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

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

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

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

Mirshekarian, Sadegh and Sormaz, Dusan.
Boosting combinatorial problem modeling with machine learning. IJCAI, 2018. paper

Lombardi, Michele and Milano, Michela.
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
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.
Machine Learning for Combinatorial Optimization: a Methodological Tour d'horizon. EJOR, 2020. journal

Bengio, Yoshua and Lodi, Andrea and Prouvost, Antoine.
Reinforcement Learning for Combinatorial Optimization: A Survey. Arxiv, 2020. paper

Mazyavkina, Nina and Sviridov, Sergey and Ivanov, Sergei and Burnaev, Evgeny.
⭐Learning Graph Matching and Related Combinatorial Optimization Problems. IJCAI, 2020. paper

Yan, Junchi and Yang, Shuang, and Hancock, Edwin R.
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.
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.
A Survey on Reinforcement Learning for Combinatorial Optimization. Arxiv, 2020. paper

Yang, Yunhao and Whinston, Andrew.
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.
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.
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
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
⭐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

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
Deep Learning of Graph Matching. CVPR, 2018. paper

Zanfir, Andrei and Sminchisescu, Cristian
⭐Learning Combinatorial Embedding Networks for Deep Graph Matching. ICCV, 2019. paper, code

Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
Deep Graphical Feature Learning for the Feature Matching Problem. ICCV, 2019. paper

Zhang, Zhen and Lee, Wee Sun
GLMNet: Graph Learning-Matching Networks for Feature Matching. Arxiv, 2019. paper

Jiang, Bo and Sun, Pengfei and Tang, Jin and Luo, Bin
⭐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
Deep Graph Matching Consensus. ICLR, 2020. paper

Fey, Matthias and Lenssen, Jan E. and Morris, Christopher and Masci, Jonathan and Kriege, Nils M.
⭐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
⭐Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach. TPAMI, 2020. paper, code

Wang, Runzhong and Yan, Junchi and Yang, Xiaokang
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
⭐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
⭐Deep Latent Graph Matching ICML, 2021. paper

Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin.
IA-GM: A Deep Bidirectional Learning Method for Graph Matching AAAI, 2021. paper

Zhao, Kaixuan and Tu, Shikui and Xu, Lei
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
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
Hypergraph Neural Networks for Hypergraph Matching ICCV, 2021. paper

Liao, Xiaowei and Xu, Yong and Ling, Haibin
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
⭐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
⭐Self-supervised Learning of Visual Graph Matching ECCV, 2022. paper, code

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

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
⭐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
⭐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

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

Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le
Learning Heuristics for the TSP by Policy Gradient CPAIOR, 2018. paper, code

Michel DeudonPierre CournutAlexandre Lacoste
Attention, Learn to Solve Routing Problems! ICLR, 2019. paper

Kool, Wouter and Van Hoof, Herke and Welling, Max.
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.
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem Arxiv, 2019. paper, code

Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson
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.
Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances. Arxiv, 2020. paper

Fu, Zhang-Hua and Qiu, Kai-Bin and Zha, Hongyuan.
A Reinforcement Learning Approach for Optimizing Multiple Traveling Salesman Problems over Graphs KBS, 2020. journal

Hu, Yujiao and Yao, Yuan and Lee, Wee Sun
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
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
The Transformer Network for the Traveling Salesman Problem IPAM, 2021. paper

Xavier Bresson，Thomas Laurent
Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal

Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie and Lim, Andrew
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper

Yao, Fan and Cai, Renqin and Wang, Hongning
Solving Dynamic Traveling Salesman Problems with Deep Reinforcement Learning. TNNLS, 2021. journal

Zizhen Zhang, Hong Liu, Meng Chu Zhou, Jiahai Wang
ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper

Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
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
Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper

Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin
Learning TSP Requires Rethinking Generalization CP, 2021. paper, code

Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau and Thomas Laurent
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
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
Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper

Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu
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
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems NeurIPS, 2022. paper

Qiu, Ruizhong and Sun, Zhiqing and Yang, Yiming
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

Kim, Minsu and Park, Junyoung and Park, Jinkyoo
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
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
Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper

Kim, Minjun and Park, Junyoung and Park, Jinkyoo
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
⭐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

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

Dai, Hanjun and Khalil, Elias B and Zhang, Yuyu and Dilkina, Bistra and Song, Le
Exploratory Combinatorial Optimization with Reinforcement Learning. AAAI, 2020. paper

LBarrett, Thomas and Clements, William and Foerster, Jakob and Lvovsky, Alex.
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS, 2020. paper

Karalias, Nikolaos and Loukas, Andreas
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning Arxiv, 2021. paper

Yao, Fan and Cai, Renqin and Wang, Hongning
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code

Ireland, David and G. Montana
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

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

Josip Djolonga, Andreas Krause
OptNet: differentiable optimization as a layer in neural networks ICML, 2017. paper, code

Brandon Amos and J. Zico Kolter
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization AAAI, 2019. paper

Bryan Wilder, Bistra Dilkina, Milind Tambe
Differentiable Convex Optimization Layers NeurIPS, 2019. paper, code

Agrawal, Akshay and Boyd, Stephen
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
Differentiation of Blackbox Combinatorial Solvers ICLR, 2020. paper, code

Marin Vlastelica, Anselm Paulus, Vít Musil, Georg Martius, Michal Rolínek
MIPaaL: Mixed Integer Program as a Layer AAAI, 2020. paper, code

Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems AAAI, 2020. paper, code

Jaynta Mandi, Emir Demirovi, Peter. J Stuckey, Tias Guns
Differentiation of blackbox combinatorial solvers ICLR, 2020. paper, code

Marin Vlastelica Pogani, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolinek
Interior Point Solving for LP-based prediction+optimization NeurIPS, 2020. paper, code

Jayanta Mandi, Tias Guns
Automatically Learning Compact Quality-aware Surrogates for Optimization Problems NeurIPS, 2020. paper

Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe
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
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
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions NeurIPS, 2021. paper, code

Mathias Niepert, Pasquale Minervini, Luca Franceschi
COPS: Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach NeurIPS, 2021. paper, code

Ahmed Abbas, Paul Swoboda
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).
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming ICML, 2022. paper

Papalexopoulos, Theodore, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma and Daving Belanger.
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
Deep Declarative Networks TPAMI, 2022. paper, code

Stephen Gould, Richard Hartley and Dylan Campbell

Vehicle Routing Problem

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

Chen, Xinyun and Tian, Yuandong.
Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows. Arxiv, 2020. paper

Lin, Bo and Ghaddar, Bissan and Nathwani, Jatin.
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
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
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing NeurIPS, 2020. paper, code

Arthur Delarue, Ross Anderson, Christian Tjandraatmadja
A Learning-based Iterative Method for Solving Vehicle Routing Problems ICLR, 2020. paper

Lu, Hao and Zhang, Xingwen and Yang, Shuang
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem Arxiv, 2020. paper

Hottung, Andre and Tierney, Kevin
Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal

Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie and Lim, Andrew
Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper

Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. AAAI, 2021. paper, code

Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
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
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
Deep Policy Dynamic Programming for Vehicle Routing Problems Arxiv, 2021. paper

Kool, Wouter and van Hoof, Herke and Gromicho, Joaquim and Welling, Max
Learning to Delegate for Large-scale Vehicle Routing NeurIPS, 2021. paper

Li, Sirui and Yan, Zhongxia and Wu, Cathy
Learning a Latent Search Space for Routing Problems using Variational Autoencoders ICLR, 2021. paper

Hottung, Andre and Bhandari, Bhanu and Tierney, Kevin
Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper

Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu
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
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

Kim, Minsu and Park, Junyoung and Park, Jinkyoo
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
Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper

Kim, Minjun and Park, Junyoung and Park, Jinkyoo
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

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

Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
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
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
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
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
Robust Scheduling with GFlowNets ICLR, 2023. paper

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

Maximal/Maximum Independent Set

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

Li, Zhuwen and Chen, Qifeng and Koltun, Vladlen.
Learning What to Defer for Maximum Independent Sets ICML, 2020. paper

Ahn, Sungsoo and Seo, Younggyo and Shin, Jinwoo
Distributed Scheduling Using Graph Neural Networks ICASSP, 2021. paper

Zhao, Zhongyuan and Verma, Gunjan and Rao, Chirag and Swami, Ananthram and Segarra, Santiago
Solving Graph-based Public Good Games with Tree Search and Imitation Learning NeurlPS, 2021. paper

Darvariu, Victor-Alexandru and Hailes, Stephen and Musolesi, Mirco
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
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
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
⭐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
Unsupervised Learning for Combinatorial Optimization Needs Meta Learning ICLR, 2023. paper, code

Wang, Haoyu and Li, Pan
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

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

Mao, Hongzi and Alizadeh, Mohammad and Menache, Ishai and Kandula, Srikanth.
Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS, 2019. paper, code

Chen, Xinyun and Tian, Yuandong.
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.
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
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
⭐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

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

Li, Dongda and Ren, Changwei and Gu, Zhaoquan and Wang, Yuexuan and Lau, Francis
RePack: Dense Object Packing Using Deep CNN with Reinforcement Learning CACS, 2019. paper

Chu, Yu-Cheng and Lin, Horng-Horng
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
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.
Robot Packing with Known Items and Nondeterministic Arrival Order. TASAE, 2020. paper

Wang, Fan and Hauser, Kris.
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.
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
Monte Carlo Tree Search on Perfect Rectangle Packing Problem Instances GECCO, 2020. paper

Pejic, Igor and van den Berg, Daan
PackIt: A Virtual Environment for Geometric Planning ICML, 2020. paper, code

Goyal, Ankit and Deng, Jia
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.
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
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention ICML Workshop, 2021. paper

Jingwei Zhang and Bin Zi and Xiaoyu Ge
Solving 3D bin packing problem via multimodal deep reinforcement learning AAMAS, 2021. paper

Jiang, Yuan, Zhiguang Cao, and Jie Zhang
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
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
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees. ICLR, 2022. paper

Hang Zhao and Kai Xu

Graph Edit Distance

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
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
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
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
⭐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
⭐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

⭐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

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

Das, Dibyendu and Ahmad, Shahid Asghar and Venkataramanan, Kumar.
Neural Models for Output-Space Invariance in Combinatorial Problems ICLR, 2022. paper

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

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

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

Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik
LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation ICML, 2022. paper, code

Ireland, David and G. Montana
⭐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

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

Bünz, Benedikt, and Matthew Lamm.
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.
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.
Learning to solve circuit-SAT: An unsupervised differentiable approach. ICLR, 2019. paper, code

Amizadeh, Saeed, Sergiy Matusevych, and Markus Weimer.
Learning Local Search Heuristics for Boolean Satisfiability. NeurIPS, 2019. paper, code

Yolcu, Emre and Poczos, Barnabas
Improving SAT solver heuristics with graph networks and reinforcement learning. Arxiv, 2019. paper

Kurin, Vitaly, Saad Godil, Shimon Whiteson, and Bryan Catanzaro.
Graph neural reasoning may fail in certifying boolean unsatisfiability. Arxiv, 2019. paper

Chen, Ziliang, and Zhanfu Yang.
Guiding high-performance SAT solvers with unsat-core predictions. SAT, 2019. paper

Selsam, Daniel, and Nikolaj Bjørner.
G2SAT: Learning to Generate SAT Formulas. NeurIPS, 2019. paper, code

You, Jiaxuan, Haoze Wu, Clark Barrett, Raghuram Ramanujan, and Jure Leskovec.
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.
Enhancing SAT solvers with glue variable predictions. Arxiv, 2020. paper

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

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

Duan, Haonan, Saeed Nejati, George Trimponias, Pascal Poupart, and Vijay Ganesh.
Learning Clause Deletion Heuristics with Reinforcement Learning. AITP, 2020. paper

Vaezipoor, Pashootan, Gil Lederman, Yuhuai Wu, Roger Grosse, and Fahiem Bacchus.
Classification of SAT problem instances by machine learning methods. CEUR, 2020. paper

Danisovszky, Márk, Zijian Győző Yang, and Gábor Kusper.
Predicting Propositional Satisfiability via End-to-End Learning. AAAI, 2020. paper

Cameron, Chris, Rex Chen, Jason Hartford, and Kevin Leyton-Brown.
Neural heuristics for SAT solving. Arxiv, 2020. paper

Jaszczur, Sebastian, Michał Łuszczyk, and Henryk Michalewski.
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.
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
Goal-Aware Neural SAT Solver. IJCNN, 2022. paper

Ozolins, Emils, Karlis Freivalds, Andis Draguns, Eliza Gaile, Ronalds Zakovskis, and Sergejs Kozlovics.
NeuroComb: Improving SAT Solving with Graph Neural Networks. Arxiv, 2022. paper

Wang, Wenxi, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, and Risto Miikkulainen.
On the Performance of Deep Generative Models of Realistic SAT Instances. SAT, 2022. paper

Garzón, Iván, Pablo Mesejo, and Jesús Giráldez-Cru.
DeepSAT: An EDA-Driven Learning Framework for SAT. Arxiv, 2022. paper

Li, Min, Zhengyuan Shi, Qiuxia Lai, Sadaf Khan, and Qiang Xu.
SATformer: Transformers for SAT Solving. Arxiv, 2022. paper

Shi, Zhengyuan, Min Li, Sadaf Khan, Hui-Ling Zhen, Mingxuan Yuan, and Qiang Xu.
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
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems NeurIPS, 2022. paper

Zhaoyu Li, Xujie Si
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions NeurIPS, 2022. paper

Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka
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

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

Kadioglu, Serdar and Malitsky, Yuri and Sellmann, Meinolf
A Aupervised Machine Learning Approach to Variable Branching in Branch-and-bound Citeseer, 2014. journal

Alvarez, Alejandro Marcos and Louveaux, Quentin and Wehenkel, Louis
Learning to Search in Branch-and-Bound Algorithms NeurlPS, 2014. paper

He, He and Daume III, Hal and Eisner, Jason M
Learningto Branch in Mixed Integer Programming AAAI, 2016. paper

E. B. Khalil, P. L. Bodic, L. Song, G. Nemhauser, B. Dilkina
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
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"
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
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
Improving Learning to Branch via Reinforcement Learning NeurIPS Workshop, 2020. paper

Sun, Haoran and Chen, Wenbo and Li, Hui and Song, Le.
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
Random sampling and machine learning to understand good decompositions Annals of Operations Research, 2020. journal

Basso, Saverio and Ceselli, Alberto and Tettamanzi, Andrea
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
Reinforcement Learning for Integer Programming: Learning to Cut ICML, 2020. paper

Tang, Yunhao and Agrawal, Shipra and Faenza, Yuri
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
Learning Efficient Search Approximation in Mixed Integer Branch and Bound Arxiv, 2020. paper

Yilmaz, Kaan and Yorke-Smith, Neil
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
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
Neural Large Neighborhood Search NeurlPS Workshop, 2020. paper

Nair, Vinod and Alizadeh, Mohammad and others
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
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
Reinforcement Learning for (Mixed) Integer Programming: Smart Feasibility Pump ICML Workshop, 2021. paper

Qi, Meng and Wang, Mengxin and Shen, Zuo-Jun
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
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
Confidence Threshold Neural Diving NeurIPS ML4CO Competition Workshop, 2021. paper

Taehyun Yoon
Learning large neighborhood search policy for integer programming NeurlPS, 2021. paper

Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie
Generative Deep Learning for Decision Making in Gas Networks Arxiv, 2021. paper

Lovis Anderson and Mark Turner and Thorsten Koch
Offline Constraint Screening for Online Mixed-integer Optimization Arxiv, 2021. paper

Asunción Jiménez-Cordero and Juan Miguel Morales and Salvador Pineda
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
Learning To Scale Mixed-Integer Programs AAAI, 2021. paper

Berthold, Timo, and Gregor Hendel
Learning Pseudo-Backdoors for Mixed Integer Programs AAAI, 2021. paper

Aaron Ferber and Jialin Song and Bistra Dilkina and Yisong Yue
Confidence Threshold Neural Diving NeurIPS ML4CO Competition Workshop, 2021. paper

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

Shen, Yunzhuang and Sun, Yuan and Eberhard, Andrew and Li, Xiaodong
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
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.
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.
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.
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing Arxiv, 2022. journal, code

Li, Longkang and Baoyuan Wu.
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.
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.
Learning to Search in Local Branching AAAI, 2022. paper, code

Liu, Defeng and Fischetti, Matteo and Lodi, Andrea
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Arxiv, 2022. paper

Zhang, Tianyu and Banitalebi-Dehkordi, Amin and Zhang, Yong
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
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
Learning to Use Local Cuts Arxiv, 2022. paper

Berthold, Timo and Francobaldi, Matteo and Hendel, Gregor
DOGE-Train: Discrete Optimization on GPU with End-to-end Training Arxiv, 2022. paper

Abbas, Ahmed and Swoboda, Paul
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
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
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

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

Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric.
Causal Discovery with Reinforcement Learning. ICLR, 2020. paper

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

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

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

Car Dispatch

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

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

Cheng, Ruoyu and Yan, Junchi
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
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
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

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

Gal Yehuda, Moshe Gabel and Assaf Schuster
Learning TSP Requires Rethinking Generalization CP, 2021. paper, code

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

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

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

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

Soroush Haeri; Ljiljana Trajković
A novel reinforcement learning algorithm for virtual network embedding Neurocomputing, 2018. journal

Haipeng Yao,Xu Chen, Maozhen Li, Peiying Zhang, Luyao Wang
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
A Virtual Network Embedding Algorithm Based On Double-Layer Reinforcement Learning TCJ, 2019. journal

Meng Li; MeiLian Lu
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
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
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
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
Accelerating Virtual Network Embedding with Graph Neural Networks CNSM, 2020. journal

Farzad Habibi; Mahdi Dolati; Ahmad Khonsari; Majid Ghaderi
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
Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks ICC, 2021. paper, code

Fahri Wisnu Murti; Samad Ali; Matti Latva-aho
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

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

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

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
⭐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

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

Butler, Andrew and Kwon, Roy H
⭐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

Ranking via sinkhorn propagation Arxiv, 2011. paper

Ryan Prescott Adams, Richard S. Zemel
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
Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR, 2019. paper, code

Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Differentiable Ranking and Sorting using Optimal Transport NeurIPS, 2019. paper

Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
Optimizing Rank-Based Metrics With Blackbox Differentiation CVPR, 2020. paper, code

Marin Vlastelica,Anselm Paulus,Vít Musil,Georg Martius and Michal Rolínek
Fast Differentiable Sorting and Ranking ICML, 2020. paper, code

Mathieu Blondel Olivier Teboul Quentin Berthet Josip Djolonga
SoftSort: A Continuous Relaxation for the argsort Operator ICML, 2020. paper, code

Sebastian Prillo, Julian Martin Eisenschlos
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
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
Decision-Focused Learning: Through the Lens of Learning to Rank ICML, 2022. paper, code

Jayanta Mandi, Vı́ctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns
PiRank-Scalable Learning To Rank via Differentiable Sorting NeurIPS, 2022. paper, code

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

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