Decomposable Submodular Function Minimization: Discrete and Continuous

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

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Alina Ene, Huy Nguyen, László A. Végh


This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the state-of-the-art continuous algorithms for the problem using combinatorial arguments. We also provide a systematic experimental comparison of the two types of methods, based on a clear distinction between level-0 and level-1 algorithms.