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Clustering for Edge-Cost Minimization
"... Leonard J. Schulman College of Computing Georgia Institute of Technology Atlanta GA 30332-0280 ABSTRACT We address the problem of partitioning a set of n points into clusters, so as to minimize the sum, over all intracluster pairs of points, of the cost associated with each pair. We obtain a ra ..."
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Cited by 34 (5 self)
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Leonard J. Schulman College of Computing Georgia Institute of Technology Atlanta GA 30332-0280 ABSTRACT We address the problem of partitioning a set of n points into clusters, so as to minimize the sum, over all intracluster pairs of points, of the cost associated with each pair. We obtain a
Optimal contracts and competitive markets with costly state verification
- Journal of Economic Theory
, 1979
"... The insight of Arrow [4] and Debreu [7] that uncertainty is easily incor-porated into general equilibrium models is double-edged. It is true that one need only index commodities by the state of nature, and classical results on the existence and optimality of competitive equilibria can be made to ..."
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Cited by 879 (8 self)
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The insight of Arrow [4] and Debreu [7] that uncertainty is easily incor-porated into general equilibrium models is double-edged. It is true that one need only index commodities by the state of nature, and classical results on the existence and optimality of competitive equilibria can be made to
Finding Low-Diameter, Low Edge-Cost, Networks
, 1997
"... This paper describes a simulated annealing algorithm to compute k-connected graphs that minimize a linear combination of graph edge-cost and diameter. Replicas of Internet information services can use graphs with these properties to propagate updates among themselves. We report the algorithm's ..."
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Cited by 5 (0 self)
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This paper describes a simulated annealing algorithm to compute k-connected graphs that minimize a linear combination of graph edge-cost and diameter. Replicas of Internet information services can use graphs with these properties to propagate updates among themselves. We report the algorithm
Shortest path algorithms with integer edge costs
, 2002
"... Single source shortest path algorithms are concerned with finding the shortest distances to all vertices in a graph from a single source vertex. Dijkstra (1959) first came up with an O(n²) algorithm to solve such a problem, where n is the number of vertices in the graph. If the given graph has a spe ..."
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special property that all edge costs within the graph are integers and these edge costs are bounded by some constant c, it is possible to improve the underlying data structure of Dijkstra’s algorithm to improve the
How bad is selfish routing?
- JOURNAL OF THE ACM
, 2002
"... We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route t ..."
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Cited by 657 (27 self)
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We consider the problem of routing traffic to optimize the performance of a congested network. We are given a network, a rate of traffic between each pair of nodes, and a latency function for each edge specifying the time needed to traverse the edge given its congestion; the objective is to route
Risk Aware Graph Search With Uncertain Edge Costs
"... Abstract—In this paper we present a novel approach to searching a graph with probabilistic edge costs. By incorporat-ing uncertainty information into the graph search we perform risk-aware planning. We present the results through a simple search domain, and report the improved results compared to tr ..."
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Abstract—In this paper we present a novel approach to searching a graph with probabilistic edge costs. By incorporat-ing uncertainty information into the graph search we perform risk-aware planning. We present the results through a simple search domain, and report the improved results compared
Distortion invariant object recognition in the dynamic link architecture
- IEEE TRANSACTIONS ON COMPUTERS
, 1993
"... We present an object recognition system based on the Dynamic Link Architecture, which is an extension to classical Artificial Neural Networks. The Dynamic Link Architecture ex-ploits correlations in the fine-scale temporal structure of cellular signals in order to group neurons dynamically into hig ..."
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Cited by 637 (80 self)
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are represented by sparse graphs, whose vertices are labeled by a multi-resolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a
A Scalable, Commodity Data Center Network Architecture
, 2008
"... Today’s data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfo ..."
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Cited by 466 (18 self)
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. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50 % of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Nonuniform bandwidth among data center nodes complicates application design and limits
CHANCE CONSTRAINED BOTTLENECK SPANNING TREE PROBLEM WITH FUZZY RANDOM EDGE COSTS
, 1999
"... Abstract This paper considers a generalized fuzzy random version of bottleneck spanning tree problem in which edge costs are fuzzy random variables. The problem is to find an optimal spanning tree under chance constraint with respect to possibility measure of bottleneck (maximum cost) edge of spanni ..."
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Abstract This paper considers a generalized fuzzy random version of bottleneck spanning tree problem in which edge costs are fuzzy random variables. The problem is to find an optimal spanning tree under chance constraint with respect to possibility measure of bottleneck (maximum cost) edge
Results 1 - 10
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