Fully Dynamic k-Clustering in O(k) Update Time

Code

• github.com/martin-costa/neurips23-dynamic-k-clustering
  OfficialIn papernone★ 1

Abstract

We present a O(1)-approximate fully dynamic algorithm for the k-median and k-means problems on metric spaces with amortized update time O(k) and worst-case query time O(k^2). We complement our theoretical analysis with the first in-depth experimental study for the dynamic k-median problem on general metrics, focusing on comparing our dynamic algorithm to the current state-of-the-art by Henzinger and Kale [ESA'20]. Finally, we also provide a lower bound for dynamic k-median which shows that any O(1)-approximate algorithm with O(poly(k)) query time must have (k) amortized update time, even in the incremental setting.

Reproductions