Revisiting Decomposable Submodular Function Minimization with Incidence Relations
Abstract
We introduce a new approach to decomposable submodular function minimization (DSFM) that exploits incidence relations. Incidence relations describe which variables effectively influence the component functions, and when properly utilized, they allow for improving the convergence rates of DSFM solvers. Our main results include the precise parametrization of the DSFM problem based on incidence relations, the development of new scalable alternative projections and parallel coordinate descent methods and an accompanying rigorous analysis of their convergence rates.
Cite
Text
Li and Milenkovic. "Revisiting Decomposable Submodular Function Minimization with Incidence Relations." Neural Information Processing Systems, 2018.Markdown
[Li and Milenkovic. "Revisiting Decomposable Submodular Function Minimization with Incidence Relations." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/li2018neurips-revisiting/)BibTeX
@inproceedings{li2018neurips-revisiting,
title = {{Revisiting Decomposable Submodular Function Minimization with Incidence Relations}},
author = {Li, Pan and Milenkovic, Olgica},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {2237-2247},
url = {https://mlanthology.org/neurips/2018/li2018neurips-revisiting/}
}