421 research outputs found
Approximating the Permanent with Fractional Belief Propagation
We discuss schemes for exact and approximate computations of permanents, and
compare them with each other. Specifically, we analyze the Belief Propagation
(BP) approach and its Fractional Belief Propagation (FBP) generalization for
computing the permanent of a non-negative matrix. Known bounds and conjectures
are verified in experiments, and some new theoretical relations, bounds and
conjectures are proposed. The Fractional Free Energy (FFE) functional is
parameterized by a scalar parameter , where
corresponds to the BP limit and corresponds to the exclusion
principle (but ignoring perfect matching constraints) Mean-Field (MF) limit.
FFE shows monotonicity and continuity with respect to . For every
non-negative matrix, we define its special value to be the
for which the minimum of the -parameterized FFE functional is
equal to the permanent of the matrix, where the lower and upper bounds of the
-interval corresponds to respective bounds for the permanent. Our
experimental analysis suggests that the distribution of varies for
different ensembles but always lies within the interval.
Moreover, for all ensembles considered the behavior of is highly
distinctive, offering an emprirical practical guidance for estimating
permanents of non-negative matrices via the FFE approach.Comment: 42 pages, 14 figure
Palette-colouring: a belief-propagation approach
We consider a variation of the prototype combinatorial-optimisation problem
known as graph-colouring. Our optimisation goal is to colour the vertices of a
graph with a fixed number of colours, in a way to maximise the number of
different colours present in the set of nearest neighbours of each given
vertex. This problem, which we pictorially call "palette-colouring", has been
recently addressed as a basic example of problem arising in the context of
distributed data storage. Even though it has not been proved to be NP complete,
random search algorithms find the problem hard to solve. Heuristics based on a
naive belief propagation algorithm are observed to work quite well in certain
conditions. In this paper, we build upon the mentioned result, working out the
correct belief propagation algorithm, which needs to take into account the
many-body nature of the constraints present in this problem. This method
improves the naive belief propagation approach, at the cost of increased
computational effort. We also investigate the emergence of a satisfiable to
unsatisfiable "phase transition" as a function of the vertex mean degree, for
different ensembles of sparse random graphs in the large size ("thermodynamic")
limit.Comment: 22 pages, 7 figure
On Cavity Approximations for Graphical Models
We reformulate the Cavity Approximation (CA), a class of algorithms recently
introduced for improving the Bethe approximation estimates of marginals in
graphical models. In our new formulation, which allows for the treatment of
multivalued variables, a further generalization to factor graphs with arbitrary
order of interaction factors is explicitly carried out, and a message passing
algorithm that implements the first order correction to the Bethe approximation
is described. Furthermore we investigate an implementation of the CA for
pairwise interactions. In all cases considered we could confirm that CA[k] with
increasing provides a sequence of approximations of markedly increasing
precision. Furthermore in some cases we could also confirm the general
expectation that the approximation of order , whose computational complexity
is has an error that scales as with the size of the
system. We discuss the relation between this approach and some recent
developments in the field.Comment: Extension to factor graphs and comments on related work adde
Single image example-based super-resolution using cross-scale patch matching and Markov random field modelling
Example-based super-resolution has become increasingly popular over the last few years for its ability to overcome the limitations of classical multi-frame approach. In this paper we present a new example-based method that uses the input low-resolution image itself as a search space for high-resolution patches by exploiting self-similarity across different resolution scales. Found examples are combined in a high-resolution image by the means of Markov Random Field modelling that forces their global agreement. Additionally, we apply back-projection and steering kernel regression as post-processing techniques. In this way, we are able to produce sharp and artefact-free results that are comparable or better than standard interpolation and state-of-the-art super-resolution techniques
Gaussian Belief with dynamic data and in dynamic network
In this paper we analyse Belief Propagation over a Gaussian model in a
dynamic environment. Recently, this has been proposed as a method to average
local measurement values by a distributed protocol ("Consensus Propagation",
Moallemi & Van Roy, 2006), where the average is available for read-out at every
single node. In the case that the underlying network is constant but the values
to be averaged fluctuate ("dynamic data"), convergence and accuracy are
determined by the spectral properties of an associated Ruelle-Perron-Frobenius
operator. For Gaussian models on Erdos-Renyi graphs, numerical computation
points to a spectral gap remaining in the large-size limit, implying
exceptionally good scalability. In a model where the underlying network also
fluctuates ("dynamic network"), averaging is more effective than in the dynamic
data case. Altogether, this implies very good performance of these methods in
very large systems, and opens a new field of statistical physics of large (and
dynamic) information systems.Comment: 5 pages, 7 figure
Statistical-mechanical iterative algorithms on complex networks
The Ising models have been applied for various problems on information
sciences, social sciences, and so on. In many cases, solving these problems
corresponds to minimizing the Bethe free energy. To minimize the Bethe free
energy, a statistical-mechanical iterative algorithm is often used. We study
the statistical-mechanical iterative algorithm on complex networks. To
investigate effects of heterogeneous structures on the iterative algorithm, we
introduce an iterative algorithm based on information of heterogeneity of
complex networks, in which higher-degree nodes are likely to be updated more
frequently than lower-degree ones. Numerical experiments clarified that the
usage of the information of heterogeneity affects the algorithm in BA networks,
but does not influence that in ER networks. It is revealed that information of
the whole system propagates rapidly through such high-degree nodes in the case
of Barab{\'a}si-Albert's scale-free networks.Comment: 7 pages, 6 figure
Sums over geometries and improvements on the mean field approximation
The saddle points of a Lagrangian due to Efetov are analyzed. This Lagrangian
was originally proposed as a tool for calculating systematic corrections to the
Bethe approximation, a mean-field approximation which is important in
statistical mechanics, glasses, coding theory, and combinatorial optimization.
Detailed analysis shows that the trivial saddle point generates a sum over
geometries reminiscent of dynamically triangulated quantum gravity, which
suggests new possibilities to design sums over geometries for the specific
purpose of obtaining improved mean field approximations to -dimensional
theories. In the case of the Efetov theory, the dominant geometries are locally
tree-like, and the sum over geometries diverges in a way that is similar to
quantum gravity's divergence when all topologies are included. Expertise from
the field of dynamically triangulated quantum gravity about sums over
geometries may be able to remedy these defects and fulfill the Efetov theory's
original promise. The other saddle points of the Efetov Lagrangian are also
analyzed; the Hessian at these points is nonnormal and pseudo-Hermitian, which
is unusual for bosonic theories. The standard formula for Gaussian integrals is
generalized to nonnormal kernels.Comment: Accepted for publication in Physical Review D, probably in November
2007. At the reviewer's request, material was added which made the article
more assertive, confident, and clear. No changes in substanc
Belief propagation algorithm for computing correlation functions in finite-temperature quantum many-body systems on loopy graphs
Belief propagation -- a powerful heuristic method to solve inference problems
involving a large number of random variables -- was recently generalized to
quantum theory. Like its classical counterpart, this algorithm is exact on
trees when the appropriate independence conditions are met and is expected to
provide reliable approximations when operated on loopy graphs. In this paper,
we benchmark the performances of loopy quantum belief propagation (QBP) in the
context of finite-tempereture quantum many-body physics. Our results indicate
that QBP provides reliable estimates of the high-temperature correlation
function when the typical loop size in the graph is large. As such, it is
suitable e.g. for the study of quantum spin glasses on Bethe lattices and the
decoding of sparse quantum error correction codes.Comment: 5 pages, 4 figure
Role of the Tracy-Widom distribution in the finite-size fluctuations of the critical temperature of the Sherrington-Kirkpatrick spin glass
We investigate the finite-size fluctuations due to quenched disorder of the
critical temperature of the Sherrington-Kirkpatrick spin glass. In order to
accomplish this task, we perform a finite-size analysis of the spectrum of the
susceptibility matrix obtained via the Plefka expansion. By exploiting results
from random matrix theory, we obtain that the fluctuations of the critical
temperature are described by the Tracy-Widom distribution with a non-trivial
scaling exponent 2/3
Inference by replication in densely connected systems
An efficient Bayesian inference method for problems that can be mapped onto
dense graphs is presented. The approach is based on message passing where
messages are averaged over a large number of replicated variable systems
exposed to the same evidential nodes. An assumption about the symmetry of the
solutions is required for carrying out the averages; here we extend the
previous derivation based on a replica symmetric (RS) like structure to include
a more complex one-step replica symmetry breaking (1RSB)-like ansatz. To
demonstrate the potential of the approach it is employed for studying critical
properties of the Ising linear perceptron and for multiuser detection in Code
Division Multiple Access (CDMA) under different noise models. Results obtained
under the RS assumption in the non-critical regime give rise to a highly
efficient signal detection algorithm in the context of CDMA; while in the
critical regime one observes a first order transition line that ends in a
continuous phase transition point. Finite size effects are also observed. While
the 1RSB ansatz is not required for the original problems, it was applied to
the CDMA signal detection problem with a more complex noise model that exhibits
RSB behaviour, resulting in an improvement in performance.Comment: 47 pages, 7 figure
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