Titlebook: WALCOM: Algorithms and Computation; 7th International Wo Subir Kumar Ghosh,Takeshi Tokuyama Conference proceedings 2013 Springer-Verlag Ber

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Base Location Problems for Base-Monotone Regionsl grid. We also present an .(..)-time 2-approximation algorithm for this problem. We then study two related problems, the . base-segment problem and the quad-decomposition problem, and present some complexity results for them.

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Exact and Approximation Algorithms for Densest ,-Subgraphiders as parameter the treewidth of the input graph and uses exponential space, while the second is parameterized by the size of the minimum vertex cover and uses polynomial space. Finally, we propose several approximation algorithms running in moderately exponential or parameterized time.

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Exact and Approximation Algorithms for Densest ,-Subgraphiders as parameter the treewidth of the input graph and uses exponential space, while the second is parameterized by the size of the minimum vertex cover and uses polynomial space. Finally, we propose several approximation algorithms running in moderately exponential or parameterized time.

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Approximation Algorithms for the Partition Vertex Cover Problemhm is based on a novel LP relaxation for this problem. This LP relaxation is obtained by adding knapsack cover inequalities to a natural LP relaxation of the problem. We show that this LP has integrality gap of .(log.), where . is the number of sets in the partition of the edge set. We also extend our result to more general settings.

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micronized

Adversarial Prediction: Lossless Predictors and Fractal Like Adversariesis trying to predict the future bit(s) from the past bits. This is like gambling on the future bits which involves the risk of making mistakes while shooting for profit from right predictions. Say the algorithm gets a payoff of 1 on a right prediction and − 1 on wrong predictions (and is also make f

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Adversarial Prediction: Lossless Predictors and Fractal Like Adversariesis trying to predict the future bit(s) from the past bits. This is like gambling on the future bits which involves the risk of making mistakes while shooting for profit from right predictions. Say the algorithm gets a payoff of 1 on a right prediction and − 1 on wrong predictions (and is also make f