# Learning to Use Local Cuts

arXiv: https://arxiv.org/abs/2206.11618 · HTML: https://mlsanity.com/paper/2206.11618/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2022.

## Built on by (most cited first, 11 of 11)

- [Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming](https://mlsanity.com/paper/2402.05501/) (2024)
- [On Representing Mixed-Integer Linear Programs by Graph Neural Networks](https://mlsanity.com/paper/2210.10759/) (2022)
- [Machine Learning for Cutting Planes in Integer Programming: A Survey](https://mlsanity.com/paper/2302.09166/) (2023)
- [Learning to Configure Separators in Branch-and-Cut](https://mlsanity.com/paper/2311.05650/) (2023)
- [Learning To Dive In Branch And Bound](https://mlsanity.com/paper/2301.09943/) (2023)
- [Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming](https://mlsanity.com/paper/2401.17527/) (2024)
- [Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization](https://mlsanity.com/paper/2311.03350/) (2023)
- [Beyond Local Selection: Global Cut Selection for Enhanced Mixed-Integer Programming](https://mlsanity.com/paper/2503.15847/) (2025)
- [BenLOC: A Benchmark for Learning to Configure MIP Optimizers](https://mlsanity.com/paper/2506.02752/) (2025)
- [Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs](https://mlsanity.com/paper/2310.14659/) (2023)
- [Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs](https://mlsanity.com/paper/2412.17623/) (2024)

## Abstract (arXiv)

An essential component in modern solvers for mixed-integer (linear) programs (MIPs) is the separation of additional inequalities (cutting planes) to tighten the linear programming relaxation. Various algorithmic decisions are necessary when integrating cutting plane methods into a branch-and-bound (B&B) solver as there is always the trade-off between the efficiency of the cuts and their costs, given that they tend to slow down the solution time of the relaxation. One of the most crucial questions is: Should cuts only be generated globally at the root or also locally at nodes of the tree? We address this question by a machine learning approach for which we train a regression forest to predict the speed-up (or slow-down) provided by using local cuts. We demonstrate with an open implementation that this helps to improve the performance of the FICO Xpress MIP solver on a public test set of general MIP instances. We further report on the impact of a practical implementation inside Xpress on a large, diverse set of real-world industry MIPs.