{"total":46,"rows":[{"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":"2302.01578","title":"Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning","created":"2023-02-03","cited_by_count":37,"area":null},{"id":"2106.04033","title":"Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond","created":"2021-06-08","cited_by_count":33,"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":"2210.10759","title":"On Representing Mixed-Integer Linear Programs by Graph Neural Networks","created":"2022-10-19","cited_by_count":23,"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":"2204.07312","title":"Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts","created":"2022-04-15","cited_by_count":19,"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":"2111.11207","title":"Improved Sample Complexity Bounds for Branch-and-Cut","created":"2021-11-18","cited_by_count":13,"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":"2401.03244","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","created":"2024-01-06","cited_by_count":11,"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":"2402.10977","title":"Generative AI and Process Systems Engineering: The Next Frontier","created":"2024-02-15","cited_by_count":9,"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":"2405.15251","title":"Learning to optimize: A tutorial for continuous and mixed-integer optimization","created":"2024-05-24","cited_by_count":8,"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":"2402.02328","title":"Sample Complexity of Algorithm Selection Using Neural Networks and Its Applications to Branch-and-Cut","created":"2024-02-04","cited_by_count":7,"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":"2304.03755","title":"Online Learning for Scheduling MIP Heuristics","created":"2023-04-04","cited_by_count":6,"area":null},{"id":"2202.07506","title":"Confidence Threshold Neural Diving","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2310.07082","title":"Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize","created":"2023-10-10","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":"2212.07231","title":"Cutting Plane Selection with Analytic Centers and Multiregression","created":"2022-12-14","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":"2311.03350","title":"Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization","created":"2023-11-06","cited_by_count":3,"area":null},{"id":"2203.16642","title":"Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization","created":"2022-03-30","cited_by_count":2,"area":null},{"id":"2211.11564","title":"Adaptive Constraint Partition based Optimization Framework for Large-scale Integer Linear Programming(Student Abstract)","created":"2022-11-18","cited_by_count":2,"area":null},{"id":"2503.15847","title":"Beyond Local Selection: Global Cut Selection for Enhanced Mixed-Integer Programming","created":"2025-03-20","cited_by_count":2,"area":null},{"id":"2401.09556","title":"Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality","created":"2024-01-17","cited_by_count":1,"area":null},{"id":"2412.09056","title":"A Context-Enhanced Framework for Sequential Graph Reasoning","created":"2024-12-12","cited_by_count":1,"area":null},{"id":"2506.00252","title":"How hard is learning to cut? Trade-offs and sample complexity","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2506.02752","title":"BenLOC: A Benchmark for Learning to Configure MIP Optimizers","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2607.18252","title":"MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2310.14659","title":"Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs","created":"2023-10-23","cited_by_count":0,"area":null},{"id":"2403.03517","title":"IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability","created":"2024-03-06","cited_by_count":0,"area":null},{"id":"2412.17623","title":"Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs","created":"2024-12-23","cited_by_count":0,"area":null},{"id":"2505.11636","title":"Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2507.23390","title":"FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2510.04273","title":"Influence branching for learning to solve mixed-integer programs online","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2511.18723","title":"N2N: A Parallel Framework for Large-Scale MILP under Distributed Memory","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2601.16056","title":"Designing faster mixed integer linear programming algorithm via learning the optimal path","created":"2026-01-22","cited_by_count":0,"area":null},{"id":"2608.25282","title":"SHSP: Structure-Aware Hierarchical Solution Prediction for Mixed-Integer Linear Programming","created":"2026-08-26","cited_by_count":0,"area":null}],"next_cursor":null}