Maximizing Non-monotone Submodular Set Functions Subject to Different Constraints: Combined Algorithms

TL;DR

This paper introduces improved algorithms for maximizing non-monotone submodular functions under various constraints, achieving better approximation factors and extending the continuous greedy method to non-monotone cases.

Contribution

It presents new combined algorithms with improved approximation ratios for non-monotone submodular maximization under multiple constraints, including a novel application of the continuous greedy process.

Findings

Achieves a 0.25-2ε approximation for multiple knapsack constraints.

Develops a 0.13-approximation algorithm for maximization over down-monotone polytopes.

Extends continuous greedy process to non-monotone functions, impacting several discrete problems.

Abstract

We study the problem of maximizing constrained non-monotone submodular functions and provide approximation algorithms that improve existing algorithms in terms of either the approximation factor or simplicity. Our algorithms combine existing local search and greedy based algorithms. Different constraints that we study are exact cardinality and multiple knapsack constraints. For the multiple-knapsack constraints we achieve a $(0.25-2\epsilon)$-factor algorithm. We also show, as our main contribution, how to use the continuous greedy process for non-monotone functions and, as a result, obtain a $0.13$-factor approximation algorithm for maximization over any solvable down-monotone polytope. The continuous greedy process has been previously used for maximizing smooth monotone submodular function over a down-monotone polytope \cite{CCPV08}. This implies a 0.13-approximation for several discrete problems, such as maximizing a non-negative submodular function subject to a matroid constraint and/or multiple knapsack constraints.