mlsanityData to 14 Oct 2026

Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming

arXiv 2401.17527 ↗First posted 31 Jan 2024cs.AIcited by 8

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Abstract (arXiv)

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), as they significantly tighten the dual bounds and improve the solving performance. A key problem for cuts is when to stop cuts generation, which is important for the efficiency of solving MILPs. However, many modern MILP solvers employ hard-coded heuristics to tackle this problem, which tends to neglect underlying patterns among MILPs from certain applications. To address this challenge, we formulate the cuts generation stopping problem as a reinforcement learning problem and propose a novel hybrid graph representation model (HYGRO) to learn effective stopping strategies. An appealing feature of HYGRO is that it can effectively capture both the dynamic and static features of MILPs, enabling dynamic decision-making for the stopping strategies. To the best of our knowledge, HYGRO is the first data-driven method to tackle the cuts generation stopping problem. By integrating our approach with modern solvers, experiments demonstrate that HYGRO significantly improves the efficiency of solving MILPs compared to competitive baselines, achieving up to 31% improvement.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 29 of its 55 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

29 of 55 references.

References · 29

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17 more
  1. 2022Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learningcited by 61
  2. 2021Learning to Select Cuts for Efficient Mixed-Integer Programmingcited by 48
  3. 2023Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Modelcited by 45
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  6. 2022Automated Dynamic Algorithm Configurationcited by 39
  7. 2021Sample Complexity of Tree Search Configuration: Cutting Planes and Beyondcited by 33
  8. 2022Learning to Compare Nodes in Branch and Bound with Graph Neural Networkscited by 31
  9. 2022Lookback for Learning to Branchcited by 27
  10. 2022Adaptive Cut Selection in Mixed-Integer Linear Programmingcited by 26
  11. 2021Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Criticcited by 23
  12. 2021Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimizationcited by 18
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  14. 2023Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractionscited by 14
  15. 2019Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimizationcited by 11
  16. 2022Learning to Use Local Cutscited by 11
  17. 2022Efficient Exploration in Resource-Restricted Reinforcement Learningcited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.

  1. 2024Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learningcited by 17
  2. 2025Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programmingcited by 16
  3. 2025CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selectioncited by 8
  4. 2025Accurate and Scalable Graph Neural Networks via Message Invariancecited by 3
  5. 2025How hard is learning to cut? Trade-offs and sample complexitycited by 1
  6. 2026A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attentioncited by 1
  7. 2025FourierCSP: Differentiable Constraint Satisfaction Problem Solving by Walsh-Fourier Expansioncited by 0
  8. 2026SRG: Score-based Relaxation-guided Generation for Mixed Integer Linear Programmingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Beyond Local Selection: Global Cut Selection for Enhanced Mixed-Integer Programming
  2. 2025Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks
  3. 2020Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
  4. 2022Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
  5. 2025Learning Branching Policies for MILPs with Proximal Policy Optimization
  6. 2025Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
  7. 2025HyP-ASO: A Hybrid Policy-based Adaptive Search Optimization Framework for Large-Scale Integer Linear Programs
  8. 2026Learning to Cut: Reinforcement Learning for Benders Decomposition