# Decomposable Submodular Function Minimization: Discrete and Continuous

arXiv: https://arxiv.org/abs/1703.01830 · HTML: https://mlsanity.com/paper/1703.01830/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond](https://mlsanity.com/paper/2101.05510/) (2021)
- [Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation](https://mlsanity.com/paper/2308.13269/) (2023)
- [Decomposable Submodular Function Minimization via Maximum Flow](https://mlsanity.com/paper/2103.03868/) (2021)
- [Sparsification of Decomposable Submodular Functions](https://mlsanity.com/paper/2201.07289/) (2022)
- [Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components](https://mlsanity.com/paper/2110.14859/) (2021)

## Abstract (arXiv)

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.