Decomposable Submodular Function Minimization: Discrete and Continuous
This work addresses optimization challenges in machine learning and operations research, offering incremental improvements in algorithm efficiency.
The paper tackled decomposable submodular function minimization by improving running time estimates for continuous algorithms using combinatorial arguments and conducting a systematic experimental comparison between discrete and continuous methods, resulting in enhanced performance insights.
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.