A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 14 Jul 2023.
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8 of 22 references.
References · 8
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
- 2019Reinforcement Learning for Integer Programming: Learning to Cutcited by 108
- 2022Adaptive Cut Selection in Mixed-Integer Linear Programmingcited by 25
- 2023Machine Learning for Cutting Planes in Integer Programming: A Surveycited by 19
- 2021Improved Sample Complexity Bounds for Branch-and-Cutcited by 13
- 2023Enabling Research through the SCIP Optimization Suite 8.0cited by 13
- 2022Cutting Plane Selection with Analytic Centers and Multiregressioncited by 4
- 2023Branching via Cutting Plane Selection: Improving Hybrid Branchingcited by 1
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