# Benders Cut Classification via Support Vector Machines for Solving Two-stage Stochastic Programs

arXiv: https://arxiv.org/abs/1906.05994 · HTML: https://mlsanity.com/paper/1906.05994/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Machine Learning for Cutting Planes in Integer Programming: A Survey](https://mlsanity.com/paper/2302.09166/) (2023)
- [Learning to optimize: A tutorial for continuous and mixed-integer optimization](https://mlsanity.com/paper/2405.15251/) (2024)
- [Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize](https://mlsanity.com/paper/2310.07082/) (2023)
- [Accelerating process control and optimization via machine learning: A review](https://mlsanity.com/paper/2412.18529/) (2024)
- [Learning to control inexact Benders decomposition via reinforcement learning](https://mlsanity.com/paper/2508.06700/) (2025)
- [Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition](https://mlsanity.com/paper/2511.11870/) (2025)
- [Learning to Cut: Reinforcement Learning for Benders Decomposition](https://mlsanity.com/paper/2605.06516/) (2026)
- [Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition](https://mlsanity.com/paper/2311.10835/) (2023)
- [Feasibility-Aware Imitation Learning for Benders Decomposition](https://mlsanity.com/paper/2604.04801/) (2026)

## Abstract (arXiv)

We consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification step in each iteration to selectively generate cuts that are more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i) sampling cuts and collecting information from training problems and (ii) solving testing problems with a support vector machine (SVM) cut classifier. We run the LearnBD algorithm on instances of capacitated facility location and multi-commodity network design under uncertain demand. Our results show that SVM cut classifier works effectively for identifying valuable cuts, and the LearnBD algorithm reduces the total solving time of all instances for different problems with various sizes and complexities.