Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
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.
Cite
@article{arxiv.2109.01556,
title = {Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems},
author = {Bo Sun and Russell Lee and Mohammad Hajiesmaili and Adam Wierman and Danny H. K. Tsang},
journal= {arXiv preprint arXiv:2109.01556},
year = {2021}
}