Titlebook: Algorithms and Computation; 8th International Wo Sudebkumar Prasant Pal,Kunihiko Sadakane Conference proceedings 2014 Springer Internationa

危机

SAHN Clustering in Arbitrary Metric Spaces Using Heuristic Nearest Neighbor Search in many application domains, e.g., in cheminformatics. Asymptotically optimal SAHN clustering algorithms are known for arbitrary dissimilarity measures, but their quadratic time and space complexity even in the best case still limits the applicability to small data sets. We present a new pivot base

prolate

Optimal Serial Broadcast of Successive Chunksto exactly one node at a time. This models the delivery of a video stream in a Peer-to-Peer (P2P) network in which chunks of a stream are successively given by the media server. We propose two schemes for solving the problem. The first scheme attains an optimum broadcast time of . steps, where . is

破布

兵团

Minimax Regret Sink Location Problem in Dynamic Tree Networks with Uniform Capacity tree with positive edge lengths and uniform edge capacity, and the vertex supply which is nonnegative value is unknown but only the interval of supply is known. A particular realization of supply to each vertex is called a scenario. Under any scenario, the cost of a sink location . is defined as th

LUMEN

On a Class of Covering Problems with Variable Capacities in Wireless Networkshe power levels of the base stations. We model the interference due to the increased power usage resulting in greater serving radius, as capacities that are non-increasing with respect to the covering radius. We consider three models. In the first model the location of the base stations and the clie

珠宝

obstruct

Some Results on Point Visibility Graphstate new properties of point visibility graphs along with some known properties that are important in understanding point visibility graphs. For planar point visibility graphs, we present a complete characterization which leads to a linear time recognition and reconstruction algorithm.

squander

上腭

I/O Efficient Algorithms for the Minimum Cut Problem on Unweighted Undirected Graphs(.)) on the number of I/Os is shown for the problem, where . is the number of vertices and . is the number of edges. Then the following are presented, for . = Ω(..), (1) a minimum cut algorithm that uses . I/Os; here MSF(.,.) is the number of I/Os needed to compute a minimum spanning tree of the gra

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