2017
NIPS
NeurIPS 2017
Decomposable Submodular Function Minimization: Discrete and Continuous
Abstract
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.
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— combinatorial optimization
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