Online Facility Location with Multiple Advice

Matteo Almanza, Flavio Chierichetti, Silvio Lattanzi, Alessandro Panconesi, Giuseppe Re

Keywords: Clustering, Facility Location, Online Algorithms, Machine-Learned Advice, Online Clustering

Abstract:

Clustering is a central topic in unsupervised learning and its online formulation has received a lot of attention in recent years. In this paper, we study the classic facility location problem in the presence of multiple machine-learned advice. We design an algorithm with provable performance guarantees such that, if the advice is good, it outperforms the best-known online algorithms for the problem, and if it is bad it still matches their performance.
We complement our theoretical analysis with an in-depth study of the performance of our algorithm, showing its effectiveness on synthetic and real-world data sets.

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Code: https://github.com/matteojug/Online-Facility-Location-with-Multiple-Advice