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Submodular Maximization Through Barrier Functions
Ashwinkumar Badanidiyuru · Amin Karbasi · Ehsan Kazemi · Jan Vondrak

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1813
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 $\ell$-knapsack constraints (for $\ell\leq k$), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an $\epsilon$ error, it is guaranteed that we have found a feasible set with a $2(k+1+\epsilon)$-approximation factor which can indeed be further improved to $(k+1+\epsilon)$ 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 YouTube videos, Twitter feeds and Yelp business locations, and a set cover problem.

Author Information

Ashwinkumar Badanidiyuru (Google Research)
Amin Karbasi (Yale)
Ehsan Kazemi (Google)
Jan Vondrak (Stanford University)

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