3,108 research outputs found
Exact Computation of a Manifold Metric, via Lipschitz Embeddings and Shortest Paths on a Graph
Data-sensitive metrics adapt distances locally based the density of data
points with the goal of aligning distances and some notion of similarity. In
this paper, we give the first exact algorithm for computing a data-sensitive
metric called the nearest neighbor metric. In fact, we prove the surprising
result that a previously published -approximation is an exact algorithm.
The nearest neighbor metric can be viewed as a special case of a
density-based distance used in machine learning, or it can be seen as an
example of a manifold metric. Previous computational research on such metrics
despaired of computing exact distances on account of the apparent difficulty of
minimizing over all continuous paths between a pair of points. We leverage the
exact computation of the nearest neighbor metric to compute sparse spanners and
persistent homology. We also explore the behavior of the metric built from
point sets drawn from an underlying distribution and consider the more general
case of inputs that are finite collections of path-connected compact sets.
The main results connect several classical theories such as the conformal
change of Riemannian metrics, the theory of positive definite functions of
Schoenberg, and screw function theory of Schoenberg and Von Neumann. We develop
novel proof techniques based on the combination of screw functions and
Lipschitz extensions that may be of independent interest.Comment: 15 page
Estimating the weight of metric minimum spanning trees in sublinear time
In this paper we present a sublinear-time -approximation randomized algorithm to estimate the weight of the minimum spanning tree of an -point metric space. The running time of the algorithm is . Since the full description of an -point metric space is of size , the complexity of our algorithm is sublinear with respect to the input size. Our algorithm is almost optimal as it is not possible to approximate in time the weight of the minimum spanning tree to within any factor. We also show that no deterministic algorithm can achieve a -approximation in time. Furthermore, it has been previously shown that no algorithm exists that returns a spanning tree whose weight is within a constant times the optimum
Computational Geometry Column 42
A compendium of thirty previously published open problems in computational
geometry is presented.Comment: 7 pages; 72 reference
Parallel Algorithms for Geometric Graph Problems
We give algorithms for geometric graph problems in the modern parallel models
inspired by MapReduce. For example, for the Minimum Spanning Tree (MST) problem
over a set of points in the two-dimensional space, our algorithm computes a
-approximate MST. Our algorithms work in a constant number of
rounds of communication, while using total space and communication proportional
to the size of the data (linear space and near linear time algorithms). In
contrast, for general graphs, achieving the same result for MST (or even
connectivity) remains a challenging open problem, despite drawing significant
attention in recent years.
We develop a general algorithmic framework that, besides MST, also applies to
Earth-Mover Distance (EMD) and the transportation cost problem. Our algorithmic
framework has implications beyond the MapReduce model. For example it yields a
new algorithm for computing EMD cost in the plane in near-linear time,
. We note that while recently Sharathkumar and Agarwal
developed a near-linear time algorithm for -approximating EMD,
our algorithm is fundamentally different, and, for example, also solves the
transportation (cost) problem, raised as an open question in their work.
Furthermore, our algorithm immediately gives a -approximation
algorithm with space in the streaming-with-sorting model with
passes. As such, it is tempting to conjecture that the
parallel models may also constitute a concrete playground in the quest for
efficient algorithms for EMD (and other similar problems) in the vanilla
streaming model, a well-known open problem
Net and Prune: A Linear Time Algorithm for Euclidean Distance Problems
We provide a general framework for getting expected linear time constant
factor approximations (and in many cases FPTAS's) to several well known
problems in Computational Geometry, such as -center clustering and farthest
nearest neighbor. The new approach is robust to variations in the input
problem, and yet it is simple, elegant and practical. In particular, many of
these well studied problems which fit easily into our framework, either
previously had no linear time approximation algorithm, or required rather
involved algorithms and analysis. A short list of the problems we consider
include farthest nearest neighbor, -center clustering, smallest disk
enclosing points, th largest distance, th smallest -nearest
neighbor distance, th heaviest edge in the MST and other spanning forest
type problems, problems involving upward closed set systems, and more. Finally,
we show how to extend our framework such that the linear running time bound
holds with high probability
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have
substantially advanced the state-of-the-art in computer vision tasks. However,
deep networks are typically resource-intensive and thus difficult to be
deployed on mobile devices. Recently, CNNs with binary weights have shown
compelling efficiency to the community, whereas the accuracy of such models is
usually unsatisfactory in practice. In this paper, we introduce network
sketching as a novel technique of pursuing binary-weight CNNs, targeting at
more faithful inference and better trade-off for practical applications. Our
basic idea is to exploit binary structure directly in pre-trained filter banks
and produce binary-weight models via tensor expansion. The whole process can be
treated as a coarse-to-fine model approximation, akin to the pencil drawing
steps of outlining and shading. To further speedup the generated models, namely
the sketches, we also propose an associative implementation of binary tensor
convolutions. Experimental results demonstrate that a proper sketch of AlexNet
(or ResNet) outperforms the existing binary-weight models by large margins on
the ImageNet large scale classification task, while the committed memory for
network parameters only exceeds a little.Comment: To appear in CVPR201
Fast spectral source integration in black hole perturbation calculations
This paper presents a new technique for achieving spectral accuracy and fast
computational performance in a class of black hole perturbation and
gravitational self-force calculations involving extreme mass ratios and generic
orbits. Called \emph{spectral source integration} (SSI), this method should see
widespread future use in problems that entail (i) point-particle description of
the small compact object, (ii) frequency domain decomposition, and (iii) use of
the background eccentric geodesic motion. Frequency domain approaches are
widely used in both perturbation theory flux-balance calculations and in local
gravitational self-force calculations. Recent self-force calculations in Lorenz
gauge, using the frequency domain and method of extended homogeneous solutions,
have been able to accurately reach eccentricities as high as . We
show here SSI successfully applied to Lorenz gauge. In a double precision
Lorenz gauge code, SSI enhances the accuracy of results and makes a factor of
three improvement in the overall speed. The primary initial application of
SSI--for us its \emph{raison d'\^{e}tre}--is in an arbitrary precision
\emph{Mathematica} code that computes perturbations of eccentric orbits in the
Regge-Wheeler gauge to extraordinarily high accuracy (e.g., 200 decimal
places). These high accuracy eccentric orbit calculations would not be possible
without the exponential convergence of SSI. We believe the method will extend
to work for inspirals on Kerr, and will be the subject of a later publication.
SSI borrows concepts from discrete-time signal processing and is used to
calculate the mode normalization coefficients in perturbation theory via sums
over modest numbers of points around an orbit. A variant of the idea is used to
obtain spectral accuracy in solution of the geodesic orbital motion.Comment: 15 pages, 7 figure
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