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An O(log 2 k)approximation algorithm for the kvertex connected subgraph problem
 In Proceedings of ACM Symposium on Theory of Computing (STOC
, 2008
"... We present an O(log n·log k)approximation algorithm for the problem of finding kvertex connected spanning subgraph of minimum cost, where n is the number of vertices in the input graph, and k is the connectivity requirement. Our algorithm works for both directed and undirected graphs. The best k ..."
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Cited by 14 (0 self)
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We present an O(log n·log k)approximation algorithm for the problem of finding kvertex connected spanning subgraph of minimum cost, where n is the number of vertices in the input graph, and k is the connectivity requirement. Our algorithm works for both directed and undirected graphs. The best
Two O(log k) approximation algorithms for the asymmetric kcenter problem
 Proceedings of the 8th Conference on Integer Programming and Combinatorial Optimization
, 2001
"... Given a set V of n points and the distances between each pair, the kcenter problem asks us to choose a subset C \subset V of size k that minimizes the maximum over all points of the distance from C to the point. This problem is NP hard even when the distances are symmetric and satisfy the triangle ..."
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Given a set V of n points and the distances between each pair, the kcenter problem asks us to choose a subset C \subset V of size k that minimizes the maximum over all points of the distance from C to the point. This problem is NP hard even when the distances are symmetric and satisfy the triangle
An O(log k)approximation algorithm for the k minimum spanning tree problem in the plane
 In Proceedings of the 26th Annual ACM Symposium on Theory of Computing
, 1994
"... Given n points in the Euclidean plane, we consider the problem of finding the minimum tree spanning any k points. The problem is NPhard and we give an O(logk)approximation algorithm. 1 Introduction A cable company is permitted to service k cities in a state with n cities. To minimise the cost of ..."
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Cited by 1 (0 self)
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Given n points in the Euclidean plane, we consider the problem of finding the minimum tree spanning any k points. The problem is NPhard and we give an O(logk)approximation algorithm. 1 Introduction A cable company is permitted to service k cities in a state with n cities. To minimise the cost
An almost O(log k)approximation for kconnected subgraphs
, 2009
"... We consider two cases of the Survivable Network Design (SND) problem: given a complete graph Gn = (V, En) with costs on the edges and connectivity requirements {r(u, v) : u, v ∈ V}, find a minimum cost subgraph G of Gn that contains r(u, v) internally disjoint uvpaths for all u, v ∈ V. Our main res ..."
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Cited by 22 (11 self)
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at minimum cost the connectivity of a given graph J from k − 1 to k; a ρapproximation for it is used to derive an O(ρ · log k)approximation for kConnected Subgraph. Fakcharoenphol and Laekhanukit showed that kConnectivity Augmentation admits an O(log ν)approximation algorithm, where ν is the number
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
The Viterbi algorithm
 Proceedings of the IEEE
, 1973
"... vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A ..."
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Cited by 985 (3 self)
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vol. 6, no. 8, pp. 211220, 1951. [7] J. L. Anderson and J. W..Ryon, “Electromagnetic radiation in accelerated systems, ” Phys. Rev., vol. 181, pp. 17651775, 1969. [8] C. V. Heer, “Resonant frequencies of an electromagnetic cavity in an accelerated system of reference, ” Phys. Reu., vol. 134, pp. A799A804, 1964. [9] T. C. Mo, “Theory of electrodynamics in media in noninertial frames and applications, ” J. Math. Phys., vol. 11, pp. 25892610, 1970.
Optimal Aggregation Algorithms for Middleware
 IN PODS
, 2001
"... Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under ..."
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Cited by 701 (4 self)
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under that attribute, sorted by grade (highest grade first). There is some monotone aggregation function, or combining rule, such as min or average, that combines the individual grades to obtain an overall grade. To determine the top k objects (that have the best overall grades), the naive algorithm
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 539 (20 self)
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This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task
On Spectral Clustering: Analysis and an algorithm
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
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Cited by 1697 (13 self)
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Despite many empirical successes of spectral clustering methods  algorithms that cluster points using eigenvectors of matrices derived from the distances between the points  there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
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