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Poster
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Matthew Fahrbach · Vahab Mirrokni · Morteza Zadimoghaddam

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #141
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Submodular maximization is a general optimization problem with a wide range of applications in machine learning (e.g., active learning, clustering, and feature selection). In large-scale optimization, the parallel running time of an algorithm is governed by its adaptivity, which measures the number of sequential rounds needed if the algorithm can execute polynomially-many independent oracle queries in parallel. While low adaptivity is ideal, it is not sufficient for an algorithm to be efficient in practice---there are many applications of distributed submodular optimization where the number of function evaluations becomes prohibitively expensive. Motivated by these applications, we study the adaptivity and query complexity of submodular maximization. In this paper, we give the first constant-factor approximation algorithm for maximizing a non-monotone submodular function subject to a cardinality constraint $k$ that runs in $O(\log(n))$ adaptive rounds and makes $O(n \log(k))$ oracle queries in expectation. In our empirical study, we use three real-world applications to compare our algorithm with several benchmarks for non-monotone submodular maximization. The results demonstrate that our algorithm finds competitive solutions using significantly fewer rounds and queries.
Keywords: Combinatorial Optimization · Large Scale Learning and Big Data · Parallel and Distributed Learning

Author Information

Matthew Fahrbach (Georgia Institute of Technology)
Vahab Mirrokni (Google Research)
Morteza Zadimoghaddam (Google)

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