Constrained Submodular Maximization via New Bounds for DR-Submodular Functions

Submodular maximization under various constraints is a fundamental problem studied continuously, in both computer science and operations research, since the late 1970’s. A central technique in this field is to approximately optimize the multilinear extension of the submodular objective, and then round the solution. The use of this technique requires a solver able to approximately maximize multilinear extensions. Following a long line of work, Buchbinder and Feldman (2019) described such a solver guaranteeing 0.385-approximation for down-closed constraints, while Oveis Gharan and Vondrák (2011) showed that no solver can guarantee better than 0.478-approximation. In this paper, we present a solver guaranteeing 0.401-approximation, which significantly reduces the gap between the best known solver and the inapproximability result. The design and analysis of our solver are based on a novel bound that we prove for DR-submodular functions. This bound improves over a previous bound due to Feldman et al. (2011) that is used by essentially all state-of-the-art results for constrained maximization of general submodular/DR-submodular functions. Hence, we believe that our new bound is likely to find many additional applications in related problems, and to be a key component for further improvement.