Fast Non-Monotone Submodular Maximisation Subject to a Matroid Constraint

TL;DR

This paper introduces the first practical approximation algorithm for maximizing a non-negative submodular function under a matroid constraint, achieving near-optimal guarantees with a detailed oracle call complexity analysis.

Contribution

It presents a novel combination of existing algorithms to efficiently approximate non-monotone submodular maximization under matroid constraints.

Findings

Achieves a .5-epsilon approximation ratio.

Provides an explicit bound on the number of value oracle calls.

Handles functions with negative marginal values effectively.

Abstract

In this work we present the first practical $\left(\frac{1}{e}-\epsilon\right)$-approximation algorithm to maximise a general non-negative submodular function subject to a matroid constraint. Our algorithm is based on combining the decreasing-threshold procedure of Badanidiyuru and Vondrak (SODA 2014) with a smoother version of the measured continuous greedy algorithm of Feldman et al. (FOCS 2011). This enables us to obtain an algorithm that requires $O(\frac{nr^2}{\epsilon^4} \big(\frac{a+b}{a}\big)^2 \log^2({\frac{n}{\epsilon}}))$ value oracle calls, where $n$ is the cardinality of the ground set, $r$ is the matroid rank, and $ b, a \in \mathbb{R}^+$ are the absolute values of the minimum and maximum marginal values that the function $f$ can take i.e.: $ -b \leq f_S(i) \leq a$, for all $i\in E$ and $S\subseteq E$, (here, $E$ is the ground set). The additional value oracle calls with respect to the work of Badanidiyuru and Vondrak come from the greater spread in the sampling of the multilinear extension that the possibility of negative marginal values introduce.