Online Primal-Dual Algorithms with Predictions for Packing Problems

TL;DR

This paper introduces a framework for online primal-dual algorithms with predictions tailored for non-linear packing problems, demonstrating improved performance in submodular maximization and ad-auction scenarios.

Contribution

It extends primal-dual algorithms with predictions to non-linear packing problems, a novel approach in online algorithm design.

Findings

Effective framework for non-linear packing problems.

Improved algorithms for submodular maximization.

Supporting experiments validate the approach.

Abstract

The domain of online algorithms with predictions has been extensively studied for different applications such as scheduling, caching (paging), clustering, ski rental, etc. Recently, Bamas et al., aiming for an unified method, have provided a primal-dual framework for linear covering problems. They extended the online primal-dual method by incorporating predictions in order to achieve a performance beyond the worst-case case analysis. In this paper, we consider this research line and present a framework to design algorithms with predictions for non-linear packing problems. We illustrate the applicability of our framework in submodular maximization and in particular ad-auction maximization in which the optimal bound is given and supporting experiments are provided.