549 research outputs found
On the switch Markov chain for perfect matchings
We study a simple Markov chain, the switch chain, on the set of all perfect matchings in a bipartite graph. This Markov chain was proposed by Diaconis, Graham and Holmes as a possible approach to a sampling problem arising in Statistics. We ask: for which hereditary classes of graphs is the Markov chain ergodic and for which is it rapidly mixing? We provide a precise answer to the ergodicity question and close bounds on the mixing question. We show for the first time that the mixing time of the switch chain is polynomial in the case of monotone graphs, a class that includes examples of interest in the statistical setting
Counting Perfect Matchings and the Switch Chain
We examine the problem of exactly or approximately counting all perfect matchings in hereditary classes of nonbipartite graphs. In particular, we consider the switch Markov chain of Diaconis, Graham, and Holmes. We determine the largest hereditary class for which the chain is ergodic, and define a large new hereditary class of graphs for which it is rapidly mixing. We go on to show that the chain has exponential mixing time for a slightly larger class. We also examine the question of ergodicity of the switch chain in an arbitrary graph. Finally, we give exact counting algorithms for three classes
Marathon: An open source software library for the analysis of Markov-Chain Monte Carlo algorithms
In this paper, we consider the Markov-Chain Monte Carlo (MCMC) approach for
random sampling of combinatorial objects. The running time of such an algorithm
depends on the total mixing time of the underlying Markov chain and is unknown
in general. For some Markov chains, upper bounds on this total mixing time
exist but are too large to be applicable in practice. We try to answer the
question, whether the total mixing time is close to its upper bounds, or if
there is a significant gap between them. In doing so, we present the software
library marathon which is designed to support the analysis of MCMC based
sampling algorithms. The main application of this library is to compute
properties of so-called state graphs which represent the structure of Markov
chains. We use marathon to investigate the quality of several bounding methods
on four well-known Markov chains for sampling perfect matchings and bipartite
graph realizations. In a set of experiments, we compute the total mixing time
and several of its bounds for a large number of input instances. We find that
the upper bound gained by the famous canonical path method is several
magnitudes larger than the total mixing time and deteriorates with growing
input size. In contrast, the spectral bound is found to be a precise
approximation of the total mixing time
Making Markov chains less lazy
The mixing time of an ergodic, reversible Markov chain can be bounded in
terms of the eigenvalues of the chain: specifically, the second-largest
eigenvalue and the smallest eigenvalue. It has become standard to focus only on
the second-largest eigenvalue, by making the Markov chain "lazy". (A lazy chain
does nothing at each step with probability at least 1/2, and has only
nonnegative eigenvalues.)
An alternative approach to bounding the smallest eigenvalue was given by
Diaconis and Stroock and Diaconis and Saloff-Coste. We give examples to show
that using this approach it can be quite easy to obtain a bound on the smallest
eigenvalue of a combinatorial Markov chain which is several orders of magnitude
below the best-known bound on the second-largest eigenvalue.Comment: 8 page
The mixing time of the switch Markov chains: a unified approach
Since 1997 a considerable effort has been spent to study the mixing time of
switch Markov chains on the realizations of graphic degree sequences of simple
graphs. Several results were proved on rapidly mixing Markov chains on
unconstrained, bipartite, and directed sequences, using different mechanisms.
The aim of this paper is to unify these approaches. We will illustrate the
strength of the unified method by showing that on any -stable family of
unconstrained/bipartite/directed degree sequences the switch Markov chain is
rapidly mixing. This is a common generalization of every known result that
shows the rapid mixing nature of the switch Markov chain on a region of degree
sequences. Two applications of this general result will be presented. One is an
almost uniform sampler for power-law degree sequences with exponent
. The other one shows that the switch Markov chain on the
degree sequence of an Erd\H{o}s-R\'enyi random graph is asymptotically
almost surely rapidly mixing if is bounded away from 0 and 1 by at least
.Comment: Clarification
Matching random colored points with rectangles
Let S ¿ [0, 1]2 be a set of n points, randomly and uniformly selected. Let R ¿ B be a random partition, or coloring, of S in which each point of S is included in R uniformly at random with probability 1/2. We study the random number M(n) of points of S that are covered by the rectangles of a maximum strong matching of S with axis-aligned rectangles. The matching consists of closed rectangles that cover exactly two points of S of the same color. A matching is strong if all its rectangles are pairwise disjoint. We prove that almost surely M(n) = 0.83 n for n large enough. Our approach is based on modeling a deterministic greedy matching algorithm, that runs over the random point set, as a Markov chain.Postprint (published version
Reversibility and further properties of FCFS infinite bipartite matching
The model of FCFS infinite bipartite matching was introduced in
caldentey-kaplan-weiss 2009. In this model there is a sequence of items that
are chosen i.i.d. from and an independent
sequence of items that are chosen i.i.d. from ,
and a bipartite compatibility graph between and
. Items of the two sequences are matched according to the
compatibility graph, and the matching is FCFS, each item in the one sequence is
matched to the earliest compatible unmatched item in the other sequence. In
adan-weiss 2011 a Markov chain associated with the matching was analyzed, a
condition for stability was verified, a product form stationary distribution
was derived and the rates of matches between compatible types
and were calculated.
In the current paper, we present several new results that unveil the
fundamental structure of the model. First, we provide a pathwise Loynes' type
construction which enables to prove the existence of a unique matching for the
model defined over all the integers. Second, we prove that the model is
dynamically reversible: we define an exchange transformation in which we
interchange the positions of each matched pair, and show that the items in the
resulting permuted sequences are again independent and i.i.d., and the matching
between them is FCFS in reversed time. Third, we obtain product form stationary
distributions of several new Markov chains associated with the model. As a by
product, we compute useful performance measures, for instance the link lengths
between matched items.Comment: 33 pages, 12 figure
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