This is the public, feature-limited version of the conference webpage. After Registration and login please visit the full version.

The Primal-Dual method for Learning Augmented Algorithms

Etienne Bamas, Andreas Maggiori, Ola Svensson

Oral presentation: Orals & Spotlights Track 32: Optimization
on 2020-12-10T18:15:00-08:00 - 2020-12-10T18:30:00-08:00
Poster Session 7 (more posters)
on 2020-12-10T21:00:00-08:00 - 2020-12-10T23:00:00-08:00
Abstract: The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online covering problems. We compare our algorithms to the cost of the true and predicted offline optimal solutions and show that these algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.

Preview Video and Chat

To see video, interact with the author and ask questions please use registration and login.