Continuous Non-monotone DR-submodular Maximization with Down-closed Convex Constraint

TL;DR

This paper studies the maximization of non-monotone DR-submodular functions under down-closed convex constraints, revealing challenges with stationary points and extending algorithms from discrete to continuous domains with improved approximation guarantees.

Contribution

It demonstrates that stationary points can be arbitrarily bad in non-monotone cases, and extends existing algorithms to the continuous domain while maintaining approximation ratios.

Findings

Stationary points can have arbitrarily poor approximation ratios.

Extended algorithms retain approximation guarantees in continuous domains.

Numerical experiments validate the algorithms on machine learning problems.

Abstract

We investigate the continuous non-monotone DR-submodular maximization problem subject to a down-closed convex solvable constraint. Our first contribution is to construct an example to demonstrate that (first-order) stationary points can have arbitrarily bad approximation ratios, and they are usually on the boundary of the feasible domain. These findings are in contrast with the monotone case where any stationary point yields a $1/2$-approximation (Hassani et al. (2017)). Moreover, this example offers insights on how to design improved algorithms by avoiding bad stationary points, such as the restricted continuous local search algorithm (Chekuri et al. (2014)) and the aided measured continuous greedy (Buchbinder and Feldman (2019)). However, the analyses in the last two algorithms only work for the discrete domain because both need to invoke the inequality that the multilinear extension of any submodular set function is bounded from below by its Lovasz extension. Our second contribution, therefore, is to remove this restriction and show that both algorithms can be extended to the continuous domain while retaining the same approximation ratios, and hence offering improved approximation ratios over those in Bian et al. (2017a). for the same problem. At last, we also include numerical experiments to demonstrate our algorithms on problems arising from machine learning and artificial intelligence.