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A 2-Approximation Algorithm for Data-Distributed Metric k-Center
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abstract
In a metric space, a set of point sets of roughly the same size and an integer $k\geq 1$ 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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Cited by 1 Pith paper
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Fully Scalable MPC Algorithms for Euclidean k-Center
New constant-round, fully scalable MPC algorithms improve Euclidean k-center approximation to (2+ε) in low dimension and O(log n/log log n) in high dimension.
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