@inproceedings{
zhao2024learningaugmented,
title={Learning-Augmented Algorithms for the Bahncard Problem},
author={Hailiang Zhao and Xueyan Tang and Peng Chen and Shuiguang Deng},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=3cb6pF3Tvf}
}
In this paper, we study learning-augmented algorithms for the Bahncard problem. The Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to irrevocably and repeatedly decide between a cheap short-term solution and an expensive long-term one with an unknown future. Even though the problem is canonical, only a primal-dual-based learning-augmented algorithm was explicitly designed for it. We develop a new learning-augmented algorithm, named PFSUM, that incorporates both history and short-term future to improve online decision making. We derive the competitive ratio of PFSUM as a function of the prediction error and conduct extensive experiments to show that PFSUM outperforms the primal-dual-based algorithm.
Learning-Augmented Algorithms for the Bahncard Problem
Hailiang Zhao Xueyan Tang Peng Chen Shuiguang Deng