97 research outputs found
Quiet Planting in the Locked Constraint Satisfaction Problems
We study the planted ensemble of locked constraint satisfaction problems. We
describe the connection between the random and planted ensembles. The use of
the cavity method is combined with arguments from reconstruction on trees and
first and second moment considerations; in particular the connection with the
reconstruction on trees appears to be crucial. Our main result is the location
of the hard region in the planted ensemble. In a part of that hard region
instances have with high probability a single satisfying assignment.Comment: 21 pages, revised versio
Inference in particle tracking experiments by passing messages between images
Methods to extract information from the tracking of mobile objects/particles
have broad interest in biological and physical sciences. Techniques based on
simple criteria of proximity in time-consecutive snapshots are useful to
identify the trajectories of the particles. However, they become problematic as
the motility and/or the density of the particles increases due to uncertainties
on the trajectories that particles followed during the images' acquisition
time. Here, we report an efficient method for learning parameters of the
dynamics of the particles from their positions in time-consecutive images. Our
algorithm belongs to the class of message-passing algorithms, known in computer
science, information theory and statistical physics as Belief Propagation (BP).
The algorithm is distributed, thus allowing parallel implementation suitable
for computations on multiple machines without significant inter-machine
overhead. We test our method on the model example of particle tracking in
turbulent flows, which is particularly challenging due to the strong transport
that those flows produce. Our numerical experiments show that the BP algorithm
compares in quality with exact Markov Chain Monte-Carlo algorithms, yet BP is
far superior in speed. We also suggest and analyze a random-distance model that
provides theoretical justification for BP accuracy. Methods developed here
systematically formulate the problem of particle tracking and provide fast and
reliable tools for its extensive range of applications.Comment: 18 pages, 9 figure
Neural-prior stochastic block model
The stochastic block model (SBM) is widely studied as a benchmark for graph
clustering aka community detection. In practice, graph data often come with
node attributes that bear additional information about the communities.
Previous works modeled such data by considering that the node attributes are
generated from the node community memberships. In this work, motivated by a
recent surge of works in signal processing using deep neural networks as
priors, we propose to model the communities as being determined by the node
attributes rather than the opposite. We define the corresponding model; we call
it the neural-prior SBM. We propose an algorithm, stemming from statistical
physics, based on a combination of belief propagation and approximate message
passing. We analyze the performance of the algorithm as well as the
Bayes-optimal performance. We identify detectability and exact recovery phase
transitions, as well as an algorithmically hard region. The proposed model and
algorithm can be used as a benchmark for both theory and algorithms. To
illustrate this, we compare the optimal performances to the performance of
simple graph neural networks
Next nearest neighbour Ising models on random graphs
This paper develops results for the next nearest neighbour Ising model on
random graphs. Besides being an essential ingredient in classic models for
frustrated systems, second neighbour interactions interactions arise naturally
in several applications such as the colour diversity problem and graphical
games. We demonstrate ensembles of random graphs, including regular
connectivity graphs, that have a periodic variation of free energy, with either
the ratio of nearest to next nearest couplings, or the mean number of nearest
neighbours. When the coupling ratio is integer paramagnetic phases can be found
at zero temperature. This is shown to be related to the locked or unlocked
nature of the interactions. For anti-ferromagnetic couplings, spin glass phases
are demonstrated at low temperature. The interaction structure is formulated as
a factor graph, the solution on a tree is developed. The replica symmetric and
energetic one-step replica symmetry breaking solution is developed using the
cavity method. We calculate within these frameworks the phase diagram and
demonstrate the existence of dynamical transitions at zero temperature for
cases of anti-ferromagnetic coupling on regular and inhomogeneous random
graphs.Comment: 55 pages, 15 figures, version 2 with minor revisions, to be published
J. Stat. Mec
Message passing for vertex covers
Constructing a minimal vertex cover of a graph can be seen as a prototype for
a combinatorial optimization problem under hard constraints. In this paper, we
develop and analyze message passing techniques, namely warning and survey
propagation, which serve as efficient heuristic algorithms for solving these
computational hard problems. We show also, how previously obtained results on
the typical-case behavior of vertex covers of random graphs can be recovered
starting from the message passing equations, and how they can be extended.Comment: 25 pages, 9 figures - version accepted for publication in PR
Exact controllability of multiplex networks
Date of Acceptance: 11/09/2014Peer reviewedPublisher PD
Belief-Propagation for Weighted b-Matchings on Arbitrary Graphs and its Relation to Linear Programs with Integer Solutions
We consider the general problem of finding the minimum weight \bm-matching
on arbitrary graphs. We prove that, whenever the linear programming (LP)
relaxation of the problem has no fractional solutions, then the belief
propagation (BP) algorithm converges to the correct solution. We also show that
when the LP relaxation has a fractional solution then the BP algorithm can be
used to solve the LP relaxation. Our proof is based on the notion of graph
covers and extends the analysis of (Bayati-Shah-Sharma 2005 and Huang-Jebara
2007}.
These results are notable in the following regards: (1) It is one of a very
small number of proofs showing correctness of BP without any constraint on the
graph structure. (2) Variants of the proof work for both synchronous and
asynchronous BP; it is the first proof of convergence and correctness of an
asynchronous BP algorithm for a combinatorial optimization problem.Comment: 28 pages, 2 figures. Submitted to SIAM journal on Discrete
Mathematics on March 19, 2009; accepted for publication (in revised form)
August 30, 2010; published electronically July 1, 201
Reconstruction of Random Colourings
Reconstruction problems have been studied in a number of contexts including
biology, information theory and and statistical physics. We consider the
reconstruction problem for random -colourings on the -ary tree for
large . Bhatnagar et. al. showed non-reconstruction when and reconstruction when . We tighten this result and show non-reconstruction when and reconstruction when .Comment: Added references, updated notatio
Sudden emergence of q-regular subgraphs in random graphs
We investigate the computationally hard problem whether a random graph of
finite average vertex degree has an extensively large -regular subgraph,
i.e., a subgraph with all vertices having degree equal to . We reformulate
this problem as a constraint-satisfaction problem, and solve it using the
cavity method of statistical physics at zero temperature. For , we find
that the first large -regular subgraphs appear discontinuously at an average
vertex degree c_\reg{3} \simeq 3.3546 and contain immediately about 24% of
all vertices in the graph. This transition is extremely close to (but different
from) the well-known 3-core percolation point c_\cor{3} \simeq 3.3509. For
, the -regular subgraph percolation threshold is found to coincide with
that of the -core.Comment: 7 pages, 5 figure
Distribution of partition function zeros of the model on the Bethe lattice
The distribution of partition function zeros is studied for the model
of spin glasses on the Bethe lattice. We find a relation between the
distribution of complex cavity fields and the density of zeros, which enables
us to obtain the density of zeros for the infinite system size by using the
cavity method. The phase boundaries thus derived from the location of the zeros
are consistent with the results of direct analytical calculations. This is the
first example in which the spin glass transition is related to the distribution
of zeros directly in the thermodynamical limit. We clarify how the spin glass
transition is characterized by the zeros of the partition function. It is also
shown that in the spin glass phase a continuous distribution of singularities
touches the axes of real field and temperature.Comment: 23 pages, 12 figure
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