191 research outputs found
The Lovasz number of random graphs
We study the Lovasz number theta along with two further SDP relaxations
theta1, theta1/2 of the independence number and the corresponding relaxations
of the chromatic number on random graphs G(n,p). We prove that these
relaxations are concentrated about their means Moreover, extending a result of
Juhasz, we compute the asymptotic value of the relaxations for essentially the
entire range of edge probabilities p. As an application, we give an improved
algorithm for approximating the independence number in polynomial expected
time, thereby extending a result of Krivelevich and Vu. We also improve on the
analysis of an algorithm of Krivelevich for deciding whether G(n,p) is
k-colorable
Random Constraint Satisfaction Problems
Random instances of constraint satisfaction problems such as k-SAT provide
challenging benchmarks. If there are m constraints over n variables there is
typically a large range of densities r=m/n where solutions are known to exist
with probability close to one due to non-constructive arguments. However, no
algorithms are known to find solutions efficiently with a non-vanishing
probability at even much lower densities. This fact appears to be related to a
phase transition in the set of all solutions. The goal of this extended
abstract is to provide a perspective on this phenomenon, and on the
computational challenge that it poses
The Lovász Number of Random Graphs
This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We study the Lovász number along with two related SDP relaxations , of the independence number and the corresponding relaxations , , of the chromatic number on random graphs . We prove that are concentrated about their means, and that in the case is a constant. As an application, we give improved algorithms for approximating the independence number of and for deciding -colourability in polynomial expected time.Peer Reviewe
The condensation phase transition in the regular -SAT model
Much of the recent work on random constraint satisfaction problems has been
inspired by ingenious but non-rigorous approaches from physics. The physics
predictions typically come in the form of distributional fixed point problems
that are intended to mimic Belief Propagation, a message passing algorithm,
applied to the random CSP. In this paper we propose a novel method for
harnessing Belief Propagation directly to obtain a rigorous proof of such a
prediction, namely the existence and location of a condensation phase
transition in the random regular -SAT model.Comment: Revised version based on arXiv:1504.03975, version
Going after the k-SAT Threshold
Random -SAT is the single most intensely studied example of a random
constraint satisfaction problem. But despite substantial progress over the past
decade, the threshold for the existence of satisfying assignments is not known
precisely for any . The best current results, based on the second
moment method, yield upper and lower bounds that differ by an additive , a term that is unbounded in (Achlioptas, Peres: STOC 2003).
The basic reason for this gap is the inherent asymmetry of the Boolean value
`true' and `false' in contrast to the perfect symmetry, e.g., among the various
colors in a graph coloring problem. Here we develop a new asymmetric second
moment method that allows us to tackle this issue head on for the first time in
the theory of random CSPs. This technique enables us to compute the -SAT
threshold up to an additive . Independently of
the rigorous work, physicists have developed a sophisticated but non-rigorous
technique called the "cavity method" for the study of random CSPs (M\'ezard,
Parisi, Zecchina: Science 2002). Our result matches the best bound that can be
obtained from the so-called "replica symmetric" version of the cavity method,
and indeed our proof directly harnesses parts of the physics calculations
Belief Propagation on replica symmetric random factor graph models
According to physics predictions, the free energy of random factor graph
models that satisfy a certain "static replica symmetry" condition can be
calculated via the Belief Propagation message passing scheme [Krzakala et al.,
PNAS 2007]. Here we prove this conjecture for two general classes of random
factor graph models, namely Poisson random factor graphs and random regular
factor graphs. Specifically, we show that the messages constructed just as in
the case of acyclic factor graphs asymptotically satisfy the Belief Propagation
equations and that the free energy density is given by the Bethe free energy
formula
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