445 research outputs found
Reply to Comment on ``Thermal Model for Adaptive Competition in a Market''
We reply to the Comment of Challet et al. [cond-mat/0004308] on our paper
[Phys. Rev. Lett. 83, 4429 (1999)]. We show that the claim of the Comment that
the effects of the temperature in the Thermal Minority Game ``can be eliminated
by time rescaling'' and consequently the behaviour is ``independent of T'' has
no general validity.Comment: 1 page, 1 figur
Survey Propagation as local equilibrium equations
It has been shown experimentally that a decimation algorithm based on Survey
Propagation (SP) equations allows to solve efficiently some combinatorial
problems over random graphs. We show that these equations can be derived as
sum-product equations for the computation of marginals in an extended space
where the variables are allowed to take an additional value -- -- when they
are not forced by the combinatorial constraints. An appropriate ``local
equilibrium condition'' cost/energy function is introduced and its entropy is
shown to coincide with the expected logarithm of the number of clusters of
solutions as computed by SP. These results may help to clarify the geometrical
notion of clusters assumed by SP for the random K-SAT or random graph coloring
(where it is conjectured to be exact) and helps to explain which kind of
clustering operation or approximation is enforced in general/small sized models
in which it is known to be inexact.Comment: 13 pages, 3 figure
Clustering of solutions in the random satisfiability problem
Using elementary rigorous methods we prove the existence of a clustered phase
in the random -SAT problem, for . In this phase the solutions are
grouped into clusters which are far away from each other. The results are in
agreement with previous predictions of the cavity method and give a rigorous
confirmation to one of its main building blocks. It can be generalized to other
systems of both physical and computational interest.Comment: 4 pages, 1 figur
The stochastic matching problem
The matching problem plays a basic role in combinatorial optimization and in
statistical mechanics. In its stochastic variants, optimization decisions have
to be taken given only some probabilistic information about the instance. While
the deterministic case can be solved in polynomial time, stochastic variants
are worst-case intractable. We propose an efficient method to solve stochastic
matching problems which combines some features of the survey propagation
equations and of the cavity method. We test it on random bipartite graphs, for
which we analyze the phase diagram and compare the results with exact bounds.
Our approach is shown numerically to be effective on the full range of
parameters, and to outperform state-of-the-art methods. Finally we discuss how
the method can be generalized to other problems of optimization under
uncertainty.Comment: Published version has very minor change
Bicoloring Random Hypergraphs
We study the problem of bicoloring random hypergraphs, both numerically and
analytically. We apply the zero-temperature cavity method to find analytical
results for the phase transitions (dynamic and static) in the 1RSB
approximation. These points appear to be in agreement with the results of the
numerical algorithm. In the second part, we implement and test the Survey
Propagation algorithm for specific bicoloring instances in the so called
HARD-SAT phase.Comment: 14 pages, 10 figure
Learning by message-passing in networks of discrete synapses
We show that a message-passing process allows to store in binary "material"
synapses a number of random patterns which almost saturates the information
theoretic bounds. We apply the learning algorithm to networks characterized by
a wide range of different connection topologies and of size comparable with
that of biological systems (e.g. ). The algorithm can be
turned into an on-line --fault tolerant-- learning protocol of potential
interest in modeling aspects of synaptic plasticity and in building
neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections;
accepted in PR
Statistical Mechanics of Steiner trees
The Minimum Weight Steiner Tree (MST) is an important combinatorial
optimization problem over networks that has applications in a wide range of
fields. Here we discuss a general technique to translate the imposed global
connectivity constrain into many local ones that can be analyzed with cavity
equation techniques. This approach leads to a new optimization algorithm for
MST and allows to analyze the statistical mechanics properties of MST on random
graphs of various types
Tricritical Points in Random Combinatorics: the (2+p)-SAT case
The (2+p)-Satisfiability (SAT) problem interpolates between different classes
of complexity theory and is believed to be of basic interest in understanding
the onset of typical case complexity in random combinatorics. In this paper, a
tricritical point in the phase diagram of the random -SAT problem is
analytically computed using the replica approach and found to lie in the range
. These bounds on are in agreement with previous
numerical simulations and rigorous results.Comment: 7 pages, 1 figure, RevTeX, to appear in J.Phys.
Lossy data compression with random gates
We introduce a new protocol for a lossy data compression algorithm which is
based on constraint satisfaction gates. We show that the theoretical capacity
of algorithms built from standard parity-check gates converges exponentially
fast to the Shannon's bound when the number of variables seen by each gate
increases. We then generalize this approach by introducing random gates. They
have theoretical performances nearly as good as parity checks, but they offer
the great advantage that the encoding can be done in linear time using the
Survey Inspired Decimation algorithm, a powerful algorithm for constraint
satisfaction problems derived from statistical physics
- …