Learning-Augmented Algorithms for the Bahncard Problem
Abstract
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.
Keywords
Cite
@article{arxiv.2410.15257,
title = {Learning-Augmented Algorithms for the Bahncard Problem},
author = {Hailiang Zhao and Xueyan Tang and Peng Chen and Shuiguang Deng},
journal= {arXiv preprint arXiv:2410.15257},
year = {2024}
}
Comments
This paper has been accepted by the 38th Conference on Neural Information Processing Systems (NeurIPS 2024)