Online Non-Monotone DR-submodular Maximization

TL;DR

This paper introduces online algorithms for maximizing non-monotone DR-submodular functions over various convex sets, achieving near-optimal approximation ratios with provable regret bounds, and demonstrates their effectiveness on real-world machine learning datasets.

Contribution

It presents the first online algorithms with approximation guarantees for non-monotone DR-submodular maximization over general convex sets, including down-closed and hypercube sets.

Findings

Achieves a 1/e approximation with O(T^{2/3}) regret for down-closed convex sets.

Provides a 1/2 approximation with O(√T) regret for hypercube sets.

Demonstrates effectiveness on real-world machine learning datasets.

Abstract

In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. It captures a subclass of non-convex optimization that provides both theoretical and practical guarantees. Here, we focus on minimizing regret for online arriving non-monotone DR-submodular functions over different types of convex sets: hypercube, down-closed and general convex sets. First, we present an online algorithm that achieves a $1/e$-approximation ratio with the regret of $O(T^{2/3})$ for maximizing DR-submodular functions over any down-closed convex set. Note that, the approximation ratio of $1/e$ matches the best-known guarantee for the offline version of the problem. Moreover, when the convex set is the hypercube, we propose a tight 1/2-approximation algorithm with regret bound of $O(\sqrt{T})$. Next, we give an online algorithm that achieves an approximation guarantee (depending on the search space) for the problem of maximizing non-monotone continuous DR-submodular functions over a \emph{general} convex set (not necessarily down-closed). To best of our knowledge, no prior algorithm with approximation guarantee was known for non-monotone DR-submodular maximization in the online setting. Finally we run experiments to verify the performance of our algorithms on problems arising in machine learning domain with the real-world datasets.