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