mlsanityData to 7 Oct 2026

Learning to Perform Local Rewriting for Combinatorial Optimization

arXiv 1810.00337 ↗First posted 30 Sep 2018cs.LG cs.AI stat.MLcited by 183

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Abstract (arXiv)

Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a region-picking and a rule-picking component, each parameterized by a neural network trained with actor-critic methods in reinforcement learning. NeuRewriter captures the general structure of combinatorial problems and shows strong performance in three versatile tasks: expression simplification, online job scheduling and vehicle routing problems. NeuRewriter outperforms the expression simplification component in Z3; outperforms DeepRM and Google OR-tools in online job scheduling; and outperforms recent neural baselines and Google OR-tools in vehicle routing problems.

Key facts. Cited by 183 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 30; 2022: 36; 2023: 34; 2024: 30; 2025: 32; 2026: 21.

183 papers.

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  1. 2023Large Language Models as Optimizerscited by 830
  2. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  3. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 140
  4. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 127
  5. 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 118
  6. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 108
  7. 2021Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformercited by 108
  8. 2021NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problemcited by 102
  9. 2021Efficient Active Search for Combinatorial Optimization Problemscited by 99
  10. 2021Deep Policy Dynamic Programming for Vehicle Routing Problemscited by 94
  11. 2021Matrix Encoding Networks for Neural Combinatorial Optimizationcited by 89
  12. 2021Learning Collaborative Policies to Solve NP-hard Routing Problemscited by 86
38 more
  1. 2022Simulation-guided Beam Search for Neural Combinatorial Optimizationcited by 84
  2. 2023Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Optcited by 81
  3. 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimizationcited by 77
  4. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 70
  5. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 69
  6. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 68
  7. 2022Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillationcited by 61
  8. 2022Winner Takes It All: Training Performant RL Populations for Combinatorial Optimizationcited by 57
  9. 2022Pareto Set Learning for Neural Multi-objective Combinatorial Optimizationcited by 56
  10. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  11. 2023Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problemcited by 49
  12. 2024Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalizationcited by 49
  13. 2022A Survey for Solving Mixed Integer Programming via Machine Learningcited by 44
  14. 2025Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimizationcited by 43
  15. 2023Combinatorial Optimization with Policy Adaptation using Latent Space Searchcited by 41
  16. 2023Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetscited by 40
  17. 2024GOAL: A Generalist Combinatorial Optimization Agent Learnercited by 39
  18. 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learningcited by 37
  19. 2025Rethinking Light Decoder-based Solvers for Vehicle Routing Problemscited by 36
  20. 2021Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learningcited by 35
  21. 2021Learning Large Neighborhood Search Policy for Integer Programmingcited by 35
  22. 2024PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimizationcited by 35
  23. 2022Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxationcited by 32
  24. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 32
  25. 2021ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningcited by 31
  26. 2021Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problemcited by 31
  27. 2022Learning to Solve Routing Problems via Distributionally Robust Optimizationcited by 31
  28. 2021Accelerating Quadratic Optimization with Reinforcement Learningcited by 30
  29. 2022Efficient Neural Neighborhood Search for Pickup and Delivery Problemscited by 29
  30. 2024Learning to Handle Complex Constraints for Vehicle Routing Problemscited by 29
  31. 2021Equality Saturation for Tensor Graph Superoptimizationcited by 27
  32. 2023ASP: Learn a Universal Neural Solver!cited by 27
  33. 2021A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphscited by 26
  34. 2024Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problemscited by 24
  35. 2023Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimizationcited by 23
  36. 2024Self-Labeling the Job Shop Scheduling Problemcited by 23
  37. 2024Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectivescited by 23
  38. 2021Analytics and Machine Learning in Vehicle Routing Researchcited by 22

50 most cited of 183 All 183 (JSON)