The paper gives a learning-augmented k-clustering algorithm for general metric (graph) spaces and an ETH-based query-complexity lower bound, though the lower-bound proof contains an arithmetic gap.
Approximating $k$-Median via Pseudo-Approximation
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We present a novel approximation algorithm for $k$-median that achieves an approximation guarantee of $1+\sqrt{3}+\epsilon$, improving upon the decade-old ratio of $3+\epsilon$. Our approach is based on two components, each of which, we believe, is of independent interest. First, we show that in order to give an $\alpha$-approximation algorithm for $k$-median, it is sufficient to give a \emph{pseudo-approximation algorithm} that finds an $\alpha$-approximate solution by opening $k+O(1)$ facilities. This is a rather surprising result as there exist instances for which opening $k+1$ facilities may lead to a significant smaller cost than if only $k$ facilities were opened. Second, we give such a pseudo-approximation algorithm with $\alpha= 1+\sqrt{3}+\epsilon$. Prior to our work, it was not even known whether opening $k + o(k)$ facilities would help improve the approximation ratio.
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cs.LG 1years
2025 1verdicts
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Learning Augmented Graph $k$-Clustering
The paper gives a learning-augmented k-clustering algorithm for general metric (graph) spaces and an ETH-based query-complexity lower bound, though the lower-bound proof contains an arithmetic gap.