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, and Junchi Yan.

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.

1. A Survey of Reinforcement Learning and Agent-Based Approaches to Combinatorial Optimization. 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. Machine Learning Approaches to Learning Heuristics for Combinatorial Optimization Problems. Procedia Manufacturing, 2018. journal

   Mirshekarian, Sadegh and Sormaz, Dusan.

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

   Lombardi, Michele and Milano, Michela.

5. 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.

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

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

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

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

8. ✨Learning Graph Matching and Related Combinatorial Optimization Problems. IJCAI, 2020. paper

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

9. 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.

10. 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.

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

    Yang, Yunhao and Whinston, Andrew.

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. ✨Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching. Arxiv, 2019. paper

   Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

7. ✨Learning deep graph matching with channel-independent embedding and Hungarian attention. ICLR, 2020. paper

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

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

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

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

   Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

10. ✨Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach. TPAMI, 2020. paper

    Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

11. 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

12. ✨Deep Reinforcement Learning of Graph Matching. Arxiv, 2020. paper

    Liu, Chang and Wang, Runzhong and Jiang, Zetian and Yan, Junchi

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. Arxiv, 2019. paper

   Wang, Runzhong and Yan, Junchi and Yang, Xiaokang

3. ✨Deep Reinforcement Learning of Graph Matching. Arxiv, 2020. paper

   Liu, Chang and Wang, Runzhong and Jiang, Zetian and Yan, Junchi

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. POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. NeurIPS, 2018. paper

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

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

   Michel DeudonPierre CournutAlexandre Lacoste

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

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

5. 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.

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

   Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

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

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

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

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

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

   Xavier Bresson，Thomas Laurent

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

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

   Lu, Hao and Zhang, Xingwen and Yang, Shuang

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

1. 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.

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. 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

4. 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.

5. 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

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

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

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

   Chu, Yu-Cheng and Lin, Horng-Horng

8. 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.

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

   Wang, Fan and Hauser, Kris.

10. 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.

11. 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

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

    Pejic, Igor and van den Berg, Daan

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

    Goyal, Ankit and Deng, Jia

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

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

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

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

3. ✨Combinatorial Learning of Graph Edit Distance via Dynamic Embedding. CVPR, 2021. paper

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

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

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

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.

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.

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

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

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

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

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

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

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

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

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.

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

   Josip Djolonga, Andreas Krause

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

   Bryan Wilder, Bistra Dilkina, Milind Tambe

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

   Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe

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

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

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

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

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

   Jayanta Mandi, Tias Guns

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

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