An Optimal Algorithm for Online Unconstrained Submodular Maximization

TL;DR

This paper introduces a polynomial-time online algorithm for unconstrained submodular maximization that guarantees a 1/2-approximation of the best fixed subset in hindsight, advancing the field of online submodular optimization.

Contribution

It presents the first efficient no-1/2-regret algorithm for online unconstrained submodular maximization, with a novel subroutine for the two-experts problem.

Findings

Achieves expected total value at least 1/2 of the best fixed subset in hindsight.

Establishes that the 1/2 factor cannot be improved by polynomial algorithms.

Provides a strong regret guarantee for a new two-experts subroutine.

Abstract

We consider a basic problem at the interface of two fundamental fields: submodular optimization and online learning. In the online unconstrained submodular maximization (online USM) problem, there is a universe $[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily monotone) submodular functions arrive over time. The goal is to design a computationally efficient online algorithm, which chooses a subset of $[n]$ at each time step as a function only of the past, such that the accumulated value of the chosen subsets is as close as possible to the maximum total value of a fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret algorithm for this problem, meaning that for every sequence of nonnegative submodular functions, the algorithm's expected total value is at least $1/2$ times that of the best subset in hindsight, up to an error term sublinear in $T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online algorithm when the submodular functions are presented as value oracles. Previous work on the offline problem implies that picking a subset uniformly at random in each time step achieves zero $1/4$-regret. A byproduct of our techniques is an explicit subroutine for the two-experts problem that has an unusually strong regret guarantee: the total value of its choices is comparable to twice the total value of either expert on rounds it did not pick that expert. This subroutine may be of independent interest.