{"total":288,"rows":[{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2103.16378","title":"End-to-End Constrained Optimization Learning: A Survey","created":"2021-03-30","cited_by_count":157,"area":null},{"id":"2102.11756","title":"Deep Policy Dynamic Programming for Vehicle Routing Problems","created":"2021-02-23","cited_by_count":94,"area":null},{"id":"2106.11113","title":"Matrix Encoding Networks for Neural Combinatorial Optimization","created":"2021-06-21","cited_by_count":91,"area":null},{"id":"2103.03012","title":"The Transformer Network for the Traveling Salesman Problem","created":"2021-03-04","cited_by_count":59,"area":null},{"id":"2105.14835","title":"Towards Lower Bounds on the Depth of ReLU Neural Networks","created":"2021-05-31","cited_by_count":56,"area":null},{"id":"2203.15386","title":"Pareto Set Learning for Neural Multi-objective Combinatorial Optimization","created":"2022-03-29","cited_by_count":56,"area":null},{"id":"2406.15007","title":"RouteFinder: Towards Foundation Models for Vehicle Routing Problems","created":"2024-06-21","cited_by_count":55,"area":null},{"id":"2205.14210","title":"MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers","created":"2022-05-27","cited_by_count":49,"area":null},{"id":"2105.13645","title":"Learning to Select Cuts for Efficient Mixed-Integer Programming","created":"2021-05-28","cited_by_count":48,"area":null},{"id":"2103.10294","title":"Learning to Schedule Heuristics in Branch-and-Bound","created":"2021-03-18","cited_by_count":46,"area":null},{"id":"2205.11107","title":"Learning to branch with Tree MDPs","created":"2022-05-23","cited_by_count":45,"area":null},{"id":"2302.00244","title":"Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model","created":"2023-02-01","cited_by_count":45,"area":null},{"id":"2203.02878","title":"A Survey for Solving Mixed Integer Programming via Machine Learning","created":"2022-03-06","cited_by_count":44,"area":null},{"id":"2107.10201","title":"Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs","created":"2021-07-21","cited_by_count":43,"area":null},{"id":"2305.17010","title":"Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets","created":"2023-05-26","cited_by_count":40,"area":null},{"id":"2203.02433","title":"The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights","created":"2022-03-04","cited_by_count":39,"area":null},{"id":"2209.12288","title":"On Representing Linear Programs by Graph Neural Networks","created":"2022-09-25","cited_by_count":39,"area":null},{"id":"2207.07888","title":"SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks","created":"2022-07-16","cited_by_count":37,"area":null},{"id":"2302.01578","title":"Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning","created":"2023-02-03","cited_by_count":37,"area":null},{"id":"2111.03466","title":"Learning Large Neighborhood Search Policy for Integer Programming","created":"2021-11-01","cited_by_count":35,"area":null},{"id":"2207.05984","title":"Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation","created":"2022-07-13","cited_by_count":32,"area":null},{"id":"2210.16934","title":"Learning to Compare Nodes in Branch and Bound with Graph Neural Networks","created":"2022-10-30","cited_by_count":31,"area":null},{"id":"2410.08288","title":"Towards Foundation Models for Mixed Integer Linear Programming","created":"2024-10-10","cited_by_count":31,"area":null},{"id":"2206.14987","title":"Lookback for Learning to Branch","created":"2022-06-30","cited_by_count":27,"area":null},{"id":"2208.10227","title":"One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction","created":"2022-08-22","cited_by_count":27,"area":null},{"id":"2106.04927","title":"A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs","created":"2021-06-09","cited_by_count":26,"area":null},{"id":"2202.10962","title":"Adaptive Cut Selection in Mixed-Integer Linear Programming","created":"2022-02-22","cited_by_count":26,"area":null},{"id":"2310.10603","title":"Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems","created":"2023-10-16","cited_by_count":24,"area":null},{"id":"2402.05501","title":"Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming","created":"2024-02-08","cited_by_count":24,"area":null},{"id":"2210.10759","title":"On Representing Mixed-Integer Linear Programs by Graph Neural Networks","created":"2022-10-19","cited_by_count":23,"area":null},{"id":"2202.08396","title":"Augment with Care: Contrastive Learning for Combinatorial Problems","created":"2022-02-17","cited_by_count":22,"area":null},{"id":"2206.02568","title":"A Deep Reinforcement Learning Framework For Column Generation","created":"2022-06-03","cited_by_count":22,"area":null},{"id":"2310.02807","title":"A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability","created":"2023-10-04","cited_by_count":21,"area":null},{"id":"2205.14345","title":"Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories","created":"2022-05-28","cited_by_count":20,"area":null},{"id":"2301.03116","title":"Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning","created":"2023-01-08","cited_by_count":20,"area":null},{"id":"2302.09166","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","created":"2023-02-17","cited_by_count":20,"area":null},{"id":"2404.02817","title":"A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches","created":"2024-04-03","cited_by_count":20,"area":null},{"id":"2105.05911","title":"The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs","created":"2021-05-12","cited_by_count":19,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2103.06643","title":"Deep Graph Matching under Quadratic Constraint","created":"2021-03-11","cited_by_count":18,"area":null},{"id":"2104.05706","title":"Towards Efficient Graph Convolutional Networks for Point Cloud Handling","created":"2021-04-12","cited_by_count":18,"area":null},{"id":"2206.13181","title":"Learning to Control Local Search for Combinatorial Optimization","created":"2022-06-27","cited_by_count":18,"area":null},{"id":"2207.11542","title":"Annealed Training for Combinatorial Optimization on Graphs","created":"2022-07-23","cited_by_count":18,"area":null},{"id":"2301.03377","title":"Machine Learning for Large-Scale Optimization in 6G Wireless Networks","created":"2023-01-03","cited_by_count":18,"area":null},{"id":"2206.11941","title":"Affinity-Aware Graph Networks","created":"2022-06-23","cited_by_count":17,"area":null},{"id":"2406.01908","title":"PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming","created":"2024-06-04","cited_by_count":17,"area":null},{"id":"2410.22806","title":"MILP-StuDio: MILP Instance Generation via Block Structure Decomposition","created":"2024-10-30","cited_by_count":17,"area":null},{"id":"2309.16965","title":"Controlling Continuous Relaxation for Combinatorial Optimization","created":"2023-09-29","cited_by_count":16,"area":null},{"id":"2406.05938","title":"Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs","created":"2024-06-09","cited_by_count":16,"area":null},{"id":"2503.01129","title":"Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming","created":"2025-03-03","cited_by_count":16,"area":null},{"id":"2406.06954","title":"Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods","created":"2024-06-11","cited_by_count":15,"area":null},{"id":"2406.14096","title":"Graph Neural Networks for Job Shop Scheduling Problems: A Survey","created":"2024-06-20","cited_by_count":15,"area":null},{"id":"2107.00866","title":"Learning Primal Heuristics for Mixed Integer Programs","created":"2021-07-02","cited_by_count":14,"area":null},{"id":"2311.05650","title":"Learning to Configure Separators in Branch-and-Cut","created":"2023-11-08","cited_by_count":14,"area":null},{"id":"2111.11207","title":"Improved Sample Complexity Bounds for Branch-and-Cut","created":"2021-11-18","cited_by_count":13,"area":null},{"id":"2201.02535","title":"Machine-learning-based arc selection for constrained shortest path problems in column generation","created":"2022-01-07","cited_by_count":13,"area":null},{"id":"2107.01410","title":"Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks","created":"2021-07-03","cited_by_count":12,"area":null},{"id":"2112.04906","title":"Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring","created":"2021-12-08","cited_by_count":12,"area":null},{"id":"2206.06965","title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch","created":"2022-06-14","cited_by_count":12,"area":null},{"id":"2310.14161","title":"Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation","created":"2023-10-22","cited_by_count":12,"area":null},{"id":"2402.07099","title":"Rethinking the Capacity of Graph Neural Networks for Branching Strategy","created":"2024-02-11","cited_by_count":12,"area":null},{"id":"2404.12638","title":"Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming","created":"2024-04-19","cited_by_count":12,"area":null},{"id":"2310.00526","title":"Are Graph Neural Networks Optimal Approximation Algorithms?","created":"2023-10-01","cited_by_count":11,"area":null},{"id":"2401.03244","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","created":"2024-01-06","cited_by_count":11,"area":null},{"id":"2505.16952","title":"FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization","created":"2025-05-22","cited_by_count":11,"area":null},{"id":"2201.06216","title":"Learning to Reformulate for Linear Programming","created":"2022-01-17","cited_by_count":10,"area":null},{"id":"2301.09943","title":"Learning To Dive In Branch And Bound","created":"2023-01-24","cited_by_count":10,"area":null},{"id":"2409.04464","title":"Leveraging Large Language Models for Solving Rare MIP Challenges","created":"2024-09-03","cited_by_count":10,"area":null},{"id":"2502.00277","title":"Regularized Langevin Dynamics for Combinatorial Optimization","created":"2025-02-01","cited_by_count":10,"area":null},{"id":"2510.27610","title":"ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling","created":"2025-10-31","cited_by_count":10,"area":null},{"id":"2111.06257","title":"Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends","created":"2021-11-05","cited_by_count":9,"area":null},{"id":"2201.10328","title":"ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation","created":"2022-01-25","cited_by_count":9,"area":null},{"id":"2202.00264","title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization","created":"2022-02-01","cited_by_count":9,"area":null},{"id":"2402.10977","title":"Generative AI and Process Systems Engineering: The Next Frontier","created":"2024-02-15","cited_by_count":9,"area":null},{"id":"2502.09994","title":"Decision Information Meets Large Language Models: The Future of Explainable Operations Research","created":"2025-02-14","cited_by_count":9,"area":null},{"id":"2502.14760","title":"EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations","created":"2025-02-20","cited_by_count":9,"area":null},{"id":"2505.24067","title":"Primal-Dual Neural Algorithmic Reasoning","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2111.10270","title":"FastDOG: Fast Discrete Optimization on GPU","created":"2021-11-19","cited_by_count":8,"area":null},{"id":"2205.10106","title":"LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation","created":"2022-05-20","cited_by_count":8,"area":null},{"id":"2401.17527","title":"Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming","created":"2024-01-31","cited_by_count":8,"area":null},{"id":"2406.09740","title":"Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2410.11061","title":"Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2412.12038","title":"LLMs for Cold-Start Cutting Plane Separator Configuration","created":"2024-12-16","cited_by_count":8,"area":null},{"id":"2103.00137","title":"Meta-Learning with Graph Neural Networks: Methods and Applications","created":"2021-02-27","cited_by_count":7,"area":null},{"id":"2106.11581","title":"Continuous-Depth Neural Models for Dynamic Graph Prediction","created":"2021-06-22","cited_by_count":7,"area":null},{"id":"2208.00374","title":"Neuro-Symbolic Learning: Principles and Applications in Ophthalmology","created":"2022-07-31","cited_by_count":7,"area":null},{"id":"2211.12443","title":"Learning context-aware adaptive solvers to accelerate quadratic programming","created":"2022-11-22","cited_by_count":7,"area":null},{"id":"2303.02442","title":"Neural Airport Ground Handling","created":"2023-03-04","cited_by_count":7,"area":null},{"id":"2310.11845","title":"Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning","created":"2023-10-18","cited_by_count":7,"area":null},{"id":"2310.13261","title":"DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee","created":"2023-10-20","cited_by_count":7,"area":null},{"id":"2401.05960","title":"Machine Learning Insides OptVerse AI Solver: Design Principles and Applications","created":"2024-01-11","cited_by_count":7,"area":null},{"id":"2409.02135","title":"Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling","created":"2024-09-02","cited_by_count":7,"area":null},{"id":"2412.14409","title":"Multi-task Representation Learning for Mixed Integer Linear Programming","created":"2024-12-18","cited_by_count":7,"area":null},{"id":"2501.00762","title":"Residual connections provably mitigate oversmoothing in graph neural networks","created":"2025-01-01","cited_by_count":7,"area":null},{"id":"2102.09193","title":"SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning","created":"2021-02-18","cited_by_count":6,"area":null},{"id":"2107.12855","title":"Neural Network Branch-and-Bound for Neural Network Verification","created":"2021-07-27","cited_by_count":6,"area":null},{"id":"2110.09076","title":"An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents","created":"2021-10-18","cited_by_count":6,"area":null},{"id":"2201.11251","title":"Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching","created":"2022-01-25","cited_by_count":6,"area":null},{"id":"2202.11296","title":"Reinforcement Learning in Practice: Opportunities and Challenges","created":"2022-02-23","cited_by_count":6,"area":null}],"next_cursor":"100"}