Nam Le

Mixed Integer Programming (MIP)

Nam Le
Table of Contents

Mixed Integer Programming (MIP) #

Mixed Integer Programming is a fundamental optimization framework widely used in operations research. Machine learning approaches are being applied to improve MIP solvers.

Recent Literature #

  1. Sequential model-based optimization for general algorithm configuration International conference on learning and intelligent optimization, 2011. journal

    Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin

  2. Non-model-based Search Guidance for Set Partitioning Problems AAAI, 2012. paper

    Kadioglu, Serdar and Malitsky, Yuri and Sellmann, Meinolf

  3. A Aupervised Machine Learning Approach to Variable Branching in Branch-and-bound Citeseer, 2014. journal

    Alvarez, Alejandro Marcos and Louveaux, Quentin and Wehenkel, Louis

  4. Learning to Search in Branch-and-Bound Algorithms NeurIPS, 2014. paper

    He, He and Daume III, Hal and Eisner, Jason M

  5. Learningto Branch in Mixed Integer Programming AAAI, 2016. paper

    E. B. Khalil, P. L. Bodic, L. Song, G. Nemhauser, B. Dilkina

  6. Dash: Dynamic Approach for Switching Heuristics European Journal of Operational Research, 2016. journal

    Di Liberto, Giovanni and Kadioglu, Serdar and Leo, Kevin and Malitsky, Yuri

  7. Learning When to Use a Decomposition International conference on AI and OR techniques in constraint programming for combinatorial optimization problems, 2017. journal

    Kruber, Markus and L{\u}bbecke Marco E and Parmentier Axel"

  8. Learning to Run Heuristics in Tree Search IJCAI, 2017. paper

    Khalil, Elias B and Dilkina, Bistra and Nemhauser, George L and Ahmed, Shabbir and Shao, Yufen

  9. Exact Combinatorial Optimization with Graph Convolutional Neural Networks NeurIPS, 2019. paper, code

    Gasse, Maxime and Chetelat, Didier and Ferroni, Nicola and Charlin, Laurent and Lodi, Andrea

  10. Improving Learning to Branch via Reinforcement Learning NeurIPS Workshop, 2020. paper

    Sun, Haoran and Chen, Wenbo and Li, Hui and Song, Le.

  11. Reinforcement learning for variable selection in a branch and bound algorithm International Conference on Integration of Constraint Programming, 2020. journal

    Etheve, Marc and Al{`e}s, Zacharie and Bissuel, C{^o}me and Juan, Olivier and Kedad-Sidhoum, Safia

  12. Random sampling and machine learning to understand good decompositions Annals of Operations Research, 2020. journal

    Basso, Saverio and Ceselli, Alberto and Tettamanzi, Andrea

  13. Hybrid Models for Learning to Branch NeurIPS, 2020. paper, code

    Gupta, Prateek and Gasse, Maxime and Khalil, Elias B and Kumar, M Pawan and Lodi, Andrea and Bengio, Yoshua

  14. Reinforcement Learning for Integer Programming: Learning to Cut ICML, 2020. paper

    Tang, Yunhao and Agrawal, Shipra and Faenza, Yuri

  15. Solving Mixed Integer Programs Using Neural Networks Arxiv, 2020. paper

    Nair, Vinod and Bartunov, Sergey and Gimeno, Felix and von Glehn, Ingrid and Lichocki, Pawel and Lobov, Ivan and O’Donoghue, Brendan and Sonnerat, Nicolas and Tjandraatmadja, Christian and Wang, Pengming and others

  16. Learning Efficient Search Approximation in Mixed Integer Branch and Bound Arxiv, 2020. paper

    Yilmaz, Kaan and Yorke-Smith, Neil

  17. Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs Arxiv, 2020. paper

    Sonnerat, Nicolas and Wang, Pengming and Ktena, Ira and Bartunov, Sergey and Nair, Vinod

  18. A General Large Neighborhood Search Framework for Solving Integer Linear Programs NeurIPS, 2020. paper

    Song, Jialin and Lanka, Ravi and Yue, Yisong and Dilkina, Bistra

  19. Neural Large Neighborhood Search NeurIPS Workshop, 2020. paper

    Nair, Vinod and Alizadeh, Mohammad and others

  20. Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction AAAI, 2020. paper

    Ding, Jian-Ya, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, and Le Song

  21. CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints Arxiv, 2021. paper, code

    Paulus, Anselm and Rolinek, Michal and Musil, Vit and Amos, Brandon and Martius, Georg

  22. Reinforcement Learning for (Mixed) Integer Programming: Smart Feasibility Pump ICML Workshop, 2021. paper

    Qi, Meng and Wang, Mengxin and Shen, Zuo-Jun

  23. Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies AAAI, 2021. paper, code

    Zarpellon, Giulia and Jo, Jason and Lodi, Andrea and Bengio, Yoshua

  24. Learning to Select Cuts for Efficient Mixed-Integer Programming Arxiv, 2021. journal

    Huang, Zeren and Wang, Kerong and Liu, Furui and Zhen, Hui-ling and Zhang, Weinan and Yuan, Mingxuan and Hao, Jianye and Yu, Yong and Wang, Jun

  25. Confidence Threshold Neural Diving NeurIPS ML4CO Competition Workshop, 2021. paper

    Taehyun Yoon

  26. Learning large neighborhood search policy for integer programming NeurIPS, 2021. paper

    Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie

  27. Generative Deep Learning for Decision Making in Gas Networks Arxiv, 2021. paper

    Lovis Anderson and Mark Turner and Thorsten Koch

  28. Offline Constraint Screening for Online Mixed-integer Optimization Arxiv, 2021. paper

    Asunción Jiménez-Cordero and Juan Miguel Morales and Salvador Pineda

  29. Mixed Integer Programming versus Evolutionary Computation for Optimizing a Hard Real-World Staff Assignment Problem ICAPS, 2021. paper

    Peters, Jannik and Stephan, Daniel and Amon, Isabel and Gawendowicz, Hans and Lischeid, Julius and Salabarria, Lennart and Umland, Jonas and Werner, Felix and Krejca, Martin S and Rothenberger, Ralf and others

  30. Learning To Scale Mixed-Integer Programs AAAI, 2021. paper

    Berthold, Timo, and Gregor Hendel

  31. Learning Pseudo-Backdoors for Mixed Integer Programs AAAI, 2021. paper

    Aaron Ferber and Jialin Song and Bistra Dilkina and Yisong Yue

  32. Learning Primal Heuristics for Mixed Integer Programs IJCNN, 2021. paper

    Shen, Yunzhuang and Sun, Yuan and Eberhard, Andrew and Li, Xiaodong

  33. Learning to Solve Large-scale Security-constrained Unit Commitment Problems INFORMS Journal on Computing, 2021. journal

    Xavier, {'A}linson S and Qiu, Feng and Ahmed, Shabbir

  34. Learning to Branch with Tree MDPs Arxiv, 2022. paper, code

    Scavuzzo, Lara, F. Chen, Didier Ch’etelat, Maxime Gasse, Andrea Lodi, N. Yorke-Smith and Karen Aardal.

  35. A Deep Reinforcement Learning Framework For Column Generation Arxiv, 2022. paper

    Chi, Cheng, Amine Mohamed Aboussalah, Elias Boutros Khalil, Juyoung Wang and Zoha Sherkat-Masoumi.

  36. Ranking Constraint Relaxations for Mixed Integer Programs Using a Machine Learning Approach Arxiv, 2022. journal

    Weiner, Jake, Andreas T. Ernst, Xiaodong Li and Yuan Sun.

  37. Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing Arxiv, 2022. journal, code

    Li, Longkang and Baoyuan Wu.

  38. Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning ICML, 2022. paper

    Paulus, Max B., Giulia Zarpellon, Andreas Krause, Laurent Charlin and Chris J. Maddison.

  39. Lookback for Learning to Branch Arxiv, 2022. journal

    Gupta, Prateek, Elias Boutros Khalil, Didier Chet’elat, Maxime Gasse, Yoshua Bengio, Andrea Lodi and M. Pawan Kumar.

  40. Learning to Search in Local Branching AAAI, 2022. paper, code

    Liu, Defeng and Fischetti, Matteo and Lodi, Andrea

  41. Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Arxiv, 2022. paper

    Zhang, Tianyu and Banitalebi-Dehkordi, Amin and Zhang, Yong

  42. Learning to Branch with Tree-aware Branching Transformers Knowledge-Based Systems, 2022. journal, code

    Lin, Jiacheng and Zhu, Jialin and Wang, Huangang and Zhang, Tao

  43. An Improved Reinforcement Learning Algorithm for Learning to Branch Arxiv, 2022. paper

    Qu, Qingyu and Li, Xijun and Zhou, Yunfan and Zeng, Jia and Yuan, Mingxuan and Wang, Jie and Lv, Jinhu and Liu, Kexin and Mao, Kun

  44. Learning to Use Local Cuts Arxiv, 2022. paper

    Berthold, Timo and Francobaldi, Matteo and Hendel, Gregor

  45. DOGE-Train: Discrete Optimization on GPU with End-to-end Training Arxiv, 2022. paper

    Abbas, Ahmed and Swoboda, Paul

  46. Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts NeurIPS, 2022. paper

    Balcan, Maria-Florina and Prasad, Siddharth and Sandholm, Tuomas and Vitercik, Ellen

  47. Constrained Discrete Black-Box Optimization using Mixed-Integer Programming ICML, 2022. paper

    Papalexopoulos, Theodore, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma and Daving Belanger.

  48. A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming ICLR, 2023. paper, code

    Han, Qingyu and Yang, Linxin and Chen, Qian and Zhou, Xiang and Zhang, Dong and Wang, Akang and Sun, Ruoyu and Luo, Xiaodong

  49. Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model ICLR, 2023. paper, code

    Wang, Zhihai and Li, Xijun and Wang, Jie and Kuang, Yufei and Yuan, Mingxuan and Zeng, Jia and Zhang, Yongdong and Wu, Feng

  50. On Representing Mixed-Integer Linear Programs by Graph Neural Networks ICLR, 2023. paper, code

    Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin

  51. GNN-GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming ICML, 2023. paper, code

    Huigen Ye, Hua Xu, Hongyan Wang, Chengming Wang, Yu Jiang

  52. Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning ICML, 2023. paper, code

    Taoan Huang, Aaron M Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner

  53. Learning to Configure Separators in Branch-and-Cut NeurIPS, 2023. paper

    Li, Sirui and Ouyang, Wenbin and Paulus, Max B and Wu, Cathy

  54. Learning to Dive in Branch and Bound NeurIPS, 2023. paper

    Paulus, Max B and Krause, Andreas

  55. A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability NeurIPS, 2023. paper, code

    Geng, Zijie and Li, Xijun and Wang, Jie and Li, Xiao and Zhang, Yongdong and Wu, Feng

  56. Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization JAIR, 2024. journal, code

    Canturk, Furkan and Varol, Taha and Aydogan, Reyhan and Ozener, Okan O

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