A Deterministic Algorithm for Maximizing Submodular Functions

TL;DR

This paper introduces the first deterministic algorithm that surpasses the 1/3 approximation ratio for maximizing non-negative submodular functions, achieving a 2/5 ratio through recursive local search composition.

Contribution

It presents the first deterministic algorithm with an approximation ratio better than 1/3 for submodular maximization, using recursive local search techniques.

Findings

Deterministic algorithm achieves 2/5 approximation ratio.

Recursion depth of 2 guarantees the approximation ratio.

Open question: potential improvement with increased recursion depth.

Abstract

The problem of maximizing a non-negative submodular function was introduced by Feige, Mirrokni, and Vondrak [FOCS'07] who provided a deterministic local-search based algorithm that guarantees an approximation ratio of $\frac 1 3$, as well as a randomized $\frac 2 5$-approximation algorithm. An extensive line of research followed and various algorithms with improving approximation ratios were developed, all of them are randomized. Finally, Buchbinder et al. [FOCS'12] presented a randomized $\frac 1 2$-approximation algorithm, which is the best possible. This paper gives the first deterministic algorithm for maximizing a non-negative submodular function that achieves an approximation ratio better than $\frac 1 3$. The approximation ratio of our algorithm is $\frac 2 5$. Our algorithm is based on recursive composition of solutions obtained by the local search algorithm of Feige et al. We show that the $\frac 2 5$ approximation ratio can be guaranteed when the recursion depth is $2$, and leave open the question of whether the approximation ratio improves as the recursion depth increases.