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).
@article{arxiv.2309.04327,
title = {A 2-Approximation Algorithm for Data-Distributed Metric k-Center},
author = {Sepideh Aghamolaei and Mohammad Ghodsi},
journal= {arXiv preprint arXiv:2309.04327},
year = {2023}
}