Chasing Convex Functions with Long-term Constraints
Abstract
We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions in a metric space to simultaneously minimize their hitting cost and switching cost as determined by the metric. Over the time horizon , the player must satisfy a long-term demand constraint , where denotes the fraction of demand satisfied at time . Such problems can find a wide array of applications to online resource allocation in sustainable energy/computing systems. We devise optimal competitive and learning-augmented algorithms for the case of bounded hitting cost gradients and weighted metrics, and further show that our proposed algorithms perform well in numerical experiments.
Keywords
Cite
@article{arxiv.2402.14012,
title = {Chasing Convex Functions with Long-term Constraints},
author = {Adam Lechowicz and Nicolas Christianson and Bo Sun and Noman Bashir and Mohammad Hajiesmaili and Adam Wierman and Prashant Shenoy},
journal= {arXiv preprint arXiv:2402.14012},
year = {2024}
}
Comments
Accepted to ICML 2024. 31 pages, 12 figures