Titlebook: Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques; 8th International Wo Chandra Chekuri,Klaus Jansen,L

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0302-9743 Overview: Includes supplementary material: 978-3-540-28239-6978-3-540-31874-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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F. Kraus,Erich Meyer,L. Langsteinproblems. Our algorithms use and extend the Boosted Sampling framework of?[6]. We also show how the framework can be adapted to give approximation algorithms even when the inflation parameters are correlated with the scenarios.

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The Network as a Storage Device: Dynamic Routing with Bounded Bufferss capable of transmitting a fixed number of packets per unit of time. At any moment in time, packets are injected at various network nodes with each packet specifying its destination node. The goal is to maximize the ., defined as the number of packets delivered to their destinations..In this paper,

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What Would Edmonds Do? Augmenting Paths and Witnesses for Degree-Bounded MSTsunds.K?nemann and Ravi [10,11] give bicriteria approximation algorithms for the problem using local search techniques of Fischer?[5]. For a graph with a cost ., degree . spanning tree, and parameters ., .> 1, their algorithm produces a tree whose cost is at most . and whose degree is at most . We gi

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Where’s the Winner? Max-Finding and Sorting with Metric Costs costs one unit (of time, work, etc.); the goal of an algorithm is to minimize the total cost incurred. However, a body of recent work has attempted to find ways to weaken this assumption – in particular, new algorithms have been given for these basic problems of searching, sorting and selection, wh