mlsanityData to 7 Oct 2026

A Survey for Solving Mixed Integer Programming via Machine Learning

arXiv 2203.02878 ↗First posted 6 Mar 2022cs.AIcited by 44

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Abstract (arXiv)

This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem, and most of the combinatorial optimization (CO) problems can be formulated as the MIP. Like other CO problems, the human-designed heuristic algorithms for MIP rely on good initial solutions and cost a lot of computational resources. Therefore, we consider applying machine learning methods to solve MIP, since ML-enhanced approaches can provide the solution based on the typical patterns from the historical data. In this paper, we first introduce the formulation and preliminaries of MIP and several traditional algorithms to solve MIP. Then, we advocate further promoting the different integration of machine learning and MIP and introducing related learning-based methods, which can be classified into exact algorithms and heuristic algorithms. Finally, we propose the outlook for learning-based MIP solvers, direction towards more combinatorial optimization problems beyond MIP, and also the mutual embrace of traditional solvers and machine learning components.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 26 of its 85 references that are in the map. First posted on arXiv 6 Mar 2022.

Builds on

26 of 85 references.

References · 26

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 612
  3. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 313
  4. 2018Learning to Perform Local Rewriting for Combinatorial Optimizationcited by 183
  5. 2020Solving Mixed Integer Programs Using Neural Networkscited by 178
  6. 2020Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instancescited by 161
  7. 2019Reinforcement Learning for Integer Programming: Learning to Cutcited by 108
  8. 2020Hybrid Models for Learning to Branchcited by 95
  9. 2019Exploratory Combinatorial Optimization with Reinforcement Learningcited by 82
  10. 2018Learning to Branchcited by 78
  11. 2020Parameterizing Branch-and-Bound Search Trees to Learn Branching Policiescited by 62
  12. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 50
14 more
  1. 2021CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraintscited by 49
  2. 2021Learning to Select Cuts for Efficient Mixed-Integer Programmingcited by 48
  3. 2020Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solverscited by 43
  4. 2021Learning a Large Neighborhood Search Algorithm for Mixed Integer Programscited by 41
  5. 2019Learning to Solve Large-Scale Security-Constrained Unit Commitment Problemscited by 38
  6. 2021Learning Large Neighborhood Search Policy for Integer Programmingcited by 35
  7. 2018Best arm identification in multi-armed bandits with delayed feedbackcited by 29
  8. 2020Reinforcement Learning for Variable Selection in a Branch and Bound Algorithmcited by 28
  9. 2021Learning Primal Heuristics for Mixed Integer Programscited by 14
  10. 2013DASH: Dynamic Approach for Switching Heuristicscited by 13
  11. 2021Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trendscited by 9
  12. 2021Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programmingcited by 8
  13. 2019Co-training for Policy Learningcited by 6
  14. 2019Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Predictioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 9; 2024: 17; 2025: 12; 2026: 4.

44 papers.

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  5. 2023Learning To Dive In Branch And Boundcited by 10
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  12. 2023Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problemcited by 1
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