Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
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Meta-theorems convert planar-graph α-approximation LOCAL algorithms for cuttable minimization problems into f(g)-round (3α+1)-approximations on bounded-genus graphs, yielding a (34+ε) approximation for MDS that improves prior bounds.
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Distributed Stochastic Graph Algorithms
Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
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Meta-Theorems for Cuttable Distributed Problems
Meta-theorems convert planar-graph α-approximation LOCAL algorithms for cuttable minimization problems into f(g)-round (3α+1)-approximations on bounded-genus graphs, yielding a (34+ε) approximation for MDS that improves prior bounds.