A 2-Approximation Algorithm for Data-Distributed Metric k-Center
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In a metric space, a set of point sets of roughly the same size and an integer k≥ 1 are given as the input and the goal of data-distributed k-center is to find a subset of size k of the input points as the set of centers to minimize the maximum distance from the input points to their closest centers. Metric k-center is known to be NP-hard which carries to the data-distributed setting. We give a 2-approximation algorithm of k-center for sublinear k in the data-distributed setting, which is tight. This algorithm works in several models, including the massively parallel computation model (MPC).
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