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arXiv · 2208.14129

On the Fixed-Parameter Tractability of Capacitated Clustering

Vincent Cohen-Addad·Jason Li
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Abstract

We study the complexity of the classic capacitated k-median and k-means problems parameterized by the number of centers, k. These problems are notoriously difficult since the best known approximation bound for high dimensional Euclidean space and general metric space is $\Theta(\log k)$ and it remains a major open problem whether a constant factor exists. We show that there exists a $(3+\epsilon)$-approximation algorithm for the capacitated k-median and a $(9+\epsilon)$-approximation algorithm for the capacitated k-means problem in general metric spaces whose running times are $f(\epsilon,k) n^{O(1)}$. For Euclidean inputs of arbitrary dimension, we give a $(1+\epsilon)$-approximation algorithm for both problems with a similar running time. This is a significant improvement over the $(7+\epsilon)$-approximation of Adamczyk et al. for k-median in general metric spaces and the $(69+\epsilon)$-approximation of Xu et al. for Euclidean k-means.

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Vincent Cohen-Addad, Jason Li. 2022-08-30. On the Fixed-Parameter Tractability of Capacitated Clustering. https://arxiv.org/abs/2208.14129

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