89,679 research outputs found
Glassy Behavior and Jamming of a Random Walk Process for Sequentially Satisfying a Constraint Satisfaction Formula
Random -satisfiability (-SAT) is a model system for studying
typical-case complexity of combinatorial optimization. Recent theoretical and
simulation work revealed that the solution space of a random -SAT formula
has very rich structures, including the emergence of solution communities
within single solution clusters. In this paper we investigate the influence of
the solution space landscape to a simple stochastic local search process {\tt
SEQSAT}, which satisfies a -SAT formula in a sequential manner. Before
satisfying each newly added clause, {\tt SEQSAT} walk randomly by single-spin
flips in a solution cluster of the old subformula. This search process is
efficient when the constraint density of the satisfied subformula is
less than certain value ; however it slows down considerably as
and finally reaches a jammed state at . The glassy dynamical behavior of {\tt SEQSAT} for probably is due to the entropic trapping of various communities in
the solution cluster of the satisfied subformula. For random 3-SAT, the jamming
transition point is larger than the solution space clustering
transition point , and its value can be predicted by a long-range
frustration mean-field theory. For random -SAT with , however, our
simulation results indicate that . The relevance of this
work for understanding the dynamic properties of glassy systems is also
discussed.Comment: 10 pages, 6 figures, 1 table, a mistake of numerical simulation
corrected, and new results adde
Modeling scale-dependent bias on the baryonic acoustic scale with the statistics of peaks of Gaussian random fields
Models of galaxy and halo clustering commonly assume that the tracers can be
treated as a continuous field locally biased with respect to the underlying
mass distribution. In the peak model pioneered by BBKS, one considers instead
density maxima of the initial, Gaussian mass density field as an approximation
to the formation site of virialized objects. In this paper, the peak model is
extended in two ways to improve its predictive accuracy. Firstly, we derive the
two-point correlation function of initial density peaks up to second order and
demonstrate that a peak-background split approach can be applied to obtain the
k-independent and k-dependent peak bias factors at all orders. Secondly, we
explore the gravitational evolution of the peak correlation function within the
Zel'dovich approximation. We show that the local (Lagrangian) bias approach
emerges as a special case of the peak model, in which all bias parameters are
scale-independent and there is no statistical velocity bias. We apply our
formulae to study how the Lagrangian peak biasing, the diffusion due to large
scale flows and the mode-coupling due to nonlocal interactions affect the scale
dependence of bias from small separations up to the baryon acoustic oscillation
(BAO) scale. For 2-sigma density peaks collapsing at z=0.3, our model predicts
a ~ 5% residual scale-dependent bias around the acoustic scale that arises
mostly from first-order Lagrangian peak biasing (as opposed to second-order
gravity mode-coupling). We also search for a scale dependence of bias in the
large scale auto-correlation of massive halos extracted from a very large
N-body simulation provided by the MICE collaboration. For halos with mass
M>10^{14}Msun/h, our measurements demonstrate a scale-dependent bias across the
BAO feature which is very well reproduced by a prediction based on the peak
model.Comment: (v1): 23 pages text, 8 figures + appendix (v2): typos fixed,
references added, accepted for publication in PR
Solution space heterogeneity of the random K-satisfiability problem: Theory and simulations
The random K-satisfiability (K-SAT) problem is an important problem for
studying typical-case complexity of NP-complete combinatorial satisfaction; it
is also a representative model of finite-connectivity spin-glasses. In this
paper we review our recent efforts on the solution space fine structures of the
random K-SAT problem. A heterogeneity transition is predicted to occur in the
solution space as the constraint density alpha reaches a critical value
alpha_cm. This transition marks the emergency of exponentially many solution
communities in the solution space. After the heterogeneity transition the
solution space is still ergodic until alpha reaches a larger threshold value
alpha_d, at which the solution communities disconnect from each other to become
different solution clusters (ergodicity-breaking). The existence of solution
communities in the solution space is confirmed by numerical simulations of
solution space random walking, and the effect of solution space heterogeneity
on a stochastic local search algorithm SEQSAT, which performs a random walk of
single-spin flips, is investigated. The relevance of this work to glassy
dynamics studies is briefly mentioned.Comment: 11 pages, 4 figures. Final version as will appear in Journal of
Physics: Conference Series (Proceedings of the International Workshop on
Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japan
A directed isoperimetric inequality with application to Bregman near neighbor lower bounds
Bregman divergences are a class of divergences parametrized by a
convex function and include well known distance functions like
and the Kullback-Leibler divergence. There has been extensive
research on algorithms for problems like clustering and near neighbor search
with respect to Bregman divergences, in all cases, the algorithms depend not
just on the data size and dimensionality , but also on a structure
constant that depends solely on and can grow without bound
independently.
In this paper, we provide the first evidence that this dependence on
might be intrinsic. We focus on the problem of approximate near neighbor search
for Bregman divergences. We show that under the cell probe model, any
non-adaptive data structure (like locality-sensitive hashing) for
-approximate near-neighbor search that admits probes must use space
. In contrast, for LSH under the best
bound is .
Our new tool is a directed variant of the standard boolean noise operator. We
show that a generalization of the Bonami-Beckner hypercontractivity inequality
exists "in expectation" or upon restriction to certain subsets of the Hamming
cube, and that this is sufficient to prove the desired isoperimetric inequality
that we use in our data structure lower bound.
We also present a structural result reducing the Hamming cube to a Bregman
cube. This structure allows us to obtain lower bounds for problems under
Bregman divergences from their analog. In particular, we get a
(weaker) lower bound for approximate near neighbor search of the form
for an -query non-adaptive data structure,
and new cell probe lower bounds for a number of other near neighbor questions
in Bregman space.Comment: 27 page
On the use of biased-randomized algorithms for solving non-smooth optimization problems
Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines
Clustering Phase Transitions and Hysteresis: Pitfalls in Constructing Network Ensembles
Ensembles of networks are used as null models in many applications. However,
simple null models often show much less clustering than their real-world
counterparts. In this paper, we study a model where clustering is enhanced by
means of a fugacity term as in the Strauss (or "triangle") model, but where the
degree sequence is strictly preserved -- thus maintaining the quenched
heterogeneity of nodes found in the original degree sequence. Similar models
had been proposed previously in [R. Milo et al., Science 298, 824 (2002)]. We
find that our model exhibits phase transitions as the fugacity is changed. For
regular graphs (identical degrees for all nodes) with degree k > 2 we find a
single first order transition. For all non-regular networks that we studied
(including Erdos - Renyi and scale-free networks) we find multiple jumps
resembling first order transitions, together with strong hysteresis. The latter
transitions are driven by the sudden emergence of "cluster cores": groups of
highly interconnected nodes with higher than average degrees. To study these
cluster cores visually, we introduce q-clique adjacency plots. We find that
these cluster cores constitute distinct communities which emerge spontaneously
from the triangle generating process. Finally, we point out that cluster cores
produce pitfalls when using the present (and similar) models as null models for
strongly clustered networks, due to the very strong hysteresis which
effectively leads to broken ergodicity on realistic time scales.Comment: 13 pages, 11 figure
A Probabilistic Embedding Clustering Method for Urban Structure Detection
Urban structure detection is a basic task in urban geography. Clustering is a
core technology to detect the patterns of urban spatial structure, urban
functional region, and so on. In big data era, diverse urban sensing datasets
recording information like human behaviour and human social activity, suffer
from complexity in high dimension and high noise. And unfortunately, the
state-of-the-art clustering methods does not handle the problem with high
dimension and high noise issues concurrently. In this paper, a probabilistic
embedding clustering method is proposed. Firstly, we come up with a
Probabilistic Embedding Model (PEM) to find latent features from high
dimensional urban sensing data by learning via probabilistic model. By latent
features, we could catch essential features hidden in high dimensional data
known as patterns; with the probabilistic model, we can also reduce uncertainty
caused by high noise. Secondly, through tuning the parameters, our model could
discover two kinds of urban structure, the homophily and structural
equivalence, which means communities with intensive interaction or in the same
roles in urban structure. We evaluated the performance of our model by
conducting experiments on real-world data and experiments with real data in
Shanghai (China) proved that our method could discover two kinds of urban
structure, the homophily and structural equivalence, which means clustering
community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201
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