Awesome Ml4co Save

Awesome machine learning for combinatorial optimization papers.

Project README

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 Vehicle Routing Problem (VRP) 2.6 Job Shop Scheduling Problem (JSSP)
2.7 Computing Resource Allocation 2.8 Bin Packing Problem (BPP)
2.9 Graph Edit Distance (GED) 2.10 Hamiltonian Cycle Problem (HCP)
2.11 Graph Coloring 2.12 Maximal Common Subgraph (MCS)
2.13 Influence Maximization 2.14 Maximal/Maximum Independent Set
2.15 Mixed Integer Programming 2.16 Causal Discovery
2.17 Game Theoretic Semantics 2.18 Boolean Satisfiability (SAT)
2.19 Differentiable Optimization 2.20 Car Dispatch
2.21 Electronic Design Automation (EDA) 2.22 Generalization
2.23 Conjunctive Query Containment 2.24 Orienteering Problem (OP)

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. 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.

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

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

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

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

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

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

  10. 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.

  11. 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.

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

    Yang, Yunhao and Whinston, Andrew.

  13. 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.

  14. 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.

  15. 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

  16. 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

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. ✨Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. Arxiv, 2020. paper

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

  12. ✨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

  13. ✨Deep Latent Graph Matching ICML, 2021. paper

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

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

    Zhao, Kaixuan and Tu, Shikui and Xu, Lei

  15. 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

  16. 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

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

    Liao, Xiaowei and Xu, Yong and Ling, Haibin

  18. 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

  19. ✨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

  20. ✨Self-supervised Learning of Visual Graph Matching ECCV, 2022. paper, code

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

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. ✨Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching. Arxiv, 2020. paper

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

  3. ✨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

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

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

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

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

  10. 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

  11. 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

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

    Xavier Bresson,Thomas Laurent

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

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

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

    Yao, Fan and Cai, Renqin and Wang, Hongning

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

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

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

    Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

  17. 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

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

    Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin

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

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

  20. 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

  21. 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

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

    Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu

  23. 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

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

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

    Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

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

    Lu, Hao and Zhang, Xingwen and Yang, Shuang

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

    Hottung, Andre and Tierney, Kevin

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

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

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

    Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin

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

    Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

  10. 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

  11. 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

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

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

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

    Li, Sirui and Yan, Zhongxia and Wu, Cathy

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

    Hottung, Andre and Bhandari, Bhanu and Tierney, Kevin

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

    Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu

Job Shop Scheduling Problem

  1. 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

  2. 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

  3. 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

  4. 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.

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

    Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

  6. 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

  7. 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

  8. 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

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. 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. 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

  9. 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.

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

    Wang, Fan and Hauser, Kris.

  11. 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.

  12. 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

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

    Pejic, Igor and van den Berg, Daan

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

    Goyal, Ankit and Deng, Jia

  15. 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.

  16. 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

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

    Jingwei Zhang and Bin Zi and Xiaoyu Ge

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

    Jiang, Yuan, Zhiguang Cao, and Jie Zhang

  19. 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

  20. 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

  21. 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.

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

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

Mixed Integer Programming

  1. Learning to Search in Branch-and-Bound Algorithms NeurlPS, 2014. paper

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

  2. 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

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

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

  4. 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

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

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

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

    Tang, Yunhao and Agrawal, Shipra and Faenza, Yuri

  7. 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

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

    Yilmaz, Kaan and Yorke-Smith, Neil

  9. 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

  10. 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

  11. 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

  12. 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

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

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

  14. 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

  15. 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

  16. Generative deep learning for decision making in gas networks Arxiv, 2021. paper

    Lovis Anderson and Mark Turner and Thorsten Koch

  17. Offline constraint screening for online mixed-integer optimization Arxiv, 2021. paper

    Asunción Jiménez-Cordero and Juan Miguel Morales and Salvador Pineda

  18. 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

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

    Berthold, Timo, and Gregor Hendel

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

    Aaron Ferber and Jialin Song and Bistra Dilkina and Yisong Yue

  21. 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.

  22. 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.

  23. 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.

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

    Li, Longkang and Baoyuan Wu.

  25. 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.

  26. 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.

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

    Liu, Defeng and Fischetti, Matteo and Lodi, Andrea

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

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

  29. 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

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.

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.

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. 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

  20. 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

Differentiable Optimization

  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

  7. 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).

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

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

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

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

Open Source Agenda is not affiliated with "Awesome Ml4co" Project. README Source: Thinklab-SJTU/awesome-ml4co
Stars
738
Open Issues
0
Last Commit
2 weeks ago

Open Source Agenda Badge

Open Source Agenda Rating