279,589 research outputs found
Local algorithms for independent sets are half-optimal
We show that the largest density of factor of i.i.d. independent sets in the d -regular tree is asymptotically at most ( log d ) / d as d → ∞ . This matches the lower bound given by previous constructions. It follows that the largest independent sets given by local algorithms on random d -regular graphs have the same asymptotic density. In contrast, the density of the largest independent sets in these graphs is asymptotically 2 ( log d ) / d . We prove analogous results for Poisson–Galton–Watson trees, which yield bounds for local algorithms on sparse Erdős–Rényi graphs
Reconstruction Threshold for the Hardcore Model
In this paper we consider the reconstruction problem on the tree for the
hardcore model. We determine new bounds for the non-reconstruction regime on
the k-regular tree showing non-reconstruction when lambda < (ln
2-o(1))ln^2(k)/(2 lnln(k)) improving the previous best bound of lambda < e-1.
This is almost tight as reconstruction is known to hold when lambda>
(e+o(1))ln^2(k). We discuss the relationship for finding large independent sets
in sparse random graphs and to the mixing time of Markov chains for sampling
independent sets on trees.Comment: 14 pages, 2 figure
Minimizing the number of independent sets in triangle-free regular graphs
Recently, Davies, Jenssen, Perkins, and Roberts gave a very nice proof of the
result (due, in various parts, to Kahn, Galvin-Tetali, and Zhao) that the
independence polynomial of a -regular graph is maximized by disjoint copies
of . Their proof uses linear programming bounds on the distribution of
a cleverly chosen random variable. In this paper, we use this method to give
lower bounds on the independence polynomial of regular graphs. We also give new
bounds on the number of independent sets in triangle-free regular graphs
Statistical Mechanics of maximal independent sets
The graph theoretic concept of maximal independent set arises in several
practical problems in computer science as well as in game theory. A maximal
independent set is defined by the set of occupied nodes that satisfy some
packing and covering constraints. It is known that finding minimum and
maximum-density maximal independent sets are hard optimization problems. In
this paper, we use cavity method of statistical physics and Monte Carlo
simulations to study the corresponding constraint satisfaction problem on
random graphs. We obtain the entropy of maximal independent sets within the
replica symmetric and one-step replica symmetry breaking frameworks, shedding
light on the metric structure of the landscape of solutions and suggesting a
class of possible algorithms. This is of particular relevance for the
application to the study of strategic interactions in social and economic
networks, where maximal independent sets correspond to pure Nash equilibria of
a graphical game of public goods allocation
Ferromagnetic Potts Model: Refined #BIS-hardness and Related Results
Recent results establish for 2-spin antiferromagnetic systems that the
computational complexity of approximating the partition function on graphs of
maximum degree D undergoes a phase transition that coincides with the
uniqueness phase transition on the infinite D-regular tree. For the
ferromagnetic Potts model we investigate whether analogous hardness results
hold. Goldberg and Jerrum showed that approximating the partition function of
the ferromagnetic Potts model is at least as hard as approximating the number
of independent sets in bipartite graphs (#BIS-hardness). We improve this
hardness result by establishing it for bipartite graphs of maximum degree D. We
first present a detailed picture for the phase diagram for the infinite
D-regular tree, giving a refined picture of its first-order phase transition
and establishing the critical temperature for the coexistence of the disordered
and ordered phases. We then prove for all temperatures below this critical
temperature that it is #BIS-hard to approximate the partition function on
bipartite graphs of maximum degree D. As a corollary, it is #BIS-hard to
approximate the number of k-colorings on bipartite graphs of maximum degree D
when k <= D/(2 ln D).
The #BIS-hardness result for the ferromagnetic Potts model uses random
bipartite regular graphs as a gadget in the reduction. The analysis of these
random graphs relies on recent connections between the maxima of the
expectation of their partition function, attractive fixpoints of the associated
tree recursions, and induced matrix norms. We extend these connections to
random regular graphs for all ferromagnetic models and establish the Bethe
prediction for every ferromagnetic spin system on random regular graphs. We
also prove for the ferromagnetic Potts model that the Swendsen-Wang algorithm
is torpidly mixing on random D-regular graphs at the critical temperature for
large q.Comment: To appear in SIAM J. Computin
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