2,212 research outputs found
Polylogarithmic Approximation for Generalized Minimum Manhattan Networks
Given a set of terminals, which are points in -dimensional Euclidean
space, the minimum Manhattan network problem (MMN) asks for a minimum-length
rectilinear network that connects each pair of terminals by a Manhattan path,
that is, a path consisting of axis-parallel segments whose total length equals
the pair's Manhattan distance. Even for , the problem is NP-hard, but
constant-factor approximations are known. For , the problem is
APX-hard; it is known to admit, for any \eps > 0, an
O(n^\eps)-approximation.
In the generalized minimum Manhattan network problem (GMMN), we are given a
set of terminal pairs, and the goal is to find a minimum-length
rectilinear network such that each pair in is connected by a Manhattan
path. GMMN is a generalization of both MMN and the well-known rectilinear
Steiner arborescence problem (RSA). So far, only special cases of GMMN have
been considered.
We present an -approximation algorithm for GMMN (and, hence,
MMN) in dimensions and an -approximation algorithm for 2D.
We show that an existing -approximation algorithm for RSA in 2D
generalizes easily to dimensions.Comment: 14 pages, 5 figures; added appendix and figure
Non-Local Product Rules for Percolation
Despite original claims of a first-order transition in the product rule model
proposed by Achlioptas et al. [Science 323, 1453 (2009)], recent studies
indicate that this percolation model, in fact, displays a continuous
transition. The distinctive scaling properties of the model at criticality,
however, strongly suggest that it should belong to a different universality
class than ordinary percolation. Here we introduce a generalization of the
product rule that reveals the effect of non-locality on the critical behavior
of the percolation process. Precisely, pairs of unoccupied bonds are chosen
according to a probability that decays as a power-law of their Manhattan
distance, and only that bond connecting clusters whose product of their sizes
is the smallest, becomes occupied. Interestingly, our results for
two-dimensional lattices at criticality shows that the power-law exponent of
the product rule has a significant influence on the finite-size scaling
exponents for the spanning cluster, the conducting backbone, and the cutting
bonds of the system. In all three cases, we observe a continuous variation from
ordinary to (non-local) explosive percolation exponents.Comment: 5 pages, 4 figure
When Hashes Met Wedges: A Distributed Algorithm for Finding High Similarity Vectors
Finding similar user pairs is a fundamental task in social networks, with
numerous applications in ranking and personalization tasks such as link
prediction and tie strength detection. A common manifestation of user
similarity is based upon network structure: each user is represented by a
vector that represents the user's network connections, where pairwise cosine
similarity among these vectors defines user similarity. The predominant task
for user similarity applications is to discover all similar pairs that have a
pairwise cosine similarity value larger than a given threshold . In
contrast to previous work where is assumed to be quite close to 1, we
focus on recommendation applications where is small, but still
meaningful. The all pairs cosine similarity problem is computationally
challenging on networks with billions of edges, and especially so for settings
with small . To the best of our knowledge, there is no practical solution
for computing all user pairs with, say on large social networks,
even using the power of distributed algorithms.
Our work directly addresses this challenge by introducing a new algorithm ---
WHIMP --- that solves this problem efficiently in the MapReduce model. The key
insight in WHIMP is to combine the "wedge-sampling" approach of Cohen-Lewis for
approximate matrix multiplication with the SimHash random projection techniques
of Charikar. We provide a theoretical analysis of WHIMP, proving that it has
near optimal communication costs while maintaining computation cost comparable
with the state of the art. We also empirically demonstrate WHIMP's scalability
by computing all highly similar pairs on four massive data sets, and show that
it accurately finds high similarity pairs. In particular, we note that WHIMP
successfully processes the entire Twitter network, which has tens of billions
of edges
Performance of hypercube routing schemes with or without buffering
Includes bibliographical references (p. 34-35).Supported by the NSF. NSF-DDM-8903385 Supported by the ARO. DAAL03-92-G-0115by Emmanouel A. Varvarigos and Dimitri P. Bertsekas
Degree Correlations in Random Geometric Graphs
Spatially embedded networks are important in several disciplines. The
prototypical spatial net- work we assume is the Random Geometric Graph of which
many properties are known. Here we present new results for the two-point degree
correlation function in terms of the clustering coefficient of the graphs for
two-dimensional space in particular, with extensions to arbitrary finite
dimension
Optimized auxiliary oscillators for the simulation of general open quantum systems
A method for the systematic construction of few-body damped harmonic
oscillator networks accurately reproducing the effect of general bosonic
environments in open quantum systems is presented. Under the sole assumptions
of a Gaussian environment and regardless of the system coupled to it, an
algorithm to determine the parameters of an equivalent set of interacting
damped oscillators obeying a Markovian quantum master equation is introduced.
By choosing a suitable coupling to the system and minimizing an appropriate
distance between the two-time correlation function of this effective bath and
that of the target environment, the error induced in the reduced dynamics of
the system is brought under rigorous control. The interactions among the
effective modes provide remarkable flexibility in replicating non-Markovian
effects on the system even with a small number of oscillators, and the
resulting Lindblad equation may therefore be integrated at a very reasonable
computational cost using standard methods for Markovian problems, even in
strongly non-perturbative coupling regimes and at arbitrary temperatures
including zero. We apply the method to an exactly solvable problem in order to
demonstrate its accuracy, and present a study based on current research in the
context of coherent transport in biological aggregates as a more realistic
example of its use; performance and versatility are highlighted, and
theoretical and numerical advantages over existing methods, as well as possible
future improvements, are discussed.Comment: 23 + 9 pages, 11 + 2 figures. No changes from previous version except
publication info and updated author affiliation
- …