ML Anthology

Decomposable Submodular Function Minimization: Discrete and Continuous

Alina Ene, Huy Nguyen, László A. Végh
NeurIPS 2017 pp. 2870-2880
/neurips/2017/ene2017neurips-decomposable/

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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Cite

Text

Ene et al. "Decomposable Submodular Function Minimization: Discrete and Continuous." Neural Information Processing Systems, 2017.

Markdown

[Ene et al. "Decomposable Submodular Function Minimization: Discrete and Continuous." Neural Information Processing Systems, 2017.](https://mlanthology.org/neurips/2017/ene2017neurips-decomposable/)

BibTeX

@inproceedings{ene2017neurips-decomposable,
  title     = {{Decomposable Submodular Function Minimization: Discrete and Continuous}},
  author    = {Ene, Alina and Nguyen, Huy and Végh, László A.},
  booktitle = {Neural Information Processing Systems},
  year      = {2017},
  pages     = {2870-2880},
  url       = {https://mlanthology.org/neurips/2017/ene2017neurips-decomposable/}
}