85 research outputs found
Approximating the partition function of the ferromagnetic Potts model
We provide evidence that it is computationally difficult to approximate the
partition function of the ferromagnetic q-state Potts model when q>2.
Specifically we show that the partition function is hard for the complexity
class #RHPi_1 under approximation-preserving reducibility. Thus, it is as hard
to approximate the partition function as it is to find approximate solutions to
a wide range of counting problems, including that of determining the number of
independent sets in a bipartite graph. Our proof exploits the first order phase
transition of the "random cluster" model, which is a probability distribution
on graphs that is closely related to the q-state Potts model.Comment: Minor correction
The Complexity of Computing the Sign of the Tutte Polynomial
We study the complexity of computing the sign of the Tutte polynomial of a
graph. As there are only three possible outcomes (positive, negative, and
zero), this seems at first sight more like a decision problem than a counting
problem. Surprisingly, however, there are large regions of the parameter space
for which computing the sign of the Tutte polynomial is actually #P-hard. As a
trivial consequence, approximating the polynomial is also #P-hard in this case.
Thus, approximately evaluating the Tutte polynomial in these regions is as hard
as exactly counting the satisfying assignments to a CNF Boolean formula. For
most other points in the parameter space, we show that computing the sign of
the polynomial is in FP, whereas approximating the polynomial can be done in
polynomial time with an NP oracle. As a special case, we completely resolve the
complexity of computing the sign of the chromatic polynomial - this is easily
computable at q=2 and when q is less than or equal to 32/27, and is NP-hard to
compute for all other values of the parameter q.Comment: minor updates. This is the final version (to appear in SICOMP
Negative association in uniform forests and connected graphs
We consider three probability measures on subsets of edges of a given finite
graph , namely those which govern, respectively, a uniform forest, a uniform
spanning tree, and a uniform connected subgraph. A conjecture concerning the
negative association of two edges is reviewed for a uniform forest, and a
related conjecture is posed for a uniform connected subgraph. The former
conjecture is verified numerically for all graphs having eight or fewer
vertices, or having nine vertices and no more than eighteen edges, using a
certain computer algorithm which is summarised in this paper. Negative
association is known already to be valid for a uniform spanning tree. The three
cases of uniform forest, uniform spanning tree, and uniform connected subgraph
are special cases of a more general conjecture arising from the random-cluster
model of statistical mechanics.Comment: With minor correction
On the Exact Evaluation of Certain Instances of the Potts Partition Function by Quantum Computers
We present an efficient quantum algorithm for the exact evaluation of either
the fully ferromagnetic or anti-ferromagnetic q-state Potts partition function
Z for a family of graphs related to irreducible cyclic codes. This problem is
related to the evaluation of the Jones and Tutte polynomials. We consider the
connection between the weight enumerator polynomial from coding theory and Z
and exploit the fact that there exists a quantum algorithm for efficiently
estimating Gauss sums in order to obtain the weight enumerator for a certain
class of linear codes. In this way we demonstrate that for a certain class of
sparse graphs, which we call Irreducible Cyclic Cocycle Code (ICCC_\epsilon)
graphs, quantum computers provide a polynomial speed up in the difference
between the number of edges and vertices of the graph, and an exponential speed
up in q, over the best classical algorithms known to date
A graph polynomial for independent sets of bipartite graphs
We introduce a new graph polynomial that encodes interesting properties of
graphs, for example, the number of matchings and the number of perfect
matchings. Most importantly, for bipartite graphs the polynomial encodes the
number of independent sets (#BIS).
We analyze the complexity of exact evaluation of the polynomial at rational
points and show that for most points exact evaluation is #P-hard (assuming the
generalized Riemann hypothesis) and for the rest of the points exact evaluation
is trivial.
We conjecture that a natural Markov chain can be used to approximately
evaluate the polynomial for a range of parameters. The conjecture, if true,
would imply an approximate counting algorithm for #BIS, a problem shown, by
[Dyer et al. 2004], to be complete (with respect to, so called, AP-reductions)
for a rich logically defined sub-class of #P. We give a mild support for our
conjecture by proving that the Markov chain is rapidly mixing on trees. As a
by-product we show that the "single bond flip" Markov chain for the random
cluster model is rapidly mixing on constant tree-width graphs
Probability around the Quantum Gravity. Part 1: Pure Planar Gravity
In this paper we study stochastic dynamics which leaves quantum gravity
equilibrium distribution invariant. We start theoretical study of this dynamics
(earlier it was only used for Monte-Carlo simulation). Main new results concern
the existence and properties of local correlation functions in the
thermodynamic limit. The study of dynamics constitutes a third part of the
series of papers where more general class of processes were studied (but it is
self-contained), those processes have some universal significance in
probability and they cover most concrete processes, also they have many
examples in computer science and biology. At the same time the paper can serve
an introduction to quantum gravity for a probabilist: we give a rigorous
exposition of quantum gravity in the planar pure gravity case. Mostly we use
combinatorial techniques, instead of more popular in physics random matrix
models, the central point is the famous exponent.Comment: 40 pages, 11 figure
Graphs with many strong orientations
We establish mild conditions under which a possibly irregular, sparse graph
has "many" strong orientations. Given a graph on vertices, orient
each edge in either direction with probability independently. We show
that if satisfies a minimum degree condition of and has
Cheeger constant at least , then the
resulting randomly oriented directed graph is strongly connected with high
probability. This Cheeger constant bound can be replaced by an analogous
spectral condition via the Cheeger inequality. Additionally, we provide an
explicit construction to show our minimum degree condition is tight while the
Cheeger constant bound is tight up to a factor.Comment: 14 pages, 4 figures; revised version includes more background and
minor changes that better clarify the expositio
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