Combinatorial Optimization

Overview

Combinatorial optimization studies how to choose the best arrangement, assignment, route, or schedule when decisions are discrete and constraints interact. Exact methods such as integer programming, constraint programming, SAT, dynamic programming, and branch-and-bound can certify optimality, while approximation algorithms and metaheuristics trade guarantees for speed on larger instances. Learning methods—including graph neural networks, reinforcement learning, and language-model-guided heuristic design—are increasingly paired with classical search to propose solutions, warm-start solvers, or select algorithms. A central challenge is making these methods scale and generalize without sacrificing feasibility, solution quality, or the guarantees that exact and approximation methods provide.

Latest papers

CardsList

386 papers47 papers in the last four weeks, up 47% on the four weeks before. 0.3% of all new papers.

  1. Learning to Approximate Uniform Facility Location via Graph Neural Networks

    Feb 13, 2026Chendi Qian, Christopher Morris, Stefanie Jegelka +1Graph Neural NetworksCombinatorial Optimization

  2. ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search

    Feb 10, 2026Alexey Kravatskiy, Valentin Khrulkov, Ivan OseledetsLarge Language Model-Guided Scientific DiscoveryEvolutionary Optimization

  3. Graph-Theoretic Formulation of Hamiltonian and Ground-State Sign Structures in Heisenberg Antiferromagnets

    Feb 4, 2026Mahmud Ashraf Shamim, Md Moshiur Rahman Raj, Mohamed Hibat-Allah +3Quantum Many-Body PhysicsCombinatorial Optimization

  4. Fair and Efficient Investment in Public Transportation

    Feb 3, 2026Martin Bullinger, Edith Elkind, Kassian KöckAlgorithmic FairnessCombinatorial Optimization

  5. Towards Solving the Gilbert-Pollak Conjecture via Large Language Models

    Jan 29, 2026Yisi Ke, Tianyu Huang, Yankai Shu +3Combinatorial OptimizationLarge Language Model-Guided Scientific Discovery

  6. Graph Neural Networks are Heuristics

    Jan 19, 2026Yimeng Min, Carla P. GomesTraveling Salesperson ProblemGraph Neural Networks

  7. Enabling Population-Based Architectures for Neural Combinatorial Optimization

    Jan 13, 2026Andoni Irazusta Garmendia, Josu Ceberio, Alexander MendiburuMetaheuristic OptimizationCombinatorial Optimization

  8. Resource-constrained Project Scheduling with Time-of-Use Energy Tariffs and Machine States: A Logic-based Benders Decomposition Approach

    Jan 10, 2026Corentin Juvigny, Antonín Novák, Jan Mandík +1Resource-Constrained Project SchedulingCombinatorial Optimization

  9. A Fast and Effective Method for Euclidean Anticlustering: The Assignment-Based-Anticlustering Algorithm

    Jan 9, 2026Philipp Baumann, Olivier Goldschmidt, Dorit S. Hochbaum +1ClusteringCombinatorial Optimization

  10. Neural Minimum Weight Perfect Matching for Quantum Error Codes

    Jan 1, 2026Yotam Peled, David Zenati, Eliya NachmaniNeural DecodingQuantum Error Correction

  11. Leveraging Discrete Function Decomposability for Scientific Design

    Nov 4, 2025James C. Bowden, Sergey Levine, Jennifer ListgartenAI-Assisted Scientific ResearchCombinatorial Optimization

  12. Arithmetic Circuits and Neural Networks for Regular Matroids

    Nov 4, 2025Christoph Hertrich, Stefan Kober, Georg LohoCombinatorial Optimization

  13. Column Generation with Domain-Independent Dynamic Programming

    Oct 16, 2025Ryo Kuroiwa, Edward LamDynamic ProgrammingCombinatorial Optimization

  14. Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm

    Sep 29, 2025Yunhao Liang, Pujun Zhang, Yuan Qu +3Graph SSLCombinatorial Optimization

  15. Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing

    Sep 26, 2025Liangyu Ding, Chenghan Wu, Guokai Li +1Graph Neural NetworksCombinatorial Optimization

  16. Recurrent State Encoders for Efficient Neural Combinatorial Optimization

    Sep 5, 2025Tim Dernedde, Daniela Thyssens, Lars Schmidt-ThiemeRecurrent Neural NetworksCombinatorial Optimization

  17. Instance-Aware Algorithm Selection for Maximum Clique via a Dual-Channel Graph Neural Architecture

    Aug 11, 2025Xiang Li, Shanshan Wang, Chenglong XiaoAlgorithm SelectionCombinatorial Optimization

  18. Physics Inspired Neural Networks Enable Efficient Graph Coloring

    Aug 2, 2024Lorenzo Colantonio, Andrea Cacioppo, Federico Scarpati +3Combinatorial OptimizationNeural Combinatorial Optimization

  19. Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement

    Jun 9, 2024Ahmed AttiaBlack-Box OptimizationCombinatorial Optimization

  20. GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing

    Jun 9, 2024Tianyang Xue, Lin Lu, Yang Liu +5Combinatorial OptimizationConstrained Optimization

  21. Leader Reward for POMO-Based Neural Combinatorial Optimization

    May 22, 2024Chaoyang Wang, Pengzhi Cheng, Jingze Li +1Traveling Salesperson ProblemReward Shaping

  22. A short review on the maximum clique problem algorithms with classical, AI, and quantum methods

    Mar 13, 2024Raffaele Marino, Lorenzo Buffoni, Bogdan ZavalnijCombinatorial Optimization

  23. Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary

    Feb 2, 2023Alexander Lindermayr, Nicole Megow, Martin RappCombinatorial OptimizationLearning-Augmented Algorithms