
Submodular Function Maximization in Parallel via the Multilinear Relaxation
Balkanski and Singer [5] recently initiated the study of adaptivity (or ...
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Few Cuts Meet Many Point Sets
We study the problem of how to breakup many point sets in R^d into small...
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Unambiguous DNFs and AlonSaksSeymour
We exhibit an unambiguous kDNF formula that requires CNF width Ω̃(k^2),...
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Optimal CPU Scheduling in Data Centers via a FiniteTime Distributed Quantized Coordination Mechanism
In this paper we analyze the problem of optimal task scheduling for data...
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A note on overrelaxation in the Sinkhorn algorithm
We derive an apriori parameter range for overrelaxation of the Sinkhorn...
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Distributed Submodular Minimization via BlockWise Updates and Communications
In this paper we deal with a network of computing agents with local proc...
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"Bring Your Own Greedy"+Max: NearOptimal 1/2Approximations for Submodular Knapsack
The problem of selecting a smallsize representative summary of a large ...
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Distributed Maximization of Submodular and Approximately Submodular Functions
We study the problem of maximizing a submodular function, subject to a cardinality constraint, with a set of agents communicating over a connected graph. We propose a distributed greedy algorithm that allows all the agents to converge to a nearoptimal solution to the global maximization problem using only local information and communication with neighbors in the graph. The nearoptimal solution approaches the (11/e) approximation of the optimal solution to the global maximization problem with an additive factor that depends on the number of communication steps in the algorithm. We then analyze convergence guarantees of the proposed algorithm. This analysis reveals a tradeoff between the number of communication steps and the performance of the algorithm. Finally, we extend our analysis to nonsubmodular settings, using the notion of approximate submodularity.
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