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Awesome Machine Learning for Combinatorial Optimization Resources

We would like to maintain a list of resources that utilize machine learning technologies to solve combinatorial optimization problems.

We mark work contributed by Thinklab with ⭐.

Maintained by members in SJTU-Thinklab: Chang Liu, Runzhong Wang, Jiayi Zhang, Zelin Zhao, Haoyu Geng, Tianzhe Wang, Wenxuan Guo, Wenjie Wu and Junchi Yan. We also thank all contributers from the community!

We are looking for post-docs interested in machine learning especially for learning combinatorial solvers, dynamic graphs, and reinforcement learning. Please send your up-to-date resume via yanjunchi AT sjtu.edu.cn.

Content

1. Survey
2. Problems
 2.1 Graph Matching (GM)  2.2 Quadratic Assignment Problem (QAP)
 2.3 Travelling Salesman Problem (TSP)  2.4 Maximal Cut
 2.5 Differentiable Optimization  2.6 Vehicle Routing Problem (VRP)
 2.7 Job Shop Scheduling Problem (JSSP)  2.8 Maximal/Maximum Independent Set
 2.9 Computing Resource Allocation  2.10 Bin Packing Problem (BPP)
 2.11 Graph Edit Distance (GED)  2.12 Hamiltonian Cycle Problem (HCP)
 2.13 Graph Coloring  2.14 Maximal Common Subgraph (MCS)
 2.15 Influence Maximization  2.16 Boolean Satisfiability (SAT)
 2.17 Mixed Integer Programming  2.18 Causal Discovery
 2.19 Game Theoretic Semantics  2.20 Car Dispatch
 2.21 Electronic Design Automation (EDA)  2.22 Generalization
 2.23 Conjunctive Query Containment  2.24 Orienteering Problem (OP)
 2.25 Virtual Network Embedding (VNE)  2.26 Optical Power Flow (OPF)
 2.27 Facility Location Problem (FLP)  2.28 Portfolio Optimization (PortOpt)
 2.29 Sorting & Ranking (Sort&Rank)  

Survey Papers

  1. Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research INFORMS Journal on Computing, 1999. journal

    Smith, Kate A.

  2. Model-Based Search for Combinatorial Optimization: A Critical Survey. Annals of Operations Research, 2004. journal

    Zlochin, Mark and Birattari, Mauro and Meuleau, Nicolas and Dorigo, Marco.

  3. A Survey of Reinforcement Learning and Agent-Based Approaches to Combinatorial Optimization. Citeseer, 2012. journal

    Miagkikh, Victor

  4. Machine Learning Approaches to Learning Heuristics for Combinatorial Optimization Problems. Procedia Manufacturing, 2018. journal

    Mirshekarian, Sadegh and Sormaz, Dusan.

  5. Boosting combinatorial problem modeling with machine learning. IJCAI, 2018. paper

    Lombardi, Michele and Milano, Michela.

  6. Deep Reinforcement Learning as a Job Shop Scheduling Solver: A Literature Review Hybrid Intelligent Systems, 2018. journal

    Bruno Cunha, Ana M. Madureira, Benjamim Fonseca, Duarte Coelho

  7. A Review of combinatorial optimization with graph neural networks. BigDIA, 2019. paper

    Huang, Tingfei and Ma, Yang and Zhou, Yuzhen and Huang, Honglan Huang and Chen, Dongmei and Gong, Zidan and Liu, Yao.

  8. Machine Learning for Combinatorial Optimization: a Methodological Tour d'horizon. EJOR, 2020. journal

    Bengio, Yoshua and Lodi, Andrea and Prouvost, Antoine.

  9. Reinforcement Learning for Combinatorial Optimization: A Survey. Arxiv, 2020. paper

    Mazyavkina, Nina and Sviridov, Sergey and Ivanov, Sergei and Burnaev, Evgeny.

  10. ⭐Learning Graph Matching and Related Combinatorial Optimization Problems. IJCAI, 2020. paper

    Yan, Junchi and Yang, Shuang, and Hancock, Edwin R.

  11. Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking. IEEE ACCESS, 2020. journal

    Vesselinova, Natalia and Steinert, Rebecca and Perez-Ramirez, Daniel F. and Boman, Magnus.

  12. From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning. Arxiv, 2020. paper

    Bouraoui, Zied and Cornuéjols, Antoine and Denœux, Thierry and Destercke, Sébastien and Dubois, Didier and Guillaume, Romain and Marques-Silva, João and Mengin, Jérôme and Prade, Henri and Schockaert, Steven and Serrurier, Mathieu and Vrain, Christel.

  13. A Survey on Reinforcement Learning for Combinatorial Optimization. Arxiv, 2020. paper

    Yang, Yunhao and Whinston, Andrew.

  14. Research Reviews of Combinatorial Optimization Methods Based on Deep Reinforcement Learning. (in chinese) 自动化学报, 2020. journal

    Li, Kai-Wen and Zhang, Tao and Wang, Rui and Qin, Wei-Jian and He, Hui-Hui and Huang, Hong.

  15. Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art. Data Science and Engineering, 2021. journal

    Peng, Yue, Choi, Byron, and Xu, Jianliang.

  16. Combinatorial Optimization and Reasoning with Graph Neural Networks Arxiv, 2021. paper

    Cappart, Quentin and Chetelat, Didier and Khalil, Elias and Lodi, Andrea and Morris, Christopher and Velickovic, Petar

  17. Machine Learning for Electronic Design Automation (EDA) : A Survey TODAES, 2021. journal

    Huang, Guyue and Hu, Jingbo and He, Yifan and Liu, Jialong and Ma, Mingyuan and Shen, Zhaoyang and Wu, Juejian and Xu, Yuanfan and Zhang, Hengrui and Zhong, Kai and others

  18. ⭐A Survey for Solving Mixed Integer Programming via Machine Learning Neurocomputing, 2022. journal

    Jiayi Zhang and Chang Liu and Xijun Li and Hui-Ling Zhen and Mingxuan Yuan and Yawen Li and Junchi Yan

Problems

Graph Matching

  1. Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks Arxiv, 2017. paper, code

    Nowak, Alex and Villar, Soledad and Bandeira, S. Afonso and Bruna, Joan

  2. Deep Learning of Graph Matching. CVPR, 2018. paper

    Zanfir, Andrei and Sminchisescu, Cristian

  3. ⭐Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV, 2019. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  4. Deep Graphical Feature Learning for the Feature Matching Problem. ICCV, 2019. paper

    Zhang, Zhen and Lee, Wee Sun

  5. GLMNet: Graph Learning-Matching Networks for Feature Matching. Arxiv, 2019. paper

    Jiang, Bo and Sun, Pengfei and Tang, Jin and Luo, Bin

  6. ⭐Learning deep graph matching with channel-independent embedding and Hungarian attention. ICLR, 2020. paper, code

    Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin

  7. Deep Graph Matching Consensus. ICLR, 2020. paper

    Fey, Matthias and Lenssen, Jan E. and Morris, Christopher and Masci, Jonathan and Kriege, Nils M.

  8. ⭐Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning. NeurIPS, 2020. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  9. ⭐Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach. TPAMI, 2020. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  10. Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers. ECCV, 2020. paper, code

    Rolinek, Michal and Swoboda, Paul and Zietlow, Dominik and Paulus, Anselm and Musil, Vit and Martius, Georg

  11. ⭐Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. TPAMI, 2021. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  12. ⭐Deep Latent Graph Matching ICML, 2021. paper

    Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin.

  13. IA-GM: A Deep Bidirectional Learning Method for Graph Matching AAAI, 2021. paper

    Zhao, Kaixuan and Tu, Shikui and Xu, Lei

  14. Deep Graph Matching under Quadratic Constraint CVPR, 2021. paper

    Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song

  15. GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network MM, 2021. paper

    Jiang, Bo and Sun, Pengfei and Zhang, Ziyan and Tang, Jin and Luo, Bin

  16. Hypergraph Neural Networks for Hypergraph Matching ICCV, 2021. paper

    Liao, Xiaowei and Xu, Yong and Ling, Haibin

  17. Learning to Match Features with Seeded Graph Matching Network ICCV, 2021. paper

    Chen, Hongkai and Luo, Zixin and Zhang, Jiahui and Zhou, Lei and Bai, Xuyang and Hu, Zeyu and Tai, Chiew-Lan and Quan, Long

  18. ⭐Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond CVPR, 2022. paper, code

    Ren, Qibing and Bao, Qingquan and Wang, Runzhong and Yan, Junchi

  19. ⭐Self-supervised Learning of Visual Graph Matching ECCV, 2022. paper, code

    Liu, Chang and Zhang, Shaofeng and Yang, Xiaokang and Yan, Junchi

  20. ⭐Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. ICLR, 2023. paper, code

    Liu, Chang and Jiang, Zetian and Wang, Runzhong and Yan, Junchi and Huang, Lingxiao and Lu, Pinyan

Quadratic Assignment Problem

  1. Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks Arxiv, 2017. paper, code

    Nowak, Alex and Villar, Soledad and Bandeira, S. Afonso and Bruna, Joan

  2. ⭐Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. TPAMI, 2021. paper, code

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

  3. ⭐Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. ICLR, 2023. paper, code

    Liu, Chang and Jiang, Zetian and Wang, Runzhong and Yan, Junchi and Huang, Lingxiao and Lu, Pinyan

Travelling Salesman Problem

  1. Learning Combinatorial Optimization Algorithms over Graphs. NeurIPS, 2017. paper

    Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le

  2. Learning Heuristics for the TSP by Policy Gradient CPAIOR, 2018. paper, code

    Michel DeudonPierre CournutAlexandre Lacoste

  3. Attention, Learn to Solve Routing Problems! ICLR, 2019. paper

    Kool, Wouter and Van Hoof, Herke and Welling, Max.

  4. Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP. AAAI, 2019. paper

    Prates, Marcelo and Avelar, Pedro HC and Lemos, Henrique and Lamb, Luis C and Vardi, Moshe Y.

  5. An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem Arxiv, 2019. paper, code

    Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

  6. POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. NeurIPS, 2020. paper, code

    Kwon, Yeong-Dae and Choo, Jinho and Kim, Byoungjip and Yoon, Iljoo and Min, Seungjai and Gwon, Youngjune.

  7. Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances. Arxiv, 2020. paper

    Fu, Zhang-Hua and Qiu, Kai-Bin and Zha, Hongyuan.

  8. A Reinforcement Learning Approach for Optimizing Multiple Traveling Salesman Problems over Graphs KBS, 2020. journal

    Hu, Yujiao and Yao, Yuan and Lee, Wee Sun

  9. Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning ACML, 2020. paper, code

    d O Costa, Paulo R and Rhuggenaath, Jason and Zhang, Yingqian and Akcay, Alp

  10. Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems. IEEE Trans Cybern, 2021. journal

    Kaiwen Li, Tao Zhang, Rui Wang Yuheng Wang, and Yi Han

  11. The Transformer Network for the Traveling Salesman Problem IPAM, 2021. paper

    Xavier Bresson，Thomas Laurent

  12. Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal

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

  13. Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper

    Yao, Fan and Cai, Renqin and Wang, Hongning

  14. Solving Dynamic Traveling Salesman Problems with Deep Reinforcement Learning. TNNLS, 2021. journal

    Zizhen Zhang, Hong Liu, Meng Chu Zhou, Jiahai Wang

  15. ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper

    Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

  16. DAN: Decentralized Attention-based Neural Network to Solve the MinMax Multiple Traveling Salesman Problem Arxiv, 2021. paper

    Cao, Yuhong and Sun, Zhanhong and Sartoretti, Guillaume

  17. Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper

    Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin

  18. Learning TSP Requires Rethinking Generalization CP, 2021. paper, code

    Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau and Thomas Laurent

  19. The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems Arxiv, 2022. paper, code

    Bliek, Laurens and da Costa, Paulo and Afshar, Reza Refaei and Zhang, Yingqian and Catshoek, Tom and Vos, Daniel and Verwer, Sicco and Schmitt-Ulms, Fynn and Hottung, Andre and Shah, Tapan and others

  20. Graph Neural Network Guided Local Search for the Traveling Salesperson Problem ICLR, 2022. paper

    Hudson, Benjamin and Li, Qingbiao and Malencia, Matthew and Prorok, Amanda

  21. Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper

    Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu

  22. Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation NeurIPS, 2022. paper, code

    Bi, Jieyi and Ma, Yining and Wang, Jiahai and Cao, Zhiguang and Chen, Jinbiao and Sun, Yuan and Chee, Yeow Meng

  23. DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems NeurIPS, 2022. paper

    Qiu, Ruizhong and Sun, Zhiqing and Yang, Yiming

  24. Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Kim, Minsu and Park, Junyoung and Park, Jinkyoo

  25. Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Choo, Jinho and Kwon, Yeong-Dae and Kim, Jihoon and Jae, Jeongwoo and Hottung, Andr{'e} and Tierney, Kevin and Gwon, Youngjune

  26. Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR, 2022. paper

    Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann

  27. Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper

    Kim, Minjun and Park, Junyoung and Park, Jinkyoo

  28. Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time ICLR, 2023. paper

    Hou, Qingchun and Yang, Jingwei and Su, Yiqiang and Wang, Xiaoqing and Deng, Yuming

  29. ⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code

    Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi

Maximal Cut

  1. Learning Combinatorial Optimization Algorithms over Graphs. NeurIPS, 2017. paper

    Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le

  2. Exploratory Combinatorial Optimization with Reinforcement Learning. AAAI, 2020. paper

    LBarrett, Thomas and Clements, William and Foerster, Jakob and Lvovsky, Alex.

  3. Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS, 2020. paper

    Karalias, Nikolaos and Loukas, Andreas

  4. Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper

    Yao, Fan and Cai, Renqin and Wang, Hongning

  5. LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code

    Ireland, David and G. Montana

  6. Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration Arxiv, 2022. paper, code

    Barrett, Thomas D and Parsonson, Christopher WF and Laterre, Alexandre

Differentiable Optimization

  1. Differentiable Learning of Submodular Models NeurIPS, 2017. paper, code

    Josip Djolonga, Andreas Krause

  2. OptNet: differentiable optimization as a layer in neural networks ICML, 2017. paper, code

    Brandon Amos and J. Zico Kolter

  3. Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization AAAI, 2019. paper

    Bryan Wilder, Bistra Dilkina, Milind Tambe

  4. Differentiable Convex Optimization Layers NeurIPS, 2019. paper, code

    Agrawal, Akshay and Boyd, Stephen

  5. Predict+optimise with ranking objectives: exhaustively learning linear functions IJCAI, 2019. paper

    Demirovic, Emir and Stuckey, Peter J. and Bailey, James and Chan, Jeffrey and Leckie, Christopher and Ramamohanarao, Kotagiri and Guns, Tias

  6. Differentiation of Blackbox Combinatorial Solvers ICLR, 2020. paper, code

    Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolínek

  7. MIPaaL: Mixed Integer Program as a Layer AAAI, 2020. paper, code

    Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe

  8. Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems AAAI, 2020. paper, code

    Jaynta Mandi, Emir Demirovi, Peter. J Stuckey, Tias Guns

  9. Differentiation of blackbox combinatorial solvers ICLR, 2020. paper, code

    Marin Vlastelica Pogani, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolinek

  10. Interior Point Solving for LP-based prediction+optimization NeurIPS, 2020. paper, code

    Jayanta Mandi, Tias Guns

  11. Automatically Learning Compact Quality-aware Surrogates for Optimization Problems NeurIPS, 2020. paper

    Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

  12. Contrastive Losses and Solution Caching for Predict-and-Optimize IJCAI, 2021. paper, code

    Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti , Michele Lombardi, Victor Bucarey, Tias Guns

  13. A Surrogate Objective Framework for Prediction+Programming with Soft Constraints NeurIPS, 2021. paper, code

    Kai Yan, Jie Yan, Chuan Luo, Liting Chen, Qingwei Lin, Dongmei Zhang

  14. Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions NeurIPS, 2021. paper, code

    Mathias Niepert, Pasquale Minervini, Luca Franceschi

  15. COPS: Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach NeurIPS, 2021. paper, code

    Ahmed Abbas, Paul Swoboda

  16. An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming ICML, 2022. paper, code

    Jeong, Jihwan, Parth Jaggi, Andrew Butler and Scott Sanner. “An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming.” ICML (2022).

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

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

  18. End-to-End Stochastic Optimization with Energy-Based Model NeurIPS, 2022. paper, code

    Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang

  19. Deep Declarative Networks TPAMI, 2022. paper, code

    Stephen Gould, Richard Hartley and Dylan Campbell

Vehicle Routing Problem

  1. Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS, 2019. paper, code

    Chen, Xinyun and Tian, Yuandong.

  2. Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows. Arxiv, 2020. paper

    Lin, Bo and Ghaddar, Bissan and Nathwani, Jatin.

  3. Efficiently Solving the Practical,Vehicle Routing Problem: A Novel Joint Learning Approach. KDD, 2020. paper

    Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, Yinghui Xu

  4. Efficiently Solving the Practical,Vehicle Routing Problem: A Novel Joint Learning Approach. KDD, 2020. paper

    Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, Yinghui Xu

  5. Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing NeurIPS, 2020. paper, code

    Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

  6. A Learning-based Iterative Method for Solving Vehicle Routing Problems ICLR, 2020. paper

    Lu, Hao and Zhang, Xingwen and Yang, Shuang

  7. Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem Arxiv, 2020. paper

    Hottung, Andre and Tierney, Kevin

  8. Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal

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

  9. Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper

    Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin

  10. Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. AAAI, 2021. paper, code

    Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

  11. Analytics and Machine Learning in Vehicle Routing Research Arxiv, 2021. paper

    Bai, Ruibin and Chen, Xinan and Chen, Zhi-Long and Cui, Tianxiang and Gong, Shuhui and He, Wentao and Jiang, Xiaoping and Jin, Huan and Jin, Jiahuan and Kendall, Graham and others

  12. RP-DQN: An application of Q-Learning to Vehicle Routing Problems Arxiv, 2021. paper

    Bdeir, Ahmad and Boeder, Simon and Dernedde, Tim and Tkachuk, Kirill and Falkner, Jonas K and Schmidt-Thieme, Lars

  13. Deep Policy Dynamic Programming for Vehicle Routing Problems Arxiv, 2021. paper

    Kool, Wouter and van Hoof, Herke and Gromicho, Joaquim and Welling, Max

  14. Learning to Delegate for Large-scale Vehicle Routing NeurIPS, 2021. paper

    Li, Sirui and Yan, Zhongxia and Wu, Cathy

  15. Learning a Latent Search Space for Routing Problems using Variational Autoencoders ICLR, 2021. paper

    Hottung, Andre and Bhandari, Bhanu and Tierney, Kevin

  16. Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper

    Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu

  17. Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation NeurIPS, 2022. paper, code

    Bi, Jieyi and Ma, Yining and Wang, Jiahai and Cao, Zhiguang and Chen, Jinbiao and Sun, Yuan and Chee, Yeow Meng

  18. Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Kim, Minsu and Park, Junyoung and Park, Jinkyoo

  19. Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Choo, Jinho and Kwon, Yeong-Dae and Kim, Jihoon and Jae, Jeongwoo and Hottung, Andr{'e} and Tierney, Kevin and Gwon, Youngjune

  20. Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper

    Kim, Minjun and Park, Junyoung and Park, Jinkyoo

  21. Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time ICLR, 2023. paper

    Hou, Qingchun and Yang, Jingwei and Su, Yiqiang and Wang, Xiaoqing and Deng, Yuming

Job Shop Scheduling Problem

  1. Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network Transactions on Industrial Informatics, 2019. journal

    Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih, Hsin-Ting Chiu

  2. Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems International Conference on Artificial Intelligence for Industries (AI4I), 2019. paper

    Schirin Baer, Jupiter Bakakeu, Richard Meyes, Tobias Meisen

  3. Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. NeurIPS, 2020. paper, code

    Zhang, Cong and Song, Wen and Cao, Zhiguang and Zhang, Jie and Tan, Puay Siew and Xu, Chi.

  4. ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper

    Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

  5. Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning Computer Networks, 2021. journal

    Libing Wang, Xin Hu, Yin Wang, Sujie Xu, Shijun Ma, Kexin Yang, Zhijun Liu, Weidong Wang

  6. Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research, 2021. journal

    Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, Jinkyoo Park

  7. Explainable reinforcement learning in production control of job shop manufacturing system. International Journal of Production Research, 2021. journal

    Andreas Kuhnle,Marvin Carl May,Louis Sch?fer & Gisela Lanza

  8. Neural DAG Scheduling via One-Shot Priority Sampling ICLR, 2023. paper

    Jeon, Wonseok and Gagrani, Mukul and Bartan, Burak and Zeng, Weiliang Will and Teague, Harris and Zappi, Piero and Lott, Christopher

  9. Robust Scheduling with GFlowNets ICLR, 2023. paper

    Zhang, David W and Rainone, Corrado and Peschl, Markus and Bondesan, Roberto

Maximal/Maximum Independent Set

  1. Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. NeurIPS, 2018. paper

    Li, Zhuwen and Chen, Qifeng and Koltun, Vladlen.

  2. Learning What to Defer for Maximum Independent Sets ICML, 2020. paper

    Ahn, Sungsoo and Seo, Younggyo and Shin, Jinwoo

  3. Distributed Scheduling Using Graph Neural Networks ICASSP, 2021. paper

    Zhao, Zhongyuan and Verma, Gunjan and Rao, Chirag and Swami, Ananthram and Segarra, Santiago

  4. Solving Graph-based Public Good Games with Tree Search and Imitation Learning NeurlPS, 2021. paper

    Darvariu, Victor-Alexandru and Hailes, Stephen and Musolesi, Mirco

  5. NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs NeurlPS, 2021. paper

    McCarty, Evan and Zhao, Qi and Sidiropoulos, Anastasios and Wang, Yusu

  6. What's Wrong with Deep Learning in Tree Search for Combinatorial Optimization ICLR, 2022. paper, code

    Bother, Maximilian and Kissig, Otto and Taraz, Martin and Cohen, Sarel and Seidel, Karen and Friedrich, Tobias

  7. Optimistic tree search strategies for black-box combinatorial optimization NeurlPS, 2022. paper

    Malherbe, Cedric and Grosnit, Antoine and Tutunov, Rasul and Ammar, Haitham Bou and Wang, Jun

  8. ⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code

    Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi

  9. Unsupervised Learning for Combinatorial Optimization Needs Meta Learning ICLR, 2023. paper, code

    Wang, Haoyu and Li, Pan

  10. Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems ICLR, 2023. paper, code

    Zhao, Zhongyuan and Swami, Ananthram and Segarra, Santiago

Computing Resource Allocation

  1. Resource Management with Deep Reinforcement Learning. HotNets, 2016. paper

    Mao, Hongzi and Alizadeh, Mohammad and Menache, Ishai and Kandula, Srikanth.

  2. Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS, 2019. paper, code

    Chen, Xinyun and Tian, Yuandong.

  3. Learning Scheduling Algorithms for Data Processing Clusters SIGCOMM, 2019. paper, code

    Mao, Hongzi and Schwarzkopf, Malte and Venkatakrishnan, Bojja Shaileshh and Meng, Zili and Alizadeh, Mohammad.

  4. Smart Resource Allocation for Mobile Edge Computing: A Deep Reinforcement Learning Approach IEEE Transactions on Emerging Topics in Computing, 2019. Paper

    Jiadai; Lei Zhao; Jiajia Liu; Nei Kato

  5. A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems Arxiv, 2021. paper

    He, Yongming and Wu, Guohua and Chen, Yingwu and Pedrycz, Witold

  6. ⭐A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs NeurIPS, 2021. paper, code

    Wang, Runzhong and Hua, Zhigang and Liu, Gan and Zhang, Jiayi and Yan, Junchi and Qi, Feng and Yang, Shuang and Zhou, Jun and Yang, Xiaokang

Bin Packing Problem

  1. Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing BigDataService, 2017. paper

    Mao, Feng and Blanco, Edgar and Fu, Mingang and Jain, Rohit and Gupta, Anurag and Mancel, Sebastien and Yuan, Rong and Guo, Stephen and Kumar, Sai and Tian, Yayang

  2. Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method Arxiv, 2017. paper

    Hu, Haoyuan and Zhang, Xiaodong and Yan, Xiaowei and Wang, Longfei and Xu, Yinghui

  3. Best Arm Identification in Multi-armed Bandits with Delayed Feedback PMLR, 2018. paper

    Grover, Aditya and Markov, Todor and Attia, Peter and Jin, Norman and Perkins, Nicolas and Cheong, Bryan and Chen, Michael and Yang, Zi and Harris, Stephen and Chueh, William and others

  4. Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization Alexandre Arxiv, 2018. paper

    Laterre, Alexandre and Fu, Yunguan and Jabri, Mohamed Khalil and Cohen, Alain-Sam and Kas, David and Hajjar, Karl and Dahl, Torbjorn S and Kerkeni, Amine and Beguir, Karim

  5. A Multi-task Selected Learning Approach for Solving 3D Bin Packing Problem. AAMAS, 2019. paper

    Duan, Lu and Hu, Haoyuan and Qian, Yu and Gong, Yu and Zhang, Xiaodong and Xu, Yinghui and Wei, Jiangwen.

  6. A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry KDD, 2019. paper

    Chen, Lei and Tong, Xialiang and Yuan, Mingxuan and Zeng, Jia and Chen, Lei

  7. Solving Packing Problems by Conditional Query Learning OpenReview, 2019. paper

    Li, Dongda and Ren, Changwei and Gu, Zhaoquan and Wang, Yuexuan and Lau, Francis

  8. RePack: Dense Object Packing Using Deep CNN with Reinforcement Learning CACS, 2019. paper

    Chu, Yu-Cheng and Lin, Horng-Horng

  9. Reinforcement learning driven heuristic optimization Arxiv, 2019. paper

    Cai, Qingpeng and Hang, Will and Mirhoseini, Azalia and Tucker, George and Wang, Jingtao and Wei, Wei

  10. A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing. AAAI Workshop, 2020. paper

    Verma, Richa and Singhal, Aniruddha and Khadilkar, Harshad and Basumatary, Ansuma and Nayak, Siddharth and Singh, Harsh Vardhan and Kumar, Swagat and Sinha, Rajesh.

  11. Robot Packing with Known Items and Nondeterministic Arrival Order. TASAE, 2020. paper

    Wang, Fan and Hauser, Kris.

  12. TAP-Net: Transport-and-Pack using Reinforcement Learning. TOG, 2020. paper, code

    Hu, Ruizhen and Xu, Juzhan and Chen, Bin and Gong, Minglun and Zhang, Hao and Huang, Hui.

  13. Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning IROS, 2020. paper

    Tanaka, Tatsuya and Kaneko, Toshimitsu and Sekine, Masahiro and Tangkaratt, Voot and Sugiyama, Masashi

  14. Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances GECCO, 2020. paper

    Pejic, Igor and van den Berg, Daan

  15. PackIt: A Virtual Environment for Geometric Planning ICML, 2020. paper, code

    Goyal, Ankit and Deng, Jia

  16. Online 3D Bin Packing with Constrained Deep Reinforcement Learning. AAAI, 2021. paper, code

    Zhao, Hang and She, Qijin and Zhu, Chenyang and Yang, Yin and Xu, Kai.

  17. Learning Practically Feasible Policies for Online 3D Bin Packing Arxiv, 2021. paper

    Hang Zhao and Chenyang Zhu and Xin Xu and Hui Huang and Kai Xu

  18. Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention ICML Workshop, 2021. paper

    Jingwei Zhang and Bin Zi and Xiaoyu Ge

  19. Solving 3D bin packing problem via multimodal deep reinforcement learning AAMAS, 2021. paper

    Jiang, Yuan, Zhiguang Cao, and Jie Zhang

  20. Learning to Solve 3-D Bin Packing Problem via Deep Reinforcement Learning and Constraint Programming IEEE transactions on cybernetics, 2021. paper

    Jiang, Yuan and Cao, Zhiguang and Zhang, Jie

  21. Learning to Pack: A Data-Driven Tree Search Algorithm for Large-Scale 3D Bin Packing Problem CIKM, 2021. paper

    Zhu, Qianwen and Li, Xihan and Zhang, Zihan and Luo, Zhixing and Tong, Xialiang and Yuan, Mingxuan and Zeng, Jia

  22. Learning Efficient Online 3D Bin Packing on Packing Configuration Trees. ICLR, 2022. paper

    Hang Zhao and Kai Xu

Graph Edit Distance

  1. SimGNN - A Neural Network Approach to Fast Graph Similarity Computation WSDM, 2019. paper, code

    Bai, Yunsheng and Ding, Hao and Bian, Song and Chen, Ting and Sun, Yizhou and Wang, Wei

  2. Graph Matching Networks for Learning the Similarity of Graph Structured Objects ICML, 2019. paper, code

    Li, Yujia and Gu, Chenjie and Dullien, Thomas and Vinyals, Oriol and Kohli, Pushmeet

  3. Convolutional Embedding for Edit Distance SIGIR, 2020. paper, code

    Dai, Xinyan and Yan, Xiao and Zhou, Kaiwen and Wang, Yuxuan and Yang, Han and Cheng, James

  4. Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching AAAI, 2020. paper, code

    Bai, Yunsheng and Ding, Hao and Gu, Ken and Sun, Yizhou and Wang, Wei

  5. ⭐A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs NeurIPS, 2021. paper, code

    Wang, Runzhong and Hua, Zhigang and Liu, Gan and Zhang, Jiayi and Yan, Junchi and Qi, Feng and Yang, Shuang and Zhou, Jun and Yang, Xiaokang

  6. ⭐Combinatorial Learning of Graph Edit Distance via Dynamic Embedding. CVPR, 2021. paper, code

    Wang, Runzhong and Zhang, Tianqi and Yu, Tianshu and Yan, Junchi and Yang, Xiaokang.

Hamiltonian Cycle Problem

  1. ⭐A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs NeurIPS, 2021. paper, code

    Wang, Runzhong and Hua, Zhigang and Liu, Gan and Zhang, Jiayi and Yan, Junchi and Qi, Feng and Yang, Shuang and Zhou, Jun and Yang, Xiaokang

Graph Coloring

  1. Deep Learning-based Hybrid Graph-Coloring Algorithm for Register Allocation. Arxiv, 2019. paper

    Das, Dibyendu and Ahmad, Shahid Asghar and Venkataramanan, Kumar.

  2. Neural Models for Output-Space Invariance in Combinatorial Problems ICLR, 2022. paper

    Nandwani, Yatin and Jain, Vidit and Singla, Parag and others

  3. Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring AAAI, 2022. paper, code

    Shen, Yunzhuang, Yuan Sun, Xiaodong Li, Andrew Craig Eberhard and Andreas T. Ernst.

  4. Learning to Generate Columns with Application to Vertex Coloring ICLR, 2023. paper, code

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

Maximal Common Subgraph

  1. Fast Detection of Maximum Common Subgraph via Deep Q-Learning. Arxiv, 2020. paper

    Bai, Yunsheng and Xu, Derek and Wang, Alex and Gu, Ken and Wu, Xueqing and Marinovic, Agustin and Ro, Christopher and Sun, Yizhou and Wang, Wei.

Influence Maximization

  1. Learning Heuristics over Large Graphs via Deep Reinforcement Learning. NeurIPS, 2020. paper

    Mittal, Akash and Dhawan, Anuj and Manchanda, Sahil and Medya, Sourav and Ranu, Sayan and Singh, Ambuj.

  2. Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks. ICML, 2021. paper

    Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

  3. LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code

    Ireland, David and G. Montana

  4. ⭐Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case ICLR, 2023. paper, code

    Wang, Runzhong and Shen, Li and Chen, Yiting and Yan, Junchi and Yang, Xiaokang and Tao, Dacheng

Boolean Satisfiability

  1. Graph neural networks and boolean satisfiability. Arxiv, 2017. paper

    Bünz, Benedikt, and Matthew Lamm.

  2. Learning a SAT solver from single-bit supervision. Arxiv, 2018. paper, code

    Selsam, Daniel, Matthew Lamm, Benedikt Bünz, Percy Liang, Leonardo de Moura, and David L. Dill.

  3. Machine learning-based restart policy for CDCL SAT solvers. SAT, 2018. paper

    Liang, Jia Hui, Chanseok Oh, Minu Mathew, Ciza Thomas, Chunxiao Li, and Vijay Ganesh.

  4. Learning to solve circuit-SAT: An unsupervised differentiable approach. ICLR, 2019. paper, code

    Amizadeh, Saeed, Sergiy Matusevych, and Markus Weimer.

  5. Learning Local Search Heuristics for Boolean Satisfiability. NeurIPS, 2019. paper, code

    Yolcu, Emre and Poczos, Barnabas

  6. Improving SAT solver heuristics with graph networks and reinforcement learning. Arxiv, 2019. paper

    Kurin, Vitaly, Saad Godil, Shimon Whiteson, and Bryan Catanzaro.

  7. Graph neural reasoning may fail in certifying boolean unsatisfiability. Arxiv, 2019. paper

    Chen, Ziliang, and Zhanfu Yang.

  8. Guiding high-performance SAT solvers with unsat-core predictions. SAT, 2019. paper

    Selsam, Daniel, and Nikolaj Bjørner.

  9. G2SAT: Learning to Generate SAT Formulas. NeurIPS, 2019. paper, code

    You, Jiaxuan, Haoze Wu, Clark Barrett, Raghuram Ramanujan, and Jure Leskovec.

  10. Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning. Arxiv, 2019. paper, code

    Lederman, Gil, Markus N. Rabe, Edward A. Lee, and Sanjit A. Seshia.

  11. Enhancing SAT solvers with glue variable predictions. Arxiv, 2020. paper

    Han, Jesse Michael.

  12. Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? NeurIPS, 2020. paper

    Whiteson, Shimon.

  13. Online Bayesian Moment Matching based SAT Solver Heuristics. ICML, 2020. paper, code

    Duan, Haonan, Saeed Nejati, George Trimponias, Pascal Poupart, and Vijay Ganesh.

  14. Learning Clause Deletion Heuristics with Reinforcement Learning. AITP, 2020. paper

    Vaezipoor, Pashootan, Gil Lederman, Yuhuai Wu, Roger Grosse, and Fahiem Bacchus.

  15. Classification of SAT problem instances by machine learning methods. CEUR, 2020. paper

    Danisovszky, Márk, Zijian Győző Yang, and Gábor Kusper.

  16. Predicting Propositional Satisfiability via End-to-End Learning. AAAI, 2020. paper

    Cameron, Chris, Rex Chen, Jason Hartford, and Kevin Leyton-Brown.

  17. Neural heuristics for SAT solving. Arxiv, 2020. paper

    Jaszczur, Sebastian, Michał Łuszczyk, and Henryk Michalewski.

  18. NLocalSAT: Boosting Local Search with Solution Prediction. Arxiv, 2020. paper, code

    Zhang, Wenjie, Zeyu Sun, Qihao Zhu, Ge Li, Shaowei Cai, Yingfei Xiong, and Lu Zhang.

  19. Optimistic tree search strategies for black-box combinatorial optimization NeurlPS, 2022. paper

    Malherbe, Cedric and Grosnit, Antoine and Tutunov, Rasul and Ammar, Haitham Bou and Wang, Jun

  20. Goal-Aware Neural SAT Solver. IJCNN, 2022. paper

    Ozolins, Emils, Karlis Freivalds, Andis Draguns, Eliza Gaile, Ronalds Zakovskis, and Sergejs Kozlovics.

  21. NeuroComb: Improving SAT Solving with Graph Neural Networks. Arxiv, 2022. paper

    Wang, Wenxi, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, and Risto Miikkulainen.

  22. On the Performance of Deep Generative Models of Realistic SAT Instances. SAT, 2022. paper

    Garzón, Iván, Pablo Mesejo, and Jesús Giráldez-Cru.

  23. DeepSAT: An EDA-Driven Learning Framework for SAT. Arxiv, 2022. paper

    Li, Min, Zhengyuan Shi, Qiuxia Lai, Sadaf Khan, and Qiang Xu.

  24. SATformer: Transformers for SAT Solving. Arxiv, 2022. paper

    Shi, Zhengyuan, Min Li, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, and Qiang Xu.

  25. Augment with Care: Contrastive Learning for Combinatorial Problems. ICML, 2022. paper, code

    Duan, Haonan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan and Chris J. Maddison

  26. NSNet: A General Neural Probabilistic Framework for Satisfiability Problems NeurIPS, 2022. paper

    Zhaoyu Li, Xujie Si

  27. Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions NeurIPS, 2022. paper

    Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka

  28. Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR, 2022. paper

    Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann

Mixed Integer Programming

  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 NeurlPS, 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 NeurlPS, 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 NeurlPS, 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 NeurlPS, 2020. paper

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

  19. Neural Large Neighborhood Search NeurlPS 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 NeurlPS, 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. Confidence Threshold Neural Diving NeurIPS ML4CO Competition Workshop, 2021. paper

    Taehyun Yoon

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

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

  34. 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

  35. 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.

  36. 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.

  37. 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.

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

    Li, Longkang and Baoyuan Wu.

  39. 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.

  40. 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.

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

    Liu, Defeng and Fischetti, Matteo and Lodi, Andrea

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

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

  43. 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

  44. 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

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

    Berthold, Timo and Francobaldi, Matteo and Hendel, Gregor

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

    Abbas, Ahmed and Swoboda, Paul

  47. 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

  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

Causal Discovery

  1. DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS, 2018. paper

    Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric.

  2. Causal Discovery with Reinforcement Learning. ICLR, 2020. paper

    Zhu, Shengyu and Ng, Ignavier and Chen, Zhitang.

  3. Large-Scale Differentiable Causal Discovery of Factor Graphs NeurIPS, 2022. paper, code

    Lopez, Romain and H{"u}tter, Jan-Christian and Pritchard, Jonathan K and Regev, Aviv

  4. Boosting Causal Discovery via Adaptive Sample Reweighting ICLR, 2023. paper, code

    Zhang, An and Liu, Fangfu and Ma, Wenchang and Cai, Zhibo and Wang, Xiang and Chua, Tat-seng

  5. CUTS: Neural Causal Discovery from Irregular Time-Series Data ICLR, 2023. paper, code

    Cheng, Yuxiao and Yang, Runzhao and Xiao, Tingxiong and Li, Zongren and Suo, Jinli and He, Kunlun and Dai, Qionghai

  6. Diffusion Models for Causal Discovery via Topological Ordering ICLR, 2023. paper, code

    Sanchez, Pedro and Liu, Xiao and O'Neil, Alison Q and Tsaftaris, Sotirios A

Game Theoretic Semantics

  1. First-Order Problem Solving through Neural MCTS based Reinforcement Learning. Arxiv, 2021. paper

    Xu, Ruiyang and Kadam, Prashank and Lieberherr, Karl.

Car Dispatch

  1. Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach Transportation Research, 2020. journal

    Chao Mao, Yulin Liu, Zuo-Jun (Max) Shen

Electronic Design Automation

  1. ⭐On Joint Learning for Solving Placement and Routing in Chip Design NeurIPS, 2021. paper, code

    Cheng, Ruoyu and Yan, Junchi

  2. A graph placement methodology for fast chip design Nature, 2021. journal

    Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter & Jeff Dean

  3. Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation NeurIPS, 2022. paper, code

    Wang, Haoyu Peter and Wu, Nan and Yang, Hang and Hao, Cong and Li, Pan

  4. CktGNN: Circuit Graph Neural Network for Electronic Design Automation ICLR, 2023. paper

    Dong, Zehao and Cao, Weidong and Zhang, Muhan and Tao, Dacheng and Chen, Yixin and Zhang, Xuan

Generalization

  1. It's Not What Machines Can Learn It's What We Cannot Teach ICML, 2020. paper

    Gal Yehuda, Moshe Gabel and Assaf Schuster

  2. Learning TSP Requires Rethinking Generalization CP, 2021. paper, code

    Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau and Thomas Laurent

  3. Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR, 2022. paper

    Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski and Stephan Günnemann

  4. Learning for Robust Combinatorial Optimization: Algorithm and Application INFOCOM, 2022. journal

    Shao, Zhihui and Yang, Jianyi and Shen, Cong and Ren, Shaolei

Conjunctive Query Containment

  1. It's Not What Machines Can Learn It's What We Cannot Teach ICML, 2020. paper

    Gal Yehuda, Moshe Gabel and Assaf Schuster

Orienteering Problem

  1. A reinforcement learning approach to the orienteering problem with time windows Computers & Operations Research, 2021. paper, code

    Ricardo Gama, Hugo L. Fernandes

Virtual Network Embedding

  1. Virtual Network Embedding via Monte Carlo Tree Search IEEE Trans. Cybern, 2017. journal

    Soroush Haeri; Ljiljana Trajković

  2. A novel reinforcement learning algorithm for virtual network embedding Neurocomputing, 2018. journal

    Haipeng Yao,Xu Chen, Maozhen Li, Peiying Zhang, Luyao Wang

  3. NeuroViNE: A Neural Preprocessor for Your Virtual Network Embedding Algorithm INFOCOM, 2018. paper

    Andreas Blenk; Patrick Kalmbach; Johannes Zerwas; Michael Jarschel; Stefan Schmid; Wolfgang Kellerer

  4. A Virtual Network Embedding Algorithm Based On Double-Layer Reinforcement Learning TCJ, 2019. journal

    Meng Li; MeiLian Lu

  5. NFVdeep: Adaptive Online Service Function Chain Deployment with Deep Reinforcement Learning IWQoS, 2019. paper

    Yikai Xiao; Qixia Zhang; Fangming Liu; Jia Wang; Miao Zhao; Zhongxing Zhang; Jiaxing Zhang

  6. A Continuous-Decision Virtual Network Embedding Scheme Relying on Reinforcement Learning IEEE TNSM, 2020. journal

    Haipeng Yao; Sihan Ma; Jingjing Wang; Peiying Zhang; Chunxiao Jiang; Song Guo

  7. Automatic Virtual Network Embedding A Deep Reinforcement Learning Approach With Graph Convolutional Networks J-SAC, 2020. journal

    Zhongxia Yan; Jingguo Ge; Yulei Wu; Liangxiong Li; Tong Li

  8. A Q-learning-based approach for virtual network embedding in data center NCA, 2020. journal

    Ying Yuan, Zejie Tian, Cong Wang, Fanghui Zheng & Yanxia Lv

  9. Accelerating Virtual Network Embedding with Graph Neural Networks CNSM, 2020. journal

    Farzad Habibi; Mahdi Dolati; Ahmad Khonsari; Majid Ghaderi

  10. Dynamic Virtual Network Embedding Algorithm Based on Graph Convolution Neural Network and Reinforcement Learning IoT-J, 2021. journal

    Peiying Zhang; Chao Wang; Neeraj Kumar; Weishan Zhang; Lei Liu

  11. Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks ICC, 2021. paper, code

    Fahri Wisnu Murti; Samad Ali; Matti Latva-aho

  12. DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning ICC, 2021. paper

    Tianfu Wang; Qilin Fan; Xiuhua Li; Xu Zhang; Qingyu Xiong; Shu Fu; Min Gao

Optical Power Flow

  1. Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods AAAI, 2020. paper

    Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck

  2. Adversarially Robust Learning for Security-Constrained Optimal Power Flow NeurIPS, 2021. paper

    Priya Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha, Larry Pileggi, J. Zico Kolter

Facility Location Problem

  1. Solving uncapacitated P-Median problem with reinforcement learning assisted by graph attention networks Applied Intelligence, 2023. paper

    Wang, Chenguang and Han, Congying and Guo, Tiande and Ding, Man

  2. ⭐Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case ICLR, 2023. paper, code

    Wang, Runzhong and Shen, Li and Chen, Yiting and Yan, Junchi and Yang, Xiaokang and Tao, Dacheng

Portfolio Optimization

  1. Integrating prediction in mean-variance portfolio optimization Quantitative Finance, 2023. paper

    Butler, Andrew and Kwon, Roy H

  2. ⭐Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case ICLR, 2023. paper, code

    Wang, Runzhong and Shen, Li and Chen, Yiting and Yan, Junchi and Yang, Xiaokang and Tao, Dacheng

Sorting & Ranking

  1. Ranking via sinkhorn propagation Arxiv, 2011. paper

    Ryan Prescott Adams, Richard S. Zemel

  2. Predict+optimise with ranking objectives: exhaustively learning linear functions IJCAI, 2019. paper

    Demirovic, Emir and Stuckey, Peter J. and Bailey, James and Chan, Jeffrey and Leckie, Christopher and Ramamohanarao, Kotagiri and Guns, Tias

  3. Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR, 2019. paper, code

    Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon

  4. Differentiable Ranking and Sorting using Optimal Transport NeurIPS, 2019. paper

    Marco Cuturi, Olivier Teboul, Jean-Philippe Vert

  5. Optimizing Rank-Based Metrics With Blackbox Differentiation CVPR, 2020. paper, code

    Marin Vlastelica,Anselm Paulus,Vít Musil,Georg Martius and Michal Rolínek

  6. Fast Differentiable Sorting and Ranking ICML, 2020. paper, code

    Mathieu Blondel Olivier Teboul Quentin Berthet Josip Djolonga

  7. SoftSort: A Continuous Relaxation for the argsort Operator ICML, 2020. paper, code

    Sebastian Prillo, Julian Martin Eisenschlos

  8. differentiable top k with optimal transport NeurIPS, 2020. paper

    Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

  9. Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property ICLR, 2022. paper, code

    Boshi Wang, Jialin Yi, Hang Dong, Bo Qiao, Chuan Luo, Qingwei Lin

  10. Decision-Focused Learning: Through the Lens of Learning to Rank ICML, 2022. paper, code

    Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns

  11. PiRank-Scalable Learning To Rank via Differentiable Sorting NeurIPS, 2022. paper, code

    Robin Marcel Edwin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon