# A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming

arXiv: https://arxiv.org/abs/2307.07322 · HTML: https://mlsanity.com/paper/2307.07322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Reinforcement Learning for Integer Programming: Learning to Cut](https://mlsanity.com/paper/1906.04859/) (2019)
- [Adaptive Cut Selection in Mixed-Integer Linear Programming](https://mlsanity.com/paper/2202.10962/) (2022)
- [Machine Learning for Cutting Planes in Integer Programming: A Survey](https://mlsanity.com/paper/2302.09166/) (2023)
- [Enabling Research through the SCIP Optimization Suite 8.0](https://mlsanity.com/paper/2303.07101/) (2023)
- [Improved Sample Complexity Bounds for Branch-and-Cut](https://mlsanity.com/paper/2111.11207/) (2021)
- [Cutting Plane Selection with Analytic Centers and Multiregression](https://mlsanity.com/paper/2212.07231/) (2022)
- [Branching via Cutting Plane Selection: Improving Hybrid Branching](https://mlsanity.com/paper/2306.06050/) (2023)

## Abstract (arXiv)

The current cut selection algorithm used in mixed-integer programming solvers has remained largely unchanged since its creation. In this paper, we propose a set of new cut scoring measures, cut filtering techniques, and stopping criteria, extending the current state-of-the-art algorithm and obtaining a 5% performance improvement for SCIP over the MIPLIB 2017 benchmark set.