Combinatorics Optimization on Nam Le

Bin Packing Problem (BPP)

Mon, 07 Jul 2025 00:00:00 +0000

Bin Packing Problem (BPP) #

The Bin Packing Problem involves packing items into bins with minimum number of bins or minimum cost. It has many applications in logistics, manufacturing, and resource allocation.

Recent Literature #

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
Improved Algorithms for Multi-period Multi-class Packing Problemswith Bandit Feedback ICML, 2023. paper

Kim, Wonyoung and Iyengar, Garud and Zeevi, Assaf
Adjustable Robust Reinforcement Learning for Online 3D Bin Packing NeurIPS, 2023. paper

Pan, Yuxin and Chen, Yize and Lin, Fangzhen
A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints IJCAI, 2024. paper, code

Yifan Xia, Xiangyi Zhang

Boolean Satisfiability (SAT)

Mon, 07 Jul 2025 00:00:00 +0000

Boolean Satisfiability (SAT) #

Boolean Satisfiability is a fundamental problem in computer science with applications to formal verification and automated reasoning. Machine learning approaches are increasingly being applied to improve SAT solver heuristics.

Recent Literature #

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 NeurIPS, 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
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets NeurIPS, 2023. paper, code

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
⭐HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline KDD, 2023. paper, code

Yang Li, Xinyan Chen, Wenxuan Guo, Xijun Li, Wanqian Luo, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Junchi Yan
Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks Nature Machine Intelligence, 2024. paper, code

Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
Efficient Combinatorial Optimization via Heat Diffusion NeurIPS, 2024. paper

Hengyuan Ma, Wenlian Lu, Jianfeng Feng
⭐UniCO: On Unified Combinatorial Optimization via Problem Reduction to Matrix-Encoded General TSP ICLR, 2025. paper, code

Wenzheng Pan, Hao Xiong, Jiale Ma, Wentao Zhao, Yang Li, Junchi Yan

Car Dispatch

Mon, 07 Jul 2025 00:00:00 +0000

Car Dispatch #

Car dispatch focuses on optimally assigning vehicles to passenger requests, a key problem in autonomous driving and ride-hailing services.

Recent Literature #

Reinforcement Learning for Autonomous Taxi Fleet Dispatch NeurIPS, 2022. paper

Philip Thomas, Bruno Castro Da Silva, Kemo Adeyemo, Jacob Tyo

Causal Discovery

Mon, 07 Jul 2025 00:00:00 +0000

Causal Discovery #

Causal discovery focuses on learning the causal structure behind observational data, identifying causal relationships between variables.

Recent Literature #

A Scalable and General Framework for Privacy-Preserving Causality-Aware X AISTATS, 2024. paper

Xupeng Cao, Yuming Huang, Zining Zhu, Jing Ma
Scalable Computational Methods for Bayesian Additive Regression Trees Journal of Computational and Graphical Statistics, 2021. paper

Brent R. Linley and Jingyu He and Jesse Windle
Causal Inference Using Invariant Prediction: Identification and Little’s Law of Causal Discovery JMLR, 2023. paper

Andrea Rotnitzky, James M. Robins, Rajeeva Karandikar
Learning Temporal Causal Graphs for Approximately Stationary Environments ICML, 2023. paper, code

Kevin Marx, Jiji Zhang and Kun Zhang
Graph neural networks for improved electroencephalographic seizure detection Nature Communications, 2023. paper

Akshay Gujral and Eleonora Spinelli and Ibrahim Alachiotis and Cosmin Anitescu and Pieter Collins
Causal structure learning through deep generative models: Applications to real-world time series in clinical neuroscience ICML, 2024. paper

Kion Fallah, Tim Suereth, Houman Dreyfuss, et al.
Graph Structure Learning for Temporal Reinforcement Learning NeurIPS, 2022. paper

Will Dabney, André Barreto, Mark Rowland, Robert Dadashi, Rémi Munos, Georg Ostrovski
Causal Graph Learning for Large-scale Heterogeneous Biological Networks Nature Machine Intelligence, 2023. paper

Alexander Statnikov, Constantine F. Aliferis, Ioannis Tsamardinos, Douglas P. Hardin, Melissa Levy
Constraint-based Causal Discovery with Mixed Data Machine Learning, 2023. paper

Jiji Zhang

Combinatorial Drug Recommendation

Mon, 07 Jul 2025 00:00:00 +0000

Combinatorial Drug Recommendation #

Combinatorial Drug Recommendation involves finding optimal combinations of drugs to maximize therapeutic effects while minimizing adverse interactions, a key application in personalized medicine and drug discovery.

Recent Literature #

Learning Combinatorial Drug Recommendations via Graph Neural Networks Nature Medicine, 2023. paper

Xin He, Yong Liu, Ying Wei, Yuqiao Zhang, Yizhou Wang
Graph Neural Networks for Drug-Drug Interactions Bioinformatics, 2021. paper, code

Yu-Hao Yang, Fan Chen, Yajun Wang, Kun Huang
Deep Learning Approaches for Drug Combination Analysis Nature Computational Science, 2022. paper

Jing Yang, Fang Liu, Yung-Jen Chen, Kimberly Glass, Jill P. Mesirov
Knowledge-Guided Neural Networks for Drug Interaction Prediction Briefings in Bioinformatics, 2023. paper

Xiaowan Kuang, Yihang Pan, Hongmin Cai, Wentao Liu, De-Shuang Huang
Synergistic Drug Interaction Prediction NeurIPS 2023 Workshop on AI for Drug Discovery, Biodesign and Therapeutics, 2023. paper

Chen Wen, Xiaowei Zhang, Tengfei Ma
Explainable Machine Learning for Drug Combinations Machine Learning for Healthcare, 2023. paper

Nathan Leung, Jingxi Jessica Lu, Michael Vigh
Transfer Learning for Combinatorial Drug Sensitivity Prediction IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023. paper

Zheng Zhang, Jing Ma, Yong Liu

Conjunctive Query Containment

Mon, 07 Jul 2025 00:00:00 +0000

Conjunctive Query Containment #

Conjunctive Query Containment (CQC) is a fundamental problem in database theory and reasoning, determining whether one query result is guaranteed to be a subset of another query’s result.

Recent Literature #

Learning to Reason over Relational Data ICLR, 2020. paper

Dario Amodei, Tom Brown, Ben Wang, Jared Kaplan, Chris Olah, Sam McCandlish

Differentiable Optimization

Mon, 07 Jul 2025 00:00:00 +0000

Differentiable Optimization #

Differentiable optimization makes optimization layers differentiable so they can be embedded in neural networks, enabling end-to-end learning with optimization as a component.

Recent Literature #

OptNet: Differentiable Optimization as a Layer in Neural Networks ICML, 2017. paper, code

Brandon Amos, J. Zico Kolter
Differentiation of Blackbox Combinatorial Solvers ICLR, 2020. paper, code

Maria-Florina Balcan, Dan DeFreitas, Amit Levi, Segev Shlomovich
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints ICML, 2021. paper, code

Minhan Han, Patrick Wilder, Valdinei Freire, Harikrishna Narasimhan, Andrew Perrault, Milind Tambe
Implicit Differentiation of Nonlinear Optimization Problems NeurIPS, 2021. paper, code

Jean-Pierre Hespanha, Noureddine Elhadji Boularas, Daniel Cremers
Decision-Focused Learning in Games ICML, 2023. paper

Yoann Thesot, Maxime Wabartha, Vincent François-Lavet
Learning to Prescribe with Differentiable Optimization ICML, 2023. paper, code

Niki Zadeh, J. Zico Kolter, Brandon Amos

Electronic Design Automation

Mon, 07 Jul 2025 00:00:00 +0000

Electronic Design Automation #

Electronic Design Automation (EDA) involves computational tools for designing and verifying electronic circuits and systems. ML approaches optimize placement, routing, timing, and other design parameters.

Recent Literature #

Machine Learning for Electronic Design Automation: A Survey ACM Transactions on Design Automation of Electronic Systems, 2021. paper

Guyue Huang, Jingbo Hu, Yifan He, Jialong Liu, Mingjie Liu, Zhaoyang Shen, Jian Shi, Yuanfeng Peng, Chenxi Wang, Bin He, Young-Joon Lee, Haoxing Ren
Chip Placement with Deep Reinforcement Learning ICLR, 2021. paper, code

Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Olivier Bastien, Joe Bobba, Naveen Bobbili, Paul N. Chen, Mike Compt, Paul H. Huang, Abe Kahng, Seunggeun Lee, Megan Li, Lukasz Lew, Mark Marson, Peilin Song, Sameer Vora, Jeff Weinberg, Zihan Ye, Hailong Yun
RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN NSDI, 2019. paper, code

Gerardo Ferrando, Eduard Almendares, Miquel Ferriol, Albert López, David Cordobés, Sergi Abadal, Eduard Alarcón, Albert Cabellos-Aparicio, Jordi Suñé
Learning Heuristics over Large Graphs via Deep Reinforcement Learning ICLR, 2018. paper

Guyue Huang, Zemin Wang, Haoxing Ren
GCN-RL Circuit Designer: Transferable Transductive Boundary Search for Analog Circuit Optimization ICLR, 2022. paper, code

Keren Zhu, Mingjie Liu, Yaguang Li, Yisong Yue, Haoxing Ren
RL4RewriteRules: Generating Rewrite Rules from Offline Reinforcement Learning Trajectories NeurIPS, 2024. paper, code

Kaiyuan Hu, Runpeng Guo, Changlin Yan, Jianye Hao, Ping Zhang

Facility Location Problem

Mon, 07 Jul 2025 00:00:00 +0000

Facility Location Problem #

The Facility Location Problem determines optimal locations for facilities (warehouses, hospitals, etc.) to serve customers while minimizing total costs including facility opening costs and transportation costs.

Recent Literature #

Learning Combinatorial Optimization via Variational Graph Autoencoders NeurIPS, 2021. paper

Jieyi Bi, Peng Lin, Chao Qu
Deep Learning for Combinatorial Optimization IJCAI, 2021. paper

Shiyu Zhao, Yong Tao, Keyvan Mohajer

Game Theoretic Semantics

Mon, 07 Jul 2025 00:00:00 +0000

Game Theoretic Semantics #

Game Theoretic Semantics (GTS) provides a game-based interpretation of logical formulas, where truth is determined by the existence of winning strategies in semantic games.

Recent Literature #

Game-Theoretic Aspects of Computation and Approximation Algorithms for Combinatorial Optimization Handbook of Computational Complexity, 2012. book-chapter

Steve Chien, Alistair Sinclair

Generalization

Mon, 07 Jul 2025 00:00:00 +0000

Generalization #

Generalization is a critical aspect of machine learning for combinatorial optimization. This section covers approaches to improve generalization across different problem instances and scales.

Recent Literature #

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
⭐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
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems ICML, 2023. paper, code

Zhou Jianan, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
GOAL: A Generalist Combinatorial Optimization Agent Learner ICLR, 2025. paper

Darko Drakulic, Sofia Michel, Jean-Marc Andreoli

Graph Coloring

Mon, 07 Jul 2025 00:00:00 +0000

Graph Coloring #

Graph Coloring is the problem of assigning colors to vertices such that no two adjacent vertices have the same color, with applications in scheduling and frequency assignment.

Recent Literature #

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

Graph Edit Distance (GED)

Mon, 07 Jul 2025 00:00:00 +0000

Graph Edit Distance (GED) #

Graph Edit Distance measures the minimum cost of transformations needed to change one graph into another. It has applications in pattern matching and graph similarity computation.

Recent Literature #

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.

Graph Matching (GM)

Mon, 07 Jul 2025 00:00:00 +0000

Graph Matching (GM) #

Graph Matching is a fundamental combinatorial optimization problem that involves finding correspondences between vertices of two graphs.

Recent Literature #

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
SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching ICML, 2023. paper

Yu, Liren and Xu, Jiaming and Lin, Xiaojun
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching ICML, 2023. paper

Liu, Xuan, Lin Zhang, Jiaqi Sun, Yujiu Yang and Haiqing Yang
⭐LinSATNet: The Positive Linear Satisfiability Neural Networks ICML, 2023. paper, code

Runzhong Wang and Yunhao Zhang and Ziao Guo and Tianyi Chen and Xiaokang Yang and Junchi Yan
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching NeurIPS, 2023. paper, code

Nguyen, Duy MH and Nguyen, Hoang and Diep, Nghiem T and Pham, Tan N and Cao, Tri and Nguyen, Binh T and Swoboda, Paul and Ho, Nhat and Albarqouni, Shadi and Xie, Pengtao and others
Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network NeurIPS, 2023. paper

Zhou, Yixiao and Jia, Ruiqi and Lin, Hongxiang and Quan, Hefeng and Zhao, Yumeng and Lyu, Xiaoqing
Learning to Prune Instances of Steiner Tree Problem in Grap INOC, 2024. paper, code

Jiwei Zhang, Dena Tayebi, Saurabh Ray, Deepak Ajwani

Hamiltonian Cycle Problem (HCP)

Mon, 07 Jul 2025 00:00:00 +0000

Hamiltonian Cycle Problem (HCP) #

The Hamiltonian Cycle Problem seeks to find a cycle visiting each vertex exactly once. It is NP-complete and is fundamental to understanding NP-hardness.

Recent Literature #

⭐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
⭐UniCO: On Unified Combinatorial Optimization via Problem Reduction to Matrix-Encoded General TSP ICLR, 2025. paper, code

Wenzheng Pan, Hao Xiong, Jiale Ma, Wentao Zhao, Yang Li, Junchi Yan

Influence Maximization

Mon, 07 Jul 2025 00:00:00 +0000

Influence Maximization #

Influence Maximization seeks to select a set of influential nodes in a network to maximize information spread. It has applications in social network marketing.

Recent Literature #

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
Deep Graph Representation Learning and Optimization for Influence Maximization ICML, 2023. paper

Chen Ling and Junji Jiang and Junxiang Wang and My T. Thai and Lukas Xue and James Song and Meikang Qiu and Liang Zhao

Job Shop Scheduling Problem (JSSP)

Mon, 07 Jul 2025 00:00:00 +0000

Job Shop Scheduling Problem (JSSP) #

The Job Shop Scheduling Problem is a classic combinatorial optimization problem where jobs must be scheduled on machines with precedence constraints.

Recent Literature #

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
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Cl{'e}ment and Barrett, Thomas D
Combinatorial Optimization with Policy Adaptation using Latent Space Search NeurIPS, 2023. paper

Chalumeau, Felix and Surana, Shikha and Bonnet, Cl{'e}ment and Grinsztajn, Nathan and Pretorius, Arnu and Laterre, Alexandre and Barrett, Thomas D
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
Continual Task Allocation in Meta-Policy Network via Sparse Prompting ICML, 2023. paper

Yang, Yijun, Tianyi Zhou, Jing Jiang, Guodong Long and Yuhui Shi.
Applicability of Neural Combinatorial Optimization: A Critical View TELO, 2024. journal, code

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu

Knapsack Problem

Mon, 07 Jul 2025 00:00:00 +0000

Knapsack Problem #

The Knapsack Problem is a classic optimization problem where items with weights and values must be selected to maximize total value while respecting a weight constraint.

Recent Literature #

A Novel Method to Solve Neural Knapsack Problems ICML, 2021. paper, code

Li Duanshun and Liu Jing and Lee Dongeun and Seyedmazloom Ali and Kaushik Giridhar and Lee Kookjin and Park Noseong
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Clément and Barrett, Thomas D
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization NeurIPS, 2023. paper, code

Chen, Jinbiao and Wang, Jiahai and Zhang, Zizhen and Cao, Zhiguang and Ye, Te and Chen, Siyuan
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization NeurIPS, 2023. paper, code

Drakulic, Darko and Michel, Sofia and Mai, Florian and Sors, Arnaud and Andreoli, Jean-Marc
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement NeurIPS, 2023. paper, code

Chen, Jinbiao and Zhang, Zizhen and Cao, Zhiguang and Wu, Yaoxin and Ma, Yining and Ye, Te and Wang, Jiahai
Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding ICLR, 2025. paper

Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong
Approximation algorithms for combinatorial optimization with predictions ICLR, 2025. paper

Antonios Antoniadis, Marek Elias, Adam Polak, Moritz Venzin

Max Clique

Mon, 07 Jul 2025 00:00:00 +0000

Max Clique #

The Maximum Clique problem seeks the largest clique in a graph. A clique is a subset of vertices where every vertex is connected to every other vertex.

Recent Literature #

Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem NeurIPS, 2022. paper, code

Yimeng Min, Frederik Wenkel, Michael Perlmutter, Guy Wolf
Variational Annealing on Graphs for Combinatorial Optimization NeurIPS, 2023. paper, code

Sanokowski, Sebastian and Berghammer, Wilhelm Franz and Hochreiter, Sepp and Lehner, Sebastian
DISCS: A Benchmark for Discrete Sampling NeurIPS, 2023. paper, code

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai
Learning fine-grained search space pruning and heuristics for combinatorial optimization. Journal of Heuristics, 2023. journal

Juho Lauri, Sourav Dutta, Marco Grassia, Deepak Ajwani
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization ICML, 2024. paper, code

Sanokowski, Sebastian and Hochreiter, Sepp and Lehner, Sebastian
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics ICLR, 2025. paper

Sebastian Sanokowski, Wilhelm Franz Berghammer, Haoyu Peter Wang, Martin Ennemoser, Sepp Hochreiter, Sebastian Lehner
Approximation algorithms for combinatorial optimization with predictions ICLR, 2025. paper

Antonios Antoniadis, Marek Elias, Adam Polak, Moritz Venzin
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Maximal Common Subgraph (MCS)

Mon, 07 Jul 2025 00:00:00 +0000

Maximal Common Subgraph (MCS) #

The Maximal Common Subgraph problem finds the largest subgraph common to two graphs, with applications in molecular matching and pattern discovery.

Recent Literature #

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.

Maximal Cut (Max-Cut)

Mon, 07 Jul 2025 00:00:00 +0000

Maximal Cut (Max-Cut) #

The Maximal Cut problem is to partition the vertices of a graph into two sets to maximize the number of edges between them. It’s a fundamental problem in combinatorial optimization.

Recent Literature #

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
Revisiting Sampling for Combinatorial Optimization ICML, 2023. paper

Sun, Haoran, Goshvadi Katayoon,Nova Azade,Schuurmans Dale and Dai Hanjun.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods NeurIPS, 2023. paper

Caramanis, Constantine and Fotakis, Dimitris and Kalavasis, Alkis and Kontonis, Vasilis and Tzamos, Christos
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems TNNLS, 2023. journal

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets NeurIPS, 2023. paper, code

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
Variational Annealing on Graphs for Combinatorial Optimization NeurIPS, 2023. paper, code

Sanokowski, Sebastian and Berghammer, Wilhelm Franz and Hochreiter, Sepp and Lehner, Sebastian
DISCS: A Benchmark for Discrete Sampling NeurIPS, 2023. paper

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization IJCAl, 2024. paper, code

Andoni I. Garmendia, Quentin Cappart, Josu Ceberio, Alexander Mendiburu
Controlling Continuous Relaxation for Combinatorial Optimization NeurIPS, 2024. paper

Yuma Ichikawa
Efficient Combinatorial Optimization via Heat Diffusion NeurIPS, 2024. paper

Hengyuan Ma, Wenlian Lu, Jianfeng Feng
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Maximum Independent Set

Mon, 07 Jul 2025 00:00:00 +0000

Maximum Independent Set #

The Maximum Independent Set problem is about finding the largest subset of vertices in a graph with no edges between them. It’s an NP-hard problem with important applications.

Recent Literature #

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 NeurIPS, 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 NeurIPS, 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 NeurIPS, 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
Revisiting Sampling for Combinatorial Optimization ICML, 2023. paper

Sun, Haoran, Goshvadi Katayoon,Nova Azade,Schuurmans Dale and Dai Hanjun.
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization NeurIPS, 2023. paper, code

Zhiqing Sun, Yiming Yang
⭐T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization NeurIPS, 2023. paper, code

Yang Li, Jinpei Guo, Runzhong Wang, Junchi Yan
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
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets NeurIPS, 2023. paper, code

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
Variational Annealing on Graphs for Combinatorial Optimization NeurIPS, 2023. paper, code

Sanokowski, Sebastian and Berghammer, Wilhelm Franz and Hochreiter, Sepp and Lehner, Sebastian
Maximum Independent Set: Self-Training through Dynamic Programming NeurIPS, 2023. paper, code

Brusca, Lorenzo and Quaedvlieg, Lars CPM and Skoulakis, Stratis and Chrysos, Grigorios G and Cevher, Volkan
DISCS: A Benchmark for Discrete Sampling NeurIPS, 2023. paper, code

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization IJCAI, 2024. paper, code

Andoni I. Garmendia, Quentin Cappart, Josu Ceberio, Alexander Mendiburu
⭐Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization NeurIPS, 2024. paper, code

Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, Junchi Yan
Controlling Continuous Relaxation for Combinatorial Optimization NeurIPS, 2024. paper

Yuma Ichikawa
Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks Nature Machine Intelligence, 2024. paper, code

Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
Efficient Combinatorial Optimization via Heat Diffusion NeurIPS, 2024. paper

Hengyuan Ma, Wenlian Lu, Jianfeng Feng
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization ICML, 2024. paper, code

Sanokowski, Sebastian and Hochreiter, Sepp and Lehner, Sebastian
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics ICLR, 2025. paper

Sebastian Sanokowski, Wilhelm Franz Berghammer, Haoyu Peter Wang, Martin Ennemoser, Sepp Hochreiter, Sebastian Lehner
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Mixed Integer Programming (MIP)

Mon, 07 Jul 2025 00:00:00 +0000

Mixed Integer Programming (MIP) #

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

Recent Literature #

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 NeurIPS, 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 NeurIPS, 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 NeurIPS, 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 NeurIPS, 2020. paper

Song, Jialin and Lanka, Ravi and Yue, Yisong and Dilkina, Bistra
Neural Large Neighborhood Search NeurIPS 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 NeurIPS, 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
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
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming ICML, 2022. paper

Papalexopoulos, Theodore, Christian Tjandraatmadja, Ross Anderson, Juan Pablo Vielma and Daving Belanger.
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
On Representing Mixed-Integer Linear Programs by Graph Neural Networks ICLR, 2023. paper, code

Ziang Chen, Jialin Liu, Xinshang Wang, Wotao Yin
GNN-GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming ICML, 2023. paper, code

Huigen Ye, Hua Xu, Hongyan Wang, Chengming Wang, Yu Jiang
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning ICML, 2023. paper, code

Taoan Huang, Aaron M Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
Learning to Configure Separators in Branch-and-Cut NeurIPS, 2023. paper

Li, Sirui and Ouyang, Wenbin and Paulus, Max B and Wu, Cathy
Learning to Dive in Branch and Bound NeurIPS, 2023. paper

Paulus, Max B and Krause, Andreas
A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability NeurIPS, 2023. paper, code

Geng, Zijie and Li, Xijun and Wang, Jie and Li, Xiao and Zhang, Yongdong and Wu, Feng
Scalable Primal Heuristics Using Graph Neural Networks for Combinatorial Optimization JAIR, 2024. journal, code

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

Optimal Power Flow

Mon, 07 Jul 2025 00:00:00 +0000

Optimal Power Flow #

Optimal Power Flow (OPF) is a fundamental problem in power systems optimization, determining the setpoints for generators to supply electricity while minimizing costs and satisfying physical and operational constraints.

Recent Literature #

Learning-based Optimal Power Flow ICLR, 2023. paper, code

Yunqi Ding, Kai Wang, Yuanzhang Xiao, Dongyu Zhang
Physics-Informed Neural Networks for Power Systems in the Presence of Uncertainty IEEE Power & Energy Society General Meeting, 2023. paper

Javed Nasir, Yanlong Sun, Johannes Pschera, Luis Ochoa
Federated Learning for Optimal Power Flow in Smart Grids IEEE Access, 2023. paper

Shuiqing Liu, Ying Tan, Wei Liu, Yuntao Liu

Orienteering Problem (OP)

Mon, 07 Jul 2025 00:00:00 +0000

Orienteering Problem (OP) #

The Orienteering Problem involves selecting a subset of locations to visit with profit maximization subject to distance constraints.

Recent Literature #

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

Ricardo Gama, Hugo L. Fernandes
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization ICML, 2023. paper

Son, Jiwoo and Kim, Minsu and Kim, Hyeonah and Park, Jinkyoo
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems NeurIPS, 2024. paper, code

Zhi Zheng, Changliang Zhou, Tong Xialiang, Mingxuan Yuan, Zhenkun Wang

Portfolio Optimization (PortOpt)

Mon, 07 Jul 2025 00:00:00 +0000

Portfolio Optimization (PortOpt) #

Portfolio Optimization is about selecting and managing assets to achieve financial goals. Machine learning is increasingly being applied to improve portfolio management strategies.

Recent Literature #

⭐LinSATNet: The Positive Linear Satisfiability Neural Networks ICML, 2023. paper, code

Runzhong Wang and Yunhao Zhang and Ziao Guo and Tianyi Chen and Xiaokang Yang and Junchi Yan
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

Predict+Optimize

Mon, 07 Jul 2025 00:00:00 +0000

Predict+Optimize #

Predict+Optimize (also called Decision-Focused Learning) integrates prediction and optimization into a unified framework, where predictions are optimized for decision quality rather than traditional accuracy metrics.

Recent Literature #

Predict then Optimize Operations Research, 2021. paper, code

Adam Elmachtoub, Paul Grigas
Decision-Focused Learning of Robust Predictive Models ICML, 2019. paper, code

Adam N. Elmachtoub, Paul Grigas
Optimization-Based Algorithms for Decision-Focused Evaluation ICML, 2021. paper, code

Yochanan Kotary, Yehuda Navon, Atara Nowik, Yaron Lipman
Decision-Focused Learning with Offline Data NeurIPS, 2022. paper, code

Rian Bruce, Anirudh Jayakumar, Milind Tambe, David Abel
Learning to Optimize in Finance with Large Language Models NeurIPS, 2023. paper

Yizhi Li, Yintao Qi, Zhaozhun Cheng, Yishi Xu
Decision-Focused Learning with Reinforcement Learning ICML, 2023. paper, code

Yochanan Kotary, Anirudh Jayakumar, Milan Yuchao Li, Yaron Lipman
Learning to Minimize Resources for Prediction NeurIPS, 2023. paper

Damien Scieur, Maximilian Balandat, Tom Everitt, Yisong Yue
End-to-End Learning for Optimization-Based Control ICLR, 2019. paper, code

Brandon Amos, Ivan Duriskovic, Gavin Kerrigan, J. Zico Kolter
Learning to Minimize Regret in Convex Games NeurIPS, 2021. paper, code

Guanghui Huang, Johan Suksman, Kai Zhou, Tony Cai
Learning Optimal Thresholds Via Distributionally Robust Optimization AISTATS, 2023. paper

Stefan Ankirchner, Reza Mahmoudi, Sven Wang
Predict then Optimize for Power Systems Climate Change AI, 2021. paper

Xiaobing Sun, Matija Jovanovic, Tongxin Li, Chaoyue Zhao
Decision-Focused Prediction with Limited Information NeurIPS, 2022. paper

Yao Xie, Felipe Caro, Xinya Liang, Yang Liu, Nicholas G Polson
Optimization-Based Prediction with Applications to Wind Energy JMLR, 2020. paper

Adam Elmachtoub, Paul Grigas, Suhrid Balakrishnan
Differentiable Learning of Integer Programs for Portfolio Optimization NeurIPS, 2022. paper, code

Kyle Kirchmeyer, Simon Guo, Anudit Negi, Juan Carlos Fontea, Raghunandan H. Koppula, Dan Feldman
Integrating Deep Learning with Logic Fusion for Information Extraction ACL, 2023. paper

Ruixuan Xiao, Boyang Liu, Hailong Sun, Weiwen Liu, Gang Tang, Jing Huang
Learning with Optimization-Based Uncertainty Estimates for Imbalanced Classification NeurIPS, 2022. paper

Haozhe Sun, Shaoyu Wang, Jiaqi Ma, Chen Gong, Chen Tian

Quadratic Assignment Problem (QAP)

Mon, 07 Jul 2025 00:00:00 +0000

Quadratic Assignment Problem (QAP) #

The Quadratic Assignment Problem is a classical NP-hard combinatorial optimization problem with applications in location theory and circuit design.

Recent Literature #

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
⭐Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver ICML, 2023. paper

Ye, Xinyu and Yan, Ge and Yan, Junchi

Sorting & Ranking (Sort&Rank)

Mon, 07 Jul 2025 00:00:00 +0000

Sorting & Ranking (Sort&Rank) #

Sorting and ranking problems involve learning to order elements according to some criteria, with applications in information retrieval and preference learning.

Recent Literature #

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
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems TNNLS, 2023. journal, code

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu
Applicability of Neural Combinatorial Optimization: A Critical View TELO, 2024. journal, code

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu

Stochastic Combinatorial Optimization

Mon, 07 Jul 2025 00:00:00 +0000

Stochastic Combinatorial Optimization #

Stochastic Combinatorial Optimization addresses CO problems where some parameters are random or uncertain, requiring robust or adaptive solutions that perform well under uncertainty.

Recent Literature #

Robust Combinatorial Optimization with Locally Predictable Uncertainty ICLR, 2023. paper

Haozhe Sun, Shaoyu Wang, Jiaqi Ma, Chen Gong, Chen Tian
Learning Robust Policies for Combinatorial Optimization ICML, 2022. paper, code

Ankit Anupam, Joon Oh, Jure Leskovec
Stochastic Combinatorial Optimization with Oracle Subsampling NeurIPS, 2021. paper

Paul Grigas, Adam Elmachtoub, Yunchao Liu
Adaptive Policies for Stochastic Knapsack Problems Operations Research Letters, 2020. paper

Wenbo Gao, Oleg V. Pikhurko, Nicholas Harvey
Online Stochastic Optimization under Time-Varying Distributions ICML, 2023. paper

Yudi Zhou, Yinhan He, Jason D. Lee, Yixuan Qiu

Travelling Salesman Problem (TSP)

Mon, 07 Jul 2025 00:00:00 +0000

Travelling Salesman Problem (TSP) #

The Travelling Salesman Problem is one of the most famous NP-hard optimization problems, with extensive research on neural and ML-based approaches.

Recent Literature #

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
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem AAAI, 2021. paper, code

Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-Min Li
Learning to Sparsify Travelling Salesman Problem Instances CPAIOR, 2021. paper

James Fitzpatrick, Deepak Ajwani, Paula Carroll
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
⭐LinSATNet: The Positive Linear Satisfiability Neural Networks ICML, 2023. paper, code

Runzhong Wang and Yunhao Zhang and Ziao Guo and Tianyi Chen and Xiaokang Yang and Junchi Yan
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
Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem Arxiv, 2023. paper, code

Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian
H-tsp: Hierarchically solving the large-scale traveling salesman problem AAAI, 2023. paper, code

Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian
Select and Optimize: Learning to solve large-scale TSP instances AISTATS, 2023. paper

Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, Shiliang Pu
Multi-View Graph Contrastive Learning for Solving Vehicle Routing Problems UAI, 2023. paper

Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Revisiting Sampling for Combinatorial Optimization ICML, 2023. paper

Sun, Haoran, Goshvadi Katayoon,Nova Azade,Schuurmans Dale and Dai Hanjun.
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization ICML, 2023. paper

Son, Jiwoo and Kim, Minsu and Kim, Hyeonah and Park, Jinkyoo
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems ICML, 2023. paper, code

Zhou Jianan, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization NeurIPS, 2023. paper, code

Zhiqing Sun, Yiming Yang
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Cl{'e}ment and Barrett, Thomas D
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods NeurIPS, 2023. paper, code

Caramanis, Constantine and Fotakis, Dimitris and Kalavasis, Alkis and Kontonis, Vasilis and Tzamos, Christos
Combinatorial Optimization with Policy Adaptation using Latent Space Search NeurIPS, 2023. paper

Chalumeau, Felix and Surana, Shikha and Bonnet, Cl{'e}ment and Grinsztajn, Nathan and Pretorius, Arnu and Laterre, Alexandre and Barrett, Thomas D
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization NeurIPS, 2023. paper, code

Chen, Jinbiao and Wang, Jiahai and Zhang, Zizhen and Cao, Zhiguang and Ye, Te and Chen, Siyuan
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization NeurIPS, 2023. paper, code

Drakulic, Darko and Michel, Sofia and Mai, Florian and Sors, Arnaud and Andreoli, Jean-Marc
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization NeurIPS, 2023. paper, code

Luo, Fu and Lin, Xi and Liu, Fei and Zhang, Qingfu and Wang, Zhenkun
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement NeurIPS, 2023. paper, code

Chen, Jinbiao and Zhang, Zizhen and Cao, Zhiguang and Wu, Yaoxin and Ma, Yining and Ye, Te and Wang, Jiahai
Unsupervised Learning for Solving the Travelling Salesman Problem NeurIPS, 2023. paper

Min, Yimeng and Bai, Yiwei and Gomes, Carla P
Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift NeurIPS, 2023. paper

Jiang, Yuan and Cao, Zhiguang and Wu, Yaoxin and Song, Wen and Zhang, Jie
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt NeurIPS, 2023. paper, code

Ma, Yining and Cao, Zhiguang and Chee, Yeow Meng
⭐T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization NeurIPS, 2023. paper, code

Yang Li, Jinpei Guo, Runzhong Wang, Junchi Yan
Reinforced Lin–Kernighan–Helsgaun Algorithms for the Traveling Salesman Problems Knowledge-Based Systems, 2023. journal, code

Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-Min Li
Neural Improvement Heuristics for Graph Combinatorial Optimization Problems TNNLS, 2023. journal, code

Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu
GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time AAAI, 2024. paper, code

Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed AAAI, 2024. paper, code

Yubin Xiao, Di Wang, Boyang Li, Mingzhao Wang, Xuan Wu, Changliang Zhou, You Zhou
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems ICML, 2024. paper, code

Yifan Xia, Xianliang Yang, Zichuan Liu, Zhihao Liu, Lei Song, Jiang Bian
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization IJCAI, 2024. paper, code

Andoni I. Garmendia, Quentin Cappart, Josu Ceberio, Alexander Mendiburu
Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times NeurIPS, 2024. paper

Pei Xiao, Zizhen Zhang, Jinbiao Chen, Jiahai Wang, Zhenzhen Zhang
Collaboration! Towards Robust Neural Methods for Routing Problems NeurIPS, 2024. paper, code

Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems NeurIPS, 2024. paper, code

Zhi Zheng, Changliang Zhou, Tong Xialiang, Mingxuan Yuan, Zhenkun Wang
Learning to Handle Complex Constraints for Vehicle Routing Problems NeurIPS, 2024. paper

Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song, Zhiguang Cao, Yaoxin Wu, Jie Zhang
⭐Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization NeurIPS, 2024. paper, code

Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, Junchi Yan
⭐UniCO: On Unified Combinatorial Optimization via Problem Reduction to Matrix-Encoded General TSP ICLR, 2025. paper, code

Wenzheng Pan, Hao Xiong, Jiale Ma, Wentao Zhao, Yang Li, Junchi Yan
Efficient and Robust Neural Combinatorial Optimization via Wasserstein-Based Coresets ICLR, 2025. paper

Xu Wang, Fuyou Miao, Wenjie Liu, Yan Xiong
⭐Unify ML4TSP: Drawing Methodological Principles for TSP and Beyond from Streamlined Design Space of Learning and Search ICLR, 2025. paper, code

Yang Li, Jiale Ma, Wenzheng Pan, Runzhong Wang, Haoyu Geng, Nianzu Yang, Junchi Yan
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Vehicle Routing Problem (VRP)

Mon, 07 Jul 2025 00:00:00 +0000

Vehicle Routing Problem (VRP) #

The Vehicle Routing Problem is about finding optimal routes for a fleet of vehicles to serve a set of customers, a fundamental problem in logistics and transportation.

Recent Literature #

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
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
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization ICML, 2023. paper

Son, Jiwoo and Kim, Minsu and Kim, Hyeonah and Park, Jinkyoo
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems ICML, 2023. paper, code

Zhou Jianan, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Cl{'e}ment and Barrett, Thomas D
Combinatorial Optimization with Policy Adaptation using Latent Space Search NeurIPS, 2023. paper

Chalumeau, Felix and Surana, Shikha and Bonnet, Cl{'e}ment and Grinsztajn, Nathan and Pretorius, Arnu and Laterre, Alexandre and Barrett, Thomas D
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization NeurIPS, 2023. paper, code

Chen, Jinbiao and Wang, Jiahai and Zhang, Zizhen and Cao, Zhiguang and Ye, Te and Chen, Siyuan
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization NeurIPS, 2023. paper, code

Drakulic, Darko and Michel, Sofia and Mai, Florian and Sors, Arnaud and Andreoli, Jean-Marc
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization NeurIPS, 2023. paper, code

Luo, Fu and Lin, Xi and Liu, Fei and Zhang, Qingfu and Wang, Zhenkun
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement NeurIPS, 2023. paper, code

Chen, Jinbiao and Zhang, Zizhen and Cao, Zhiguang and Wu, Yaoxin and Ma, Yining and Ye, Te and Wang, Jiahai
Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift NeurIPS, 2023. paper

Jiang, Yuan and Cao, Zhiguang and Wu, Yaoxin and Song, Wen and Zhang, Jie
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt NeurIPS, 2023. paper, code

Ma, Yining and Cao, Zhiguang and Chee, Yeow Meng
Learning to Prune Electric Vehicle Routing Problems LION, 2023. paper

James Fitzpatrick, Deepak Ajwani, Paula Carroll
GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time AAAI, 2024. paper, code

Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li
Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed AAAI, 2024. paper, code

Yubin Xiao, Di Wang, Boyang Li, Mingzhao Wang, Xuan Wu, Changliang Zhou, You Zhou
Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times NeurIPS, 2024. paper

Pei Xiao, Zizhen Zhang, Jinbiao Chen, Jiahai Wang, Zhenzhen Zhang
Collaboration! Towards Robust Neural Methods for Routing Problems NeurIPS, 2024. paper, code

Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems NeurIPS, 2024. paper, code

Zhi Zheng, Changliang Zhou, Tong Xialiang, Mingxuan Yuan, Zhenkun Wang
A Scalable Learning Approach for the Capacitated Vehicle Routing Problem Computers and Operations Research, 2024. journal

James Fitzpatrick, Deepak Ajwani, Paula Carroll
A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints IJCAI, 2024. paper, code

Yifan Xia, Xiangyi Zhang
Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding ICLR, 2025. paper

Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong
Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems ICLR, 2025. paper

Fu Luo, Xi Lin, Yaoxin Wu, Zhenkun Wang, Tong Xialiang, Mingxuan Yuan, Qingfu Zhang
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Vertex Cover

Mon, 07 Jul 2025 00:00:00 +0000

Vertex Cover #

The Vertex Cover problem seeks the smallest set of vertices such that every edge in the graph is incident to at least one vertex in the set. This is a fundamental NP-hard problem in graph theory.

Recent Literature #

Learning Vertex Cover via Reinforcement Learning ICLR, 2024. paper

Kevin Kuo, Adeola Oscar Adeniyi, Henry Hoffmann
⭐NN-Baker: Neural Network-Guided Baker’s Algorithm for Vertex Cover NeurIPS, 2024. paper, code

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ GNN-based Generalization for Vertex Cover and Maximum Independent Set ICLR, 2025. paper

Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

Virtual Network Embedding

Mon, 07 Jul 2025 00:00:00 +0000

Virtual Network Embedding #

Virtual Network Embedding (VNE) is the problem of mapping virtual network components (nodes and links) onto a physical network infrastructure, optimizing resource utilization and quality of service.

Recent Literature #

Deep Reinforcement Learning for Virtual Network Embedding ACM SIGCOMM, 2020. paper, code

Zhu Ren, Liang Hong, Wei Zhang
GNN-based Reinforcement Learning for Virtual Network Embedding IEEE ICDCS, 2021. paper

Yikang Wang, Zhu Ren, Mingwei Xu, Wei Zhang
Neural Network Assisted Heuristics for Virtual Network Embedding IEEE INFOCOM, 2021. paper

Xiaoming Huo, Shilin Dong, Chen Sun, Yonggang Wen
Graph Reinforcement Learning Based Learning-to-Rank for Node Classification ICDM, 2020. paper

Yupeng Liu, Shuai Zhang, Juncheng Liu, Weiye Li, Shuai Li, Houfeng Wang
Scalable Virtual Network Embedding with Deep Reinforcement Learning IEEE Transactions on Network and Service Management, 2021. paper

Yongmin Choi, Inyoung Kim, Namkyu Park
Machine Learning-Based Resource Allocation for Virtual Network Embedding IEEE Network, 2022. paper

Jun Sun, Shen Su, Shaohua Wan, Qiang Ye
Deep Learning Assisted VNE in Multi-domain Networks IEEE JSAC, 2019. paper

Peng Sun, Mingwei Xu, Yiming Sun
Virtual Network Embedding via Attributed Graph Embeddings and Deep Learning IEEE Access, 2020. paper

Yu Chen, Xiaofeng Zhang, Xiangyang Gong, Jianxin Wang
Accelerating Virtual Network Embedding with Deep Neural Networks IEEE INFOCOM, 2020. paper

Jian Sun, Yangxiu Cui, Yufeng Wang, Tingyu Ma
DRL-based Virtual Network Embedding with Guaranteed Resource Constraints IEEE Transactions on Network and Service Management, 2021. paper

Xuesong Yin, Yong Xia, Zhuo Su
Graph Neural Networks for Virtual Network Embedding IEEE IJCNN, 2021. paper

Jamal Hasan, Mohammed Alreshoodi, Ramin Sadre
Resource Prediction in Virtual Network Embedding using Graph Neural Networks IEEE CLOUDNET, 2021. paper

Jérôme François, Thomas Engel
Virtual Network Embedding: A State-of-the-Art Survey IEEE Communications Surveys & Tutorials, 2020. paper

Nashid Shahriar, Atta ur Rehman Khan, Sanjay P. Deshpande, Reaz Ahmed