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Decomposable submodular function minimization: discrete and continuous

Ene, Alina, Nguyen, Huy and Végh, László A. (2017) Decomposable submodular function minimization: discrete and continuous. In: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. and Garnett, R., (eds.) Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings. Neural Information Processing Systems Foundation, Long Beach, USA.

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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

Item Type: Book Section
Official URL: https://papers.nips.cc/book/advances-in-neural-inf...
Additional Information: © 2017 Neural Information Processing Systems Foundation
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Sets: Departments > Mathematics
Date Deposited: 08 Jan 2018 16:24
Last Modified: 20 Nov 2019 02:32
URI: http://eprints.lse.ac.uk/id/eprint/86395

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