm{d} t+\varepsilon
\sqrt{X^{\varepsilon}_t(x)}\mathrm{d} B_t, \quad X^{\varepsilon}_0(x) = x,
\quad t\geqslant 0, \end{align*} where , and
are constants, and is a one
dimensional standard Brownian motion. More precisely, we show the strongest
notion of asymptotic profile cut-off phenomenon in the total variation distance
and in the renormalized Wasserstein distance when tends to zero
with explicit cut-off time, explicit time window, and explicit profile
function. In addition, asymptotics of the so-called mixing times are given
explicitly
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