## Learning from a Sample in Online Algorithms

### C.J. Argue · Alan Frieze · Anupam Gupta · Christopher Seiler

##### Hall J #936

Keywords: [ Steiner tree ] [ Facility Location ] [ Learning Augmented Algorithms ] [ Sample Complexity ] [ online algorithms ] [ Optimization ] [ Clustering ] [ Load Balancing ]

[ Abstract ]
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Tue 29 Nov 2 p.m. PST — 4 p.m. PST

Spotlight presentation: Lightning Talks 6B-2
Thu 8 Dec 5:30 p.m. PST — 5:45 p.m. PST

Abstract: We consider three central problems in optimization: the restricted assignment load-balancing problem, the Steiner tree network design problem, and facility location clustering. We consider the online setting, where the input arrives over time, and irrevocable decisions must be made without knowledge of the future. For all these problems, any online algorithm must incur a cost that is approximately $\log |I|$ times the optimal cost in the worst-case, where $|I|$ is the length of the input. But can we go beyond the worst-case? In this work we give algorithms that perform substantially better when a $p$-fraction of the input is given as a sample: the algorithm use this sample to \emph{learn} a good strategy to use for the rest of the input.

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