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arXiv · 2107.07358

A Refined Approximation for Euclidean k-Means

Fabrizio Grandoni·Rafail Ostrovsky·Yuval Rabani·Leonard J. Schulman·Rakesh Venkat
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Abstract

In the Euclidean $k$-Means problem we are given a collection of $n$ points $D$ in an Euclidean space and a positive integer $k$. Our goal is to identify a collection of $k$ points in the same space (centers) so as to minimize the sum of the squared Euclidean distances between each point in $D$ and the closest center. This problem is known to be APX-hard and the current best approximation ratio is a primal-dual $6.357$ approximation based on a standard LP for the problem [Ahmadian et al. FOCS'17, SICOMP'20]. In this note we show how a minor modification of Ahmadian et al.'s analysis leads to a slightly improved $6.12903$ approximation. As a related result, we also show that the mentioned LP has integrality gap at least $\frac{16+\sqrt{5}}{15}>1.2157$.

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Fabrizio Grandoni, Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman, Rakesh Venkat. 2021-07-15. A Refined Approximation for Euclidean k-Means. https://arxiv.org/abs/2107.07358

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