2,511 research outputs found
Extremal Optimization at the Phase Transition of the 3-Coloring Problem
We investigate the phase transition of the 3-coloring problem on random
graphs, using the extremal optimization heuristic. 3-coloring is among the
hardest combinatorial optimization problems and is closely related to a 3-state
anti-ferromagnetic Potts model. Like many other such optimization problems, it
has been shown to exhibit a phase transition in its ground state behavior under
variation of a system parameter: the graph's mean vertex degree. This phase
transition is often associated with the instances of highest complexity. We use
extremal optimization to measure the ground state cost and the ``backbone'', an
order parameter related to ground state overlap, averaged over a large number
of instances near the transition for random graphs of size up to 512. For
graphs up to this size, benchmarks show that extremal optimization reaches
ground states and explores a sufficient number of them to give the correct
backbone value after about update steps. Finite size scaling gives
a critical mean degree value . Furthermore, the
exploration of the degenerate ground states indicates that the backbone order
parameter, measuring the constrainedness of the problem, exhibits a first-order
phase transition.Comment: RevTex4, 8 pages, 4 postscript figures, related information available
at http://www.physics.emory.edu/faculty/boettcher
Extremal Optimization for Graph Partitioning
Extremal optimization is a new general-purpose method for approximating
solutions to hard optimization problems. We study the method in detail by way
of the NP-hard graph partitioning problem. We discuss the scaling behavior of
extremal optimization, focusing on the convergence of the average run as a
function of runtime and system size. The method has a single free parameter,
which we determine numerically and justify using a simple argument. Our
numerical results demonstrate that on random graphs, extremal optimization
maintains consistent accuracy for increasing system sizes, with an
approximation error decreasing over runtime roughly as a power law t^(-0.4). On
geometrically structured graphs, the scaling of results from the average run
suggests that these are far from optimal, with large fluctuations between
individual trials. But when only the best runs are considered, results
consistent with theoretical arguments are recovered.Comment: 34 pages, RevTex4, 1 table and 20 ps-figures included, related papers
available at http://www.physics.emory.edu/faculty/boettcher
Continuous extremal optimization for Lennard-Jones Clusters
In this paper, we explore a general-purpose heuristic algorithm for finding
high-quality solutions to continuous optimization problems. The method, called
continuous extremal optimization(CEO), can be considered as an extension of
extremal optimization(EO) and is consisted of two components, one is with
responsibility for global searching and the other is with responsibility for
local searching. With only one adjustable parameter, the CEO's performance
proves competitive with more elaborate stochastic optimization procedures. We
demonstrate it on a well known continuous optimization problem: the
Lennerd-Jones clusters optimization problem.Comment: 5 pages and 3 figure
Aging in Dense Colloids as Diffusion in the Logarithm of Time
The far-from-equilibrium dynamics of glassy systems share important
phenomenological traits. A transition is generally observed from a
time-homogeneous dynamical regime to an aging regime where physical changes
occur intermittently and, on average, at a decreasing rate. It has been
suggested that a global change of the independent time variable to its
logarithm may render the aging dynamics homogeneous: for colloids, this entails
diffusion but on a logarithmic time scale. Our novel analysis of experimental
colloid data confirms that the mean square displacement grows linearly in time
at low densities and shows that it grows linearly in the logarithm of time at
high densities. Correspondingly, pairs of particles initially in close contact
survive as pairs with a probability which decays exponentially in either time
or its logarithm. The form of the Probability Density Function of the
displacements shows that long-ranged spatial correlations are very long-lived
in dense colloids. A phenomenological stochastic model is then introduced which
relies on the growth and collapse of strongly correlated clusters ("dynamic
heterogeneity"), and which reproduces the full spectrum of observed colloidal
behaviors depending on the form assumed for the probability that a cluster
collapses during a Monte Carlo update. In the limit where large clusters
dominate, the collapse rate is ~1/t, implying a homogeneous, log-Poissonian
process that qualitatively reproduces the experimental results for dense
colloids. Finally an analytical toy-model is discussed to elucidate the strong
dependence of the simulation results on the integrability (or lack thereof) of
the cluster collapse probability function.Comment: 6 pages, extensively revised, final version; for related work, see
http://www.physics.emory.edu/faculty/boettcher/ or
http://www.fysik.sdu.dk/staff/staff-vip/pas-personal.htm
Hysteretic Optimization For Spin Glasses
The recently proposed Hysteretic Optimization (HO) procedure is applied to
the 1D Ising spin chain with long range interactions. To study its
effectiveness, the quality of ground state energies found as a function of the
distance dependence exponent, , is assessed. It is found that the
transition from an infinite-range to a long-range interaction at
is accompanied by a sharp decrease in the performance . The transition is
signaled by a change in the scaling behavior of the average avalanche size
observed during the hysteresis process. This indicates that HO requires the
system to be infinite-range, with a high degree of interconnectivity between
variables leading to large avalanches, in order to function properly. An
analysis of the way auto-correlations evolve during the optimization procedure
confirm that the search of phase space is less efficient, with the system
becoming effectively stuck in suboptimal configurations much earlier. These
observations explain the poor performance that HO obtained for the
Edwards-Anderson spin glass on finite-dimensional lattices, and suggest that
its usefulness might be limited in many combinatorial optimization problems.Comment: 6 pages, 9 figures. To appear in JSTAT. Author website:
http://www.bgoncalves.co
Universal persistence exponents in an extremally driven system
The local persistence R(t), defined as the proportion of the system still in
its initial state at time t, is measured for the Bak--Sneppen model. For 1 and
2 dimensions, it is found that the decay of R(t) depends on one of two classes
of initial configuration. For a subcritical initial state, R(t)\sim
t^{-\theta}, where the persistence exponent \theta can be expressed in terms of
a known universal exponent. Hence \theta is universal. Conversely, starting
from a supercritical state, R(t) decays by the anomalous form 1-R(t)\sim
t^{\tau_{\rm ALL}} until a finite time t_{0}, where \tau_{\rm ALL} is also a
known exponent. Finally, for the high dimensional model R(t) decays
exponentially with a non--universal decay constant.Comment: 4 pages, 6 figures. To appear in Phys. Rev.
Extremal Optimization of Graph Partitioning at the Percolation Threshold
The benefits of a recently proposed method to approximate hard optimization
problems are demonstrated on the graph partitioning problem. The performance of
this new method, called Extremal Optimization, is compared to Simulated
Annealing in extensive numerical simulations. While generally a complex
(NP-hard) problem, the optimization of the graph partitions is particularly
difficult for sparse graphs with average connectivities near the percolation
threshold. At this threshold, the relative error of Simulated Annealing for
large graphs is found to diverge relative to Extremal Optimization at equalized
runtime. On the other hand, Extremal Optimization, based on the extremal
dynamics of self-organized critical systems, reproduces known results about
optimal partitions at this critical point quite well.Comment: 7 pages, RevTex, 9 ps-figures included, as to appear in Journal of
Physics
Entropic Sampling and Natural Selection in Biological Evolution
With a view to connecting random mutation on the molecular level to
punctuated equilibrium behavior on the phenotype level, we propose a new model
for biological evolution, which incorporates random mutation and natural
selection. In this scheme the system evolves continuously into new
configurations, yielding non-stationary behavior of the total fitness. Further,
both the waiting time distribution of species and the avalanche size
distribution display power-law behaviors with exponents close to two, which are
consistent with the fossil data. These features are rather robust, indicating
the key role of entropy
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