2,051 research outputs found
What makes a phase transition? Analysis of the random satisfiability problem
In the last 30 years it was found that many combinatorial systems undergo
phase transitions. One of the most important examples of these can be found
among the random k-satisfiability problems (often referred to as k-SAT), asking
whether there exists an assignment of Boolean values satisfying a Boolean
formula composed of clauses with k random variables each. The random 3-SAT
problem is reported to show various phase transitions at different critical
values of the ratio of the number of clauses to the number of variables. The
most famous of these occurs when the probability of finding a satisfiable
instance suddenly drops from 1 to 0. This transition is associated with a rise
in the hardness of the problem, but until now the correlation between any of
the proposed phase transitions and the hardness is not totally clear. In this
paper we will first show numerically that the number of solutions universally
follows a lognormal distribution, thereby explaining the puzzling question of
why the number of solutions is still exponential at the critical point.
Moreover we provide evidence that the hardness of the closely related problem
of counting the total number of solutions does not show any phase
transition-like behavior. This raises the question of whether the probability
of finding a satisfiable instance is really an order parameter of a phase
transition or whether it is more likely to just show a simple sharp threshold
phenomenon. More generally, this paper aims at starting a discussion where a
simple sharp threshold phenomenon turns into a genuine phase transition
The Phase Diagram of 1-in-3 Satisfiability Problem
We study the typical case properties of the 1-in-3 satisfiability problem,
the boolean satisfaction problem where a clause is satisfied by exactly one
literal, in an enlarged random ensemble parametrized by average connectivity
and probability of negation of a variable in a clause. Random 1-in-3
Satisfiability and Exact 3-Cover are special cases of this ensemble. We
interpolate between these cases from a region where satisfiability can be
typically decided for all connectivities in polynomial time to a region where
deciding satisfiability is hard, in some interval of connectivities. We derive
several rigorous results in the first region, and develop the
one-step--replica-symmetry-breaking cavity analysis in the second one. We
discuss the prediction for the transition between the almost surely satisfiable
and the almost surely unsatisfiable phase, and other structural properties of
the phase diagram, in light of cavity method results.Comment: 30 pages, 12 figure
Pairs of SAT Assignment in Random Boolean Formulae
We investigate geometrical properties of the random K-satisfiability problem
using the notion of x-satisfiability: a formula is x-satisfiable if there exist
two SAT assignments differing in Nx variables. We show the existence of a sharp
threshold for this property as a function of the clause density. For large
enough K, we prove that there exists a region of clause density, below the
satisfiability threshold, where the landscape of Hamming distances between SAT
assignments experiences a gap: pairs of SAT-assignments exist at small x, and
around x=1/2, but they donot exist at intermediate values of x. This result is
consistent with the clustering scenario which is at the heart of the recent
heuristic analysis of satisfiability using statistical physics analysis (the
cavity method), and its algorithmic counterpart (the survey propagation
algorithm). The method uses elementary probabilistic arguments (first and
second moment methods), and might be useful in other problems of computational
and physical interest where similar phenomena appear
The backtracking survey propagation algorithm for solving random K-SAT problems
Discrete combinatorial optimization has a central role in many scientific
disciplines, however, for hard problems we lack linear time algorithms that
would allow us to solve very large instances. Moreover, it is still unclear
what are the key features that make a discrete combinatorial optimization
problem hard to solve. Here we study random K-satisfiability problems with
, which are known to be very hard close to the SAT-UNSAT threshold,
where problems stop having solutions. We show that the backtracking survey
propagation algorithm, in a time practically linear in the problem size, is
able to find solutions very close to the threshold, in a region unreachable by
any other algorithm. All solutions found have no frozen variables, thus
supporting the conjecture that only unfrozen solutions can be found in linear
time, and that a problem becomes impossible to solve in linear time when all
solutions contain frozen variables.Comment: 11 pages, 10 figures. v2: data largely improved and manuscript
rewritte
Reweighted belief propagation and quiet planting for random K-SAT
We study the random K-satisfiability problem using a partition function where
each solution is reweighted according to the number of variables that satisfy
every clause. We apply belief propagation and the related cavity method to the
reweighted partition function. This allows us to obtain several new results on
the properties of random K-satisfiability problem. In particular the
reweighting allows to introduce a planted ensemble that generates instances
that are, in some region of parameters, equivalent to random instances. We are
hence able to generate at the same time a typical random SAT instance and one
of its solutions. We study the relation between clustering and belief
propagation fixed points and we give a direct evidence for the existence of
purely entropic (rather than energetic) barriers between clusters in some
region of parameters in the random K-satisfiability problem. We exhibit, in
some large planted instances, solutions with a non-trivial whitening core; such
solutions were known to exist but were so far never found on very large
instances. Finally, we discuss algorithmic hardness of such planted instances
and we determine a region of parameters in which planting leads to satisfiable
benchmarks that, up to our knowledge, are the hardest known.Comment: 23 pages, 4 figures, revised for readability, stability expression
correcte
Solution space structure of random constraint satisfaction problems with growing domains
In this paper we study the solution space structure of model RB, a standard
prototype of Constraint Satisfaction Problem (CSPs) with growing domains. Using
rigorous the first and the second moment method, we show that in the solvable
phase close to the satisfiability transition, solutions are clustered into
exponential number of well-separated clusters, with each cluster contains
sub-exponential number of solutions. As a consequence, the system has a
clustering (dynamical) transition but no condensation transition. This picture
of phase diagram is different from other classic random CSPs with fixed domain
size, such as random K-Satisfiability (K-SAT) and graph coloring problems,
where condensation transition exists and is distinct from satisfiability
transition. Our result verifies the non-rigorous results obtained using cavity
method from spin glass theory, and sheds light on the structures of solution
spaces of problems with a large number of states.Comment: 8 pages, 1 figure
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
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