{"total":165,"rows":[{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2210.04123","title":"DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems","created":"2022-10-08","cited_by_count":129,"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":"2103.03012","title":"The Transformer Network for the Traveling Salesman Problem","created":"2021-03-04","cited_by_count":59,"area":null},{"id":"2203.15544","title":"Graph Neural Networks are Dynamic Programmers","created":"2022-03-29","cited_by_count":55,"area":null},{"id":"2105.02343","title":"CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints","created":"2021-05-05","cited_by_count":49,"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":"2111.03466","title":"Learning Large Neighborhood Search Policy for Integer Programming","created":"2021-11-01","cited_by_count":35,"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":"2206.14987","title":"Lookback for Learning to Branch","created":"2022-06-30","cited_by_count":27,"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":"2106.02601","title":"Learning Hard Optimization Problems: A Data Generation Perspective","created":"2021-06-04","cited_by_count":21,"area":null},{"id":"2210.12547","title":"SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems","created":"2022-10-22","cited_by_count":21,"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":"2110.06365","title":"Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method","created":"2021-10-12","cited_by_count":20,"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":"2302.09166","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","created":"2023-02-17","cited_by_count":20,"area":null},{"id":"2203.10452","title":"CrossBeam: Learning to Search in Bottom-Up Program Synthesis","created":"2022-03-20","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":"2410.22806","title":"MILP-StuDio: MILP Instance Generation via Block Structure Decomposition","created":"2024-10-30","cited_by_count":17,"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":"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":"2103.01043","title":"Persistent Message Passing","created":"2021-03-01","cited_by_count":13,"area":null},{"id":"2210.13763","title":"Teal: Learning-Accelerated Optimization of WAN Traffic Engineering","created":"2022-10-25","cited_by_count":13,"area":null},{"id":"2211.15755","title":"Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments","created":"2022-11-28","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":"2107.10254","title":"Neural Fixed-Point Acceleration for Convex Optimization","created":"2021-07-21","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":"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":"2202.00264","title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization","created":"2022-02-01","cited_by_count":9,"area":null},{"id":"2102.09663","title":"Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming","created":"2021-02-18","cited_by_count":8,"area":null},{"id":"2111.10270","title":"FastDOG: Fast Discrete Optimization on GPU","created":"2021-11-19","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":"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":"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":"2412.14409","title":"Multi-task Representation Learning for Mixed Integer Linear Programming","created":"2024-12-18","cited_by_count":7,"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":"2205.11638","title":"DOGE-Train: Discrete Optimization on GPU with End-to-end Training","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2208.06742","title":"Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations","created":"2022-08-13","cited_by_count":6,"area":null},{"id":"2304.03755","title":"Online Learning for Scheduling MIP Heuristics","created":"2023-04-04","cited_by_count":6,"area":null},{"id":"2312.10589","title":"NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem","created":"2023-12-17","cited_by_count":6,"area":null},{"id":"2402.14847","title":"Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness","created":"2024-02-19","cited_by_count":6,"area":null},{"id":"2503.04047","title":"Reheated Gradient-based Discrete Sampling for Combinatorial Optimization","created":"2025-03-06","cited_by_count":6,"area":null},{"id":"2111.09824","title":"Machine Learning Assisted Approach for Security-Constrained Unit Commitment","created":"2021-11-17","cited_by_count":5,"area":null},{"id":"2202.07506","title":"Confidence Threshold Neural Diving","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2204.09052","title":"Optimizing Tensor Network Contraction Using Reinforcement Learning","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2401.10467","title":"Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning","created":"2024-01-19","cited_by_count":5,"area":null},{"id":"2112.12251","title":"ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data","created":"2021-12-22","cited_by_count":4,"area":null},{"id":"2307.01434","title":"Learning to Branch in Combinatorial Optimization with Graph Pointer Networks","created":"2023-07-04","cited_by_count":4,"area":null},{"id":"2406.18781","title":"Learning to Remove Cuts in Integer Linear Programming","created":"2024-06-26","cited_by_count":4,"area":null},{"id":"2408.07272","title":"Abstract Operations Research Modeling Using Natural Language Inputs","created":"2024-08-14","cited_by_count":4,"area":null},{"id":"2410.03720","title":"NeuralQP: A General Hypergraph-based Optimization Framework for Large-scale QCQPs","created":"2024-09-28","cited_by_count":4,"area":null},{"id":"2411.02158","title":"Learning Multiple Initial Solutions to Optimization Problems","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2502.02446","title":"Towards graph neural networks for provably solving convex optimization problems","created":"2025-02-04","cited_by_count":4,"area":null},{"id":"2511.09209","title":"CoCo-MILP: Inter-Variable Contrastive and Intra-Constraint Competitive MILP Solution Prediction","created":"2025-11-12","cited_by_count":4,"area":null},{"id":"2201.00723","title":"A Mixed-Integer Programming Approach to Training Dense Neural Networks","created":"2022-01-03","cited_by_count":3,"area":null},{"id":"2206.04249","title":"An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems","created":"2022-06-09","cited_by_count":3,"area":null},{"id":"2208.11010","title":"Convex mixed-integer optimization with Frank-Wolfe methods","created":"2022-08-23","cited_by_count":3,"area":null},{"id":"2211.12581","title":"UNSAT Solver Synthesis via Monte Carlo Forest Search","created":"2022-11-22","cited_by_count":3,"area":null},{"id":"2307.12409","title":"A Machine Learning Approach to Two-Stage Adaptive Robust Optimization","created":"2023-07-23","cited_by_count":3,"area":null},{"id":"2310.07068","title":"Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose","created":"2023-10-10","cited_by_count":3,"area":null},{"id":"2311.13843","title":"Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks","created":"2023-11-23","cited_by_count":3,"area":null},{"id":"2311.18072","title":"Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow","created":"2023-11-29","cited_by_count":3,"area":null},{"id":"2412.01051","title":"An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling","created":"2024-12-02","cited_by_count":3,"area":null},{"id":"2501.14211","title":"When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2506.11057","title":"STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization","created":"2025-05-22","cited_by_count":3,"area":null},{"id":"2508.01951","title":"Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization","created":"2025-08-03","cited_by_count":3,"area":null},{"id":"2102.02125","title":"Generative deep learning for decision making in gas networks","created":"2021-02-03","cited_by_count":2,"area":null},{"id":"2111.09212","title":"Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI","created":"2021-11-17","cited_by_count":2,"area":null},{"id":"2112.04785","title":"VMAgent: Scheduling Simulator for Reinforcement Learning","created":"2021-12-09","cited_by_count":2,"area":null},{"id":"2208.12226","title":"Lifelong Learning for Neural powered Mixed Integer Programming","created":"2022-08-24","cited_by_count":2,"area":null},{"id":"2209.04698","title":"Structured Q-learning For Antibody Design","created":"2022-09-10","cited_by_count":2,"area":null},{"id":"2211.13436","title":"Solving Bilevel Knapsack Problem using Graph Neural Networks","created":"2022-11-24","cited_by_count":2,"area":null},{"id":"2305.11087","title":"Lightweight Online Learning for Sets of Related Problems in Automated Reasoning","created":"2023-05-18","cited_by_count":2,"area":null},{"id":"2310.00728","title":"Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems","created":"2023-10-01","cited_by_count":2,"area":null}],"next_cursor":"100"}