5,082 research outputs found
On k-Convex Polygons
We introduce a notion of -convexity and explore polygons in the plane that
have this property. Polygons which are \mbox{-convex} can be triangulated
with fast yet simple algorithms. However, recognizing them in general is a
3SUM-hard problem. We give a characterization of \mbox{-convex} polygons, a
particularly interesting class, and show how to recognize them in \mbox{} time. A description of their shape is given as well, which leads to
Erd\H{o}s-Szekeres type results regarding subconfigurations of their vertex
sets. Finally, we introduce the concept of generalized geometric permutations,
and show that their number can be exponential in the number of
\mbox{-convex} objects considered.Comment: 23 pages, 19 figure
Asymptotically exponential hitting times and metastability: a pathwise approach without reversibility
We study the hitting times of Markov processes to target set , starting
from a reference configuration or its basin of attraction. The
configuration can correspond to the bottom of a (meta)stable well, while
the target could be either a set of saddle (exit) points of the well, or a
set of further (meta)stable configurations. Three types of results are
reported: (1) A general theory is developed, based on the path-wise approach to
metastability, which has three important attributes. First, it is general in
that it does not assume reversibility of the process, does not focus only on
hitting times to rare events and does not assume a particular starting measure.
Second, it relies only on the natural hypothesis that the mean hitting time to
is asymptotically longer than the mean recurrence time to or .
Third, despite its mathematical simplicity, the approach yields precise and
explicit bounds on the corrections to exponentiality. (2) We compare and relate
different metastability conditions proposed in the literature so to eliminate
potential sources of confusion. This is specially relevant for evolutions of
infinite-volume systems, whose treatment depends on whether and how relevant
parameters (temperature, fields) are adjusted. (3) We introduce the notion of
early asymptotic exponential behavior to control time scales asymptotically
smaller than the mean-time scale. This control is particularly relevant for
systems with unbounded state space where nucleations leading to exit from
metastability can happen anywhere in the volume. We provide natural sufficient
conditions on recurrence times for this early exponentiality to hold and show
that it leads to estimations of probability density functions
Factor models on locally tree-like graphs
We consider homogeneous factor models on uniformly sparse graph sequences
converging locally to a (unimodular) random tree , and study the existence
of the free energy density , the limit of the log-partition function
divided by the number of vertices as tends to infinity. We provide a
new interpolation scheme and use it to prove existence of, and to explicitly
compute, the quantity subject to uniqueness of a relevant Gibbs measure
for the factor model on . By way of example we compute for the
independent set (or hard-core) model at low fugacity, for the ferromagnetic
Ising model at all parameter values, and for the ferromagnetic Potts model with
both weak enough and strong enough interactions. Even beyond uniqueness regimes
our interpolation provides useful explicit bounds on . In the regimes in
which we establish existence of the limit, we show that it coincides with the
Bethe free energy functional evaluated at a suitable fixed point of the belief
propagation (Bethe) recursions on . In the special case that has a
Galton-Watson law, this formula coincides with the nonrigorous "Bethe
prediction" obtained by statistical physicists using the "replica" or "cavity"
methods. Thus our work is a rigorous generalization of these heuristic
calculations to the broader class of sparse graph sequences converging locally
to trees. We also provide a variational characterization for the Bethe
prediction in this general setting, which is of independent interest.Comment: Published in at http://dx.doi.org/10.1214/12-AOP828 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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