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The Ethereal
Decomposable Submodular Function Minimization: Discrete and Continuous
March 06, 2017 ยท Declared Dead ยท ๐ Neural Information Processing Systems
"No code URL or promise found in abstract"
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Authors
Alina Ene, Huy L. Nguyen, Lรกszlรณ A. Vรฉgh
arXiv ID
1703.01830
Category
cs.LG: Machine Learning
Cross-listed
cs.DS
Citations
25
Venue
Neural Information Processing Systems
Last Checked
3 months ago
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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