63,603 research outputs found
From one solution of a 3-satisfiability formula to a solution cluster: Frozen variables and entropy
A solution to a 3-satisfiability (3-SAT) formula can be expanded into a
cluster, all other solutions of which are reachable from this one through a
sequence of single-spin flips. Some variables in the solution cluster are
frozen to the same spin values by one of two different mechanisms: frozen-core
formation and long-range frustrations. While frozen cores are identified by a
local whitening algorithm, long-range frustrations are very difficult to trace,
and they make an entropic belief-propagation (BP) algorithm fail to converge.
For BP to reach a fixed point the spin values of a tiny fraction of variables
(chosen according to the whitening algorithm) are externally fixed during the
iteration. From the calculated entropy values, we infer that, for a large
random 3-SAT formula with constraint density close to the satisfiability
threshold, the solutions obtained by the survey-propagation or the walksat
algorithm belong neither to the most dominating clusters of the formula nor to
the most abundant clusters. This work indicates that a single solution cluster
of a random 3-SAT formula may have further community structures.Comment: 13 pages, 6 figures. Final version as published in PR
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
Integrating Conflict Driven Clause Learning to Local Search
This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions
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