352 research outputs found
Random k-SAT and the Power of Two Choices
We study an Achlioptas-process version of the random k-SAT process: a bounded
number of k-clauses are drawn uniformly at random at each step, and exactly one
added to the growing formula according to a particular rule. We prove the
existence of a rule that shifts the satisfiability threshold. This extends a
well-studied area of probabilistic combinatorics (Achlioptas processes) to
random CSP's. In particular, while a rule to delay the 2-SAT threshold was
known previously, this is the first proof of a rule to shift the threshold of
k-SAT for k >= 3.
We then propose a gap decision problem based upon this semi-random model. The
aim of the problem is to investigate the hardness of the random k-SAT decision
problem, as opposed to the problem of finding an assignment or certificate of
unsatisfiability. Finally, we discuss connections to the study of Achlioptas
random graph processes.Comment: 13 page
Birthday Inequalities, Repulsion, and Hard Spheres
We study a birthday inequality in random geometric graphs: the probability of
the empty graph is upper bounded by the product of the probabilities that each
edge is absent. We show the birthday inequality holds at low densities, but
does not hold in general. We give three different applications of the birthday
inequality in statistical physics and combinatorics: we prove lower bounds on
the free energy of the hard sphere model and upper bounds on the number of
independent sets and matchings of a given size in d-regular graphs.
The birthday inequality is implied by a repulsion inequality: the expected
volume of the union of spheres of radius r around n randomly placed centers
increases if we condition on the event that the centers are at pairwise
distance greater than r. Surprisingly we show that the repulsion inequality is
not true in general, and in particular that it fails in 24-dimensional
Euclidean space: conditioning on the pairwise repulsion of centers of
24-dimensional spheres can decrease the expected volume of their union
Spectral Thresholds in the Bipartite Stochastic Block Model
We consider a bipartite stochastic block model on vertex sets and
, with planted partitions in each, and ask at what densities efficient
algorithms can recover the partition of the smaller vertex set.
When , multiple thresholds emerge. We first locate a sharp
threshold for detection of the partition, in the sense of the results of
\cite{mossel2012stochastic,mossel2013proof} and \cite{massoulie2014community}
for the stochastic block model. We then show that at a higher edge density, the
singular vectors of the rectangular biadjacency matrix exhibit a localization /
delocalization phase transition, giving recovery above the threshold and no
recovery below. Nevertheless, we propose a simple spectral algorithm, Diagonal
Deletion SVD, which recovers the partition at a nearly optimal edge density.
The bipartite stochastic block model studied here was used by
\cite{feldman2014algorithm} to give a unified algorithm for recovering planted
partitions and assignments in random hypergraphs and random -SAT formulae
respectively. Our results give the best known bounds for the clause density at
which solutions can be found efficiently in these models as well as showing a
barrier to further improvement via this reduction to the bipartite block model.Comment: updated version, will appear in COLT 201
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
On kissing numbers and spherical codes in high dimensions
We prove a lower bound of on the
kissing number in dimension . This improves the classical lower bound of
Chabauty, Shannon, and Wyner by a linear factor in the dimension. We obtain a
similar linear factor improvement to the best known lower bound on the maximal
size of a spherical code of acute angle in high dimensions
On the hard sphere model and sphere packings in high dimensions
We prove a lower bound on the entropy of sphere packings of of
density . The entropy measures how plentiful such
packings are, and our result is significantly stronger than the trivial lower
bound that can be obtained from the mere existence of a dense packing. Our
method also provides a new, statistical-physics-based proof of the lower bound on the maximum sphere packing density by showing
that the expected packing density of a random configuration from the hard
sphere model is at least when the
ratio of the fugacity parameter to the volume covered by a single sphere is at
least . Such a bound on the sphere packing density was first achieved
by Rogers, with subsequent improvements to the leading constant by Davenport
and Rogers, Ball, Vance, and Venkatesh
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's
We present an algorithm for recovering planted solutions in two well-known
models, the stochastic block model and planted constraint satisfaction
problems, via a common generalization in terms of random bipartite graphs. Our
algorithm matches up to a constant factor the best-known bounds for the number
of edges (or constraints) needed for perfect recovery and its running time is
linear in the number of edges used. The time complexity is significantly better
than both spectral and SDP-based approaches.
The main contribution of the algorithm is in the case of unequal sizes in the
bipartition (corresponding to odd uniformity in the CSP). Here our algorithm
succeeds at a significantly lower density than the spectral approaches,
surpassing a barrier based on the spectral norm of a random matrix.
Other significant features of the algorithm and analysis include (i) the
critical use of power iteration with subsampling, which might be of independent
interest; its analysis requires keeping track of multiple norms of an evolving
solution (ii) it can be implemented statistically, i.e., with very limited
access to the input distribution (iii) the algorithm is extremely simple to
implement and runs in linear time, and thus is practical even for very large
instances
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