86,871 research outputs found
Dominance Product and High-Dimensional Closest Pair under
Given a set of points in , the Closest Pair problem is
to find a pair of distinct points in at minimum distance. When is
constant, there are efficient algorithms that solve this problem, and fast
approximate solutions for general . However, obtaining an exact solution in
very high dimensions seems to be much less understood. We consider the
high-dimensional Closest Pair problem, where for some , and the underlying metric is .
We improve and simplify previous results for Closest Pair, showing
that it can be solved by a deterministic strongly-polynomial algorithm that
runs in time, and by a randomized algorithm that runs in
expected time, where is the time bound for computing the
{\em dominance product} for points in . That is a matrix ,
such that ; this is the
number of coordinates at which dominates . For integer coordinates
from some interval , we obtain an algorithm that runs in
time, where
is the exponent of multiplying an matrix by an
matrix.
We also give slightly better bounds for , by using more recent
rectangular matrix multiplication bounds. Computing the dominance product
itself is an important task, since it is applied in many algorithms as a major
black-box ingredient, such as algorithms for APBP (all pairs bottleneck paths),
and variants of APSP (all pairs shortest paths)
A well-separated pairs decomposition algorithm for k-d trees implemented on multi-core architectures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.Variations of k-d trees represent a fundamental data structure used in Computational Geometry with numerous applications in science. For example particle track tting in the software of the LHC experiments, and in simulations of N-body systems in the study of dynamics of interacting galaxies, particle beam physics, and molecular dynamics in biochemistry. The many-body tree methods devised by Barnes and Hutt in the 1980s and the Fast Multipole Method introduced in 1987 by Greengard and Rokhlin use variants of k-d trees to reduce the computation time upper bounds to O(n log n) and even O(n) from O(n2). We present an algorithm that uses the principle of well-separated pairs decomposition to always produce compressed trees in O(n log n) work. We present and evaluate parallel implementations for the algorithm that can take advantage of multi-core architectures.The Science and Technology Facilities Council, UK
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Fully dynamic maintenance of euclidean minimum spanning trees
We maintain the minimum spanning tree of a point set in the plane, subject to point insertions and deletions, in time O(n^5/6 log1^2/2 n) per update operation. No nontrivial dynamic geometric minimum spanning tree algorithm was previously known. We reduce the problem to maintaining bichromatic closest pairs, which we also solve in the same time bounds. Our algorithm uses a novel construction, the ordered nearest neighbors of a sequence of points. Any point set or bichromatic point set can be ordered so that this graph is a simple path
Algorithms for Stable Matching and Clustering in a Grid
We study a discrete version of a geometric stable marriage problem originally
proposed in a continuous setting by Hoffman, Holroyd, and Peres, in which
points in the plane are stably matched to cluster centers, as prioritized by
their distances, so that each cluster center is apportioned a set of points of
equal area. We show that, for a discretization of the problem to an
grid of pixels with centers, the problem can be solved in time , and we experiment with two slower but more practical algorithms and
a hybrid method that switches from one of these algorithms to the other to gain
greater efficiency than either algorithm alone. We also show how to combine
geometric stable matchings with a -means clustering algorithm, so as to
provide a geometric political-districting algorithm that views distance in
economic terms, and we experiment with weighted versions of stable -means in
order to improve the connectivity of the resulting clusters.Comment: 23 pages, 12 figures. To appear (without the appendices) at the 18th
International Workshop on Combinatorial Image Analysis, June 19-21, 2017,
Plovdiv, Bulgari
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Fully dynamic maintenance of Euclidean minimum spanning trees and maxima of decomposable functions
We maintain the minimum spanning tree of a point set in the plane, subject to point insertions and deletions, in time O(n^1/2 log^2 n) per update operation. We reduce the problem to maintaining bichromatic closest pairs, which we solve in time O(n^E) per update. Our algorithm uses a novel construction, the ordered nearest neighbors of a sequence of points. Any point set or bichromatic point set can be ordered so that this graph is a simple path. Our results generalize to higher dimensions, and to fully dynamic algorithms for maintaining maxima of decomposable functions, including the diameter of a point set and the bichromatic farthest pair
Efficient Algorithms for the Closest Pair Problem and Applications
The closest pair problem (CPP) is one of the well studied and fundamental
problems in computing. Given a set of points in a metric space, the problem is
to identify the pair of closest points. Another closely related problem is the
fixed radius nearest neighbors problem (FRNNP). Given a set of points and a
radius , the problem is, for every input point , to identify all the
other input points that are within a distance of from . A naive
deterministic algorithm can solve these problems in quadratic time. CPP as well
as FRNNP play a vital role in computational biology, computational finance,
share market analysis, weather prediction, entomology, electro cardiograph,
N-body simulations, molecular simulations, etc. As a result, any improvements
made in solving CPP and FRNNP will have immediate implications for the solution
of numerous problems in these domains. We live in an era of big data and
processing these data take large amounts of time. Speeding up data processing
algorithms is thus much more essential now than ever before. In this paper we
present algorithms for CPP and FRNNP that improve (in theory and/or practice)
the best-known algorithms reported in the literature for CPP and FRNNP. These
algorithms also improve the best-known algorithms for related applications
including time series motif mining and the two locus problem in Genome Wide
Association Studies (GWAS)
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