{"total":38,"rows":[{"id":"2410.08288","title":"Towards Foundation Models for Mixed Integer Linear Programming","created":"2024-10-10","cited_by_count":31,"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":"2410.22806","title":"MILP-StuDio: MILP Instance Generation via Block Structure Decomposition","created":"2024-10-30","cited_by_count":17,"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":"2311.05650","title":"Learning to Configure Separators in Branch-and-Cut","created":"2023-11-08","cited_by_count":14,"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":"2301.09943","title":"Learning To Dive In Branch And Bound","created":"2023-01-24","cited_by_count":10,"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":"2412.12038","title":"LLMs for Cold-Start Cutting Plane Separator Configuration","created":"2024-12-16","cited_by_count":8,"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":"2503.20505","title":"Riemannian Optimization on Relaxed Indicator Matrix Manifold","created":"2025-03-26","cited_by_count":5,"area":null},{"id":"2310.00112","title":"Reinforcement Learning for Node Selection in Branch-and-Bound","created":"2023-09-29","cited_by_count":4,"area":null},{"id":"2403.14413","title":"Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis","created":"2024-03-21","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":"2605.10401","title":"LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs","created":"2026-05-11","cited_by_count":4,"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":"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":"2209.04698","title":"Structured Q-learning For Antibody Design","created":"2022-09-10","cited_by_count":2,"area":null},{"id":"2505.24779","title":"Are Your Generated Instances Truly Useful? GenBench-MILP: A Benchmark Suite for MILP Instance Generation","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2508.18742","title":"Constraint Matters: Multi-Modal Representation for Reducing Mixed-Integer Linear programming","created":"2025-08-26","cited_by_count":2,"area":null},{"id":"2306.05905","title":"TreeDQN: Sample-Efficient Off-Policy Reinforcement Learning for Combinatorial Optimization","created":"2023-06-09","cited_by_count":1,"area":null},{"id":"2308.00327","title":"Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs","created":"2023-08-01","cited_by_count":1,"area":null},{"id":"2411.19517","title":"RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs","created":"2024-11-29","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":"2601.04509","title":"A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention","created":"2026-01-08","cited_by_count":1,"area":null},{"id":"2606.23299","title":"GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2404.14457","title":"Graph Coloring Using Heat Diffusion","created":"2024-04-21","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":"2508.03030","title":"Collab-Solver: Collaborative Solving Policy Learning for Mixed-Integer Linear Programming","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2509.21000","title":"Feature Augmentation of GNNs for ILPs: Local Uniqueness Suffices","created":"2025-09-25","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":"2603.24033","title":"SRG: Score-based Relaxation-guided Generation for Mixed Integer Linear Programming","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2604.23921","title":"Crystal structure prediction using graph neural combinatorial optimization","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2607.06532","title":"GraphBU: MILP Instance Generation with Graph-Native Block Units","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2609.33879","title":"DCEmbed: Scalable Optimization over Neural Surrogates","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}