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arXiv · 2309.04327

A 2-Approximation Algorithm for Data-Distributed Metric k-Center

Sepideh Aghamolaei·Mohammad Ghodsi
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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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Sepideh Aghamolaei, Mohammad Ghodsi. 2023-09-08. A 2-Approximation Algorithm for Data-Distributed Metric k-Center. https://arxiv.org/abs/2309.04327

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