# Predictive Flows for Faster Ford-Fulkerson

arXiv: https://arxiv.org/abs/2303.00837 · HTML: https://mlsanity.com/paper/2303.00837/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2023.

## Built on by (most cited first, 20 of 20)

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- [Competitive strategies to use "warm start" algorithms with predictions](https://mlsanity.com/paper/2405.03661/) (2024)
- [Faster Discrete Convex Function Minimization with Predictions: The M-Convex Case](https://mlsanity.com/paper/2306.05865/) (2023)
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- [Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors](https://mlsanity.com/paper/2506.05479/) (2025)
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- [Learning-Augmented Streaming Algorithms for Correlation Clustering](https://mlsanity.com/paper/2510.10705/) (2025)
- [Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation](https://mlsanity.com/paper/2505.03955/) (2025)
- [Transductive and Learning-Augmented Online Regression](https://mlsanity.com/paper/2510.03917/) (2025)
- [Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimization](https://mlsanity.com/paper/2511.19573/) (2025)
- [Minimum-Cost Network Flow with Dual Predictions](https://mlsanity.com/paper/2601.20203/) (2026)
- [Better Learning-Augmented Spanning Tree Algorithms via Metric Forest Completion](https://mlsanity.com/paper/2602.24232/) (2026)
- [Graph Neural Network-Informed Predictive Flows for Faster Ford-Fulkerson and PAC-Learnability](https://mlsanity.com/paper/2604.21175/) (2026)
- [Incremental Strongly Connected Components with Predictions](https://mlsanity.com/paper/2604.26062/) (2026)
- [Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications](https://mlsanity.com/paper/2609.04787/) (2026)

## Abstract (arXiv)

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.