Predictive Flows for Faster Ford-Fulkerson
Data Structures and Algorithms
2023-03-03 v1
Abstract
Recent work has shown that leveraging learned predictions can improve the running time of algorithms for bipartite matching and similar combinatorial problems. In this work, we build on this idea to improve the performance of the widely used Ford-Fulkerson algorithm for computing maximum flows by seeding Ford-Fulkerson with predicted flows. Our proposed method offers strong theoretical performance in terms of the quality of the prediction. We then consider image segmentation, a common use-case of flows in computer vision, and complement our theoretical analysis with strong empirical results.
Keywords
Cite
@article{arxiv.2303.00837,
title = {Predictive Flows for Faster Ford-Fulkerson},
author = {Sami Davies and Benjamin Moseley and Sergei Vassilvitskii and Yuyan Wang},
journal= {arXiv preprint arXiv:2303.00837},
year = {2023}
}