3,350 research outputs found
Solving satisfiability problems by fluctuations: The dynamics of stochastic local search algorithms
Stochastic local search algorithms are frequently used to numerically solve
hard combinatorial optimization or decision problems. We give numerical and
approximate analytical descriptions of the dynamics of such algorithms applied
to random satisfiability problems. We find two different dynamical regimes,
depending on the number of constraints per variable: For low constraintness,
the problems are solved efficiently, i.e. in linear time. For higher
constraintness, the solution times become exponential. We observe that the
dynamical behavior is characterized by a fast equilibration and fluctuations
around this equilibrium. If the algorithm runs long enough, an exponentially
rare fluctuation towards a solution appears.Comment: 21 pages, 18 figures, revised version, to app. in PRE (2003
Bit-Vector Model Counting using Statistical Estimation
Approximate model counting for bit-vector SMT formulas (generalizing \#SAT)
has many applications such as probabilistic inference and quantitative
information-flow security, but it is computationally difficult. Adding random
parity constraints (XOR streamlining) and then checking satisfiability is an
effective approximation technique, but it requires a prior hypothesis about the
model count to produce useful results. We propose an approach inspired by
statistical estimation to continually refine a probabilistic estimate of the
model count for a formula, so that each XOR-streamlined query yields as much
information as possible. We implement this approach, with an approximate
probability model, as a wrapper around an off-the-shelf SMT solver or SAT
solver. Experimental results show that the implementation is faster than the
most similar previous approaches which used simpler refinement strategies. The
technique also lets us model count formulas over floating-point constraints,
which we demonstrate with an application to a vulnerability in differential
privacy mechanisms
Hamiltonian Oracles
Hamiltonian oracles are the continuum limit of the standard unitary quantum
oracles. In this limit, the problem of finding the optimal query algorithm can
be mapped into the problem of finding shortest paths on a manifold. The study
of these shortest paths leads to lower bounds of the original unitary oracle
problem. A number of example Hamiltonian oracles are studied in this paper,
including oracle interrogation and the problem of computing the XOR of the
hidden bits. Both of these problems are related to the study of geodesics on
spheres with non-round metrics. For the case of two hidden bits a complete
description of the geodesics is given. For n hidden bits a simple lower bound
is proven that shows the problems require a query time proportional to n, even
in the continuum limit. Finally, the problem of continuous Grover search is
reexamined leading to a modest improvement to the protocol of Farhi and
Gutmann.Comment: 16 pages, REVTeX 4 (minor corrections in v2
A Meaningful MD5 Hash Collision Attack
It is now proved by Wang et al., that MD5 hash is no more secure, after they proposed an attack that would generate two different messages that gives the same MD5 sum. Many conditions need to be satisfied to attain this collision. Vlastimil Klima then proposed a more efficient and faster technique to implement this attack. We use these techniques to first create a collision attack and then use these collisions to implement meaningful collisions by creating two different packages that give identical MD5 hash, but when extracted, each gives out different files with contents specified by the atacker
On the cavity method for decimated random constraint satisfaction problems and the analysis of belief propagation guided decimation algorithms
We introduce a version of the cavity method for diluted mean-field spin
models that allows the computation of thermodynamic quantities similar to the
Franz-Parisi quenched potential in sparse random graph models. This method is
developed in the particular case of partially decimated random constraint
satisfaction problems. This allows to develop a theoretical understanding of a
class of algorithms for solving constraint satisfaction problems, in which
elementary degrees of freedom are sequentially assigned according to the
results of a message passing procedure (belief-propagation). We confront this
theoretical analysis to the results of extensive numerical simulations.Comment: 32 pages, 24 figure
Computational core and fixed-point organisation in Boolean networks
In this paper, we analyse large random Boolean networks in terms of a
constraint satisfaction problem. We first develop an algorithmic scheme which
allows to prune simple logical cascades and under-determined variables,
returning thereby the computational core of the network. Second we apply the
cavity method to analyse number and organisation of fixed points. We find in
particular a phase transition between an easy and a complex regulatory phase,
the latter one being characterised by the existence of an exponential number of
macroscopically separated fixed-point clusters. The different techniques
developed are reinterpreted as algorithms for the analysis of single Boolean
networks, and they are applied to analysis and in silico experiments on the
gene-regulatory networks of baker's yeast (saccaromices cerevisiae) and the
segment-polarity genes of the fruit-fly drosophila melanogaster.Comment: 29 pages, 18 figures, version accepted for publication in JSTA
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