Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems

• URL: http://arxiv.org/abs/2109.01556v1
• Date: Fri, 3 Sep 2021 14:27:33 GMT
• Title: Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
• Authors: Bo Sun, Russell Lee, Mohammad Hajiesmaili, Adam Wierman, Danny H.K. Tsang
• Abstract summary: We design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate. We also guarantee a worst-case competitive ratio regardless of the prediction quality. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.
• Score: 13.593621603504847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper leverages machine-learned predictions to design competitive algorithms for online conversion problems with the goal of improving the competitive ratio when predictions are accurate (i.e., consistency), while also guaranteeing a worst-case competitive ratio regardless of the prediction quality (i.e., robustness). We unify the algorithmic design of both integral and fractional conversion problems, which are also known as the 1-max-search and one-way trading problems, into a class of online threshold-based algorithms (OTA). By incorporating predictions into design of OTA, we achieve the Pareto-optimal trade-off of consistency and robustness, i.e., no online algorithm can achieve a better consistency guarantee given for a robustness guarantee. We demonstrate the performance of OTA using numerical experiments on Bitcoin conversion.

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