Submodular Maximization Through Barrier Functions

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a monotone submodular function subject to the combination of a k -matchoid and null -knapsack constraints (for null k), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an ε error, it is guaranteed that we have found a feasible set with a 2(k +1+ ε)-approximation factor which can indeed be further improved to ( k +1+ ε) by an enumeration technique. We extensively evaluate the performance of our proposed algorithm over several real-world applications, including a movie recommendation system, summarization tasks for Y ouTube videos, Twitter feeds and Y elp business locations, and a set cover problem.