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Conference Papers Year : 2019

Inapproximability of Clustering in Lp-metrics

Vincent Cohen-Addad
Karthik Srikanta
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Abstract

Proving hardness of approximation for min-sum objectives is an infamous challenge. For classic problems such as the Traveling Salesman problem, the Steiner tree problem, or the k-means and k-median problems, the best known inapproximability bounds for p-metrics of dimension O(log n) remain well below 1.01. In this paper, we take a significant step to improve the hardness of approximation of the k-means problem in various p-metrics, and more particularly on 1 , 2 , Hamming and ∞ metrics of dimension Ω(log n). We show that it is hard to approximate the k-means objective in O(log n)-dimensional space: (1) To a factor of 3.94 in the ∞-metric when centers have to be chosen from a discrete set of locations (i.e., the discrete case). This improves upon the result of Guruswami and Indyk (SODA'03) who proved hardness of approximation for a factor less than 1.01. (2) To a factor of 1.56 in the 1-metric and to a factor of 1.17 in the 2-metric, both in the discrete case. This improves upon the result of Trevisan (SICOMP'00) who proved hardness of approximation for a factor less than 1.01 in both the metrics. (3) To a factor of 1.07 in the 2-metric, when centers can be placed at arbitrary locations, (i.e., the continuous case). This improves on a result of Lee-Schmidt-Wright (IPL'17) who proved hardness of approximation for a factor of 1.0013. We also obtain similar improvements over the state-of-the-art hardness of approximation results for the k-median objective in various p-metrics. Our hardness result given in (1) above, is under the standard NP = P assumption, whereas all the remaining results given above are under the Unique Games Conjecture (UGC). We can remove our reliance on UGC and prove standard NP-hardness for the above problems but for smaller approximation factors. Finally, we note that in order to obtain our result for the 1 and ∞-metrics in O(log n)-dimensional space we introduce an embedding technique which combines the transcripts of certain communication protocols with the geometric realization of certain graphs
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Dates and versions

hal-02360762 , version 1 (13-11-2019)
hal-02360762 , version 2 (15-12-2019)

Identifiers

  • HAL Id : hal-02360762 , version 2

Cite

Vincent Cohen-Addad, Karthik Srikanta. Inapproximability of Clustering in Lp-metrics. FOCS'19 - 60th Annual IEEE Symposium on Foundations of Computer Science, Nov 2019, Baltimore, United States. ⟨hal-02360762v2⟩
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