Submodular Maximization by Simulated Annealing

TL;DR

This paper introduces a simulated annealing-based algorithm for maximizing submodular functions, achieving improved approximation ratios for unconstrained and constrained cases, and establishes new hardness bounds in the value oracle model.

Contribution

The paper presents a novel simulated annealing algorithm that improves approximation ratios for submodular maximization problems and provides new hardness results in the value oracle model.

Findings

Achieves 0.41-approximation for unconstrained submodular maximization.

Achieves 0.325-approximation for submodular maximization with matroid independence constraint.

Establishes hardness bounds of 0.478 and 0.394 for related constrained problems.

Abstract

We consider the problem of maximizing a nonnegative (possibly non-monotone) submodular set function with or without constraints. Feige et al. [FOCS'07] showed a 2/5-approximation for the unconstrained problem and also proved that no approximation better than 1/2 is possible in the value oracle model. Constant-factor approximation was also given for submodular maximization subject to a matroid independence constraint (a factor of 0.309 Vondrak [FOCS'09]) and for submodular maximization subject to a matroid base constraint, provided that the fractional base packing number is at least 2 (a 1/4-approximation, Vondrak [FOCS'09]). In this paper, we propose a new algorithm for submodular maximization which is based on the idea of {\em simulated annealing}. We prove that this algorithm achieves improved approximation for two problems: a 0.41-approximation for unconstrained submodular maximization, and a 0.325-approximation for submodular maximization subject to a matroid independence constraint. On the hardness side, we show that in the value oracle model it is impossible to achieve a 0.478-approximation for submodular maximization subject to a matroid independence constraint, or a 0.394-approximation subject to a matroid base constraint in matroids with two disjoint bases. Even for the special case of cardinality constraint, we prove it is impossible to achieve a 0.491-approximation. (Previously it was conceivable that a 1/2-approximation exists for these problems.) It is still an open question whether a 1/2-approximation is possible for unconstrained submodular maximization.