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Awesome machine learning for combinatorial optimization papers.
| Date | Stars |
|---|---|
| 2026-07-31 | 2154 |
| 2026-08-05 | 2159 |
| 2026-08-06 | 2159 |
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# Awesome Machine Learning for Combinatorial Optimization Resources We would like to maintain a list of resources that utilize machine learning technologies to solve combinatorial optimization problems. We mark work contributed by [Thinklab](http://thinklab.sjtu.edu.cn) with ⭐. *Maintained by members in SJTU-Thinklab: Chang Liu, Runzhong Wang, Jiayi Zhang, Zelin Zhao, Haoyu Geng, Tianzhe Wang, Wenxuan Guo, Wenjie Wu, Nianzu Yang, Ziao Guo, Yang Li, Hao Xiong, Jiale Ma, Wenzheng Pan and Junchi Yan. We also thank [all contributers from the community](https://github.com/Thinklab-SJTU/awesome-ml4co/graphs/contributors)!* *We are looking for post-docs interested in machine learning especially for learning combinatorial solvers, dynamic graphs, and reinforcement learning. Please send your up-to-date resume via yanjunchi AT sjtu.edu.cn.* ## [Content](#content) <table> <tr><td colspan="2"><a href="#survey-papers">1. Survey</a></td></tr> <tr><td colspan="2"><a href="#problems">2. Problems</a></td></tr> <tr> <td> <a href=#travelling-salesman-problem>2.1 Travelling Salesman Problem (TSP)</a></td> <td> <a href=#job-shop-scheduling-problem>2.2 Job Shop Scheduling Problem (JSSP)</a></td> </tr> <tr> <td> <a href=#flow-shop-problem>2.3 Flow Shop Problem (FSP)</a></td> <td> <a href=#sorting-&-ranking>2.4 Sorting & Ranking (Sort&Rank)</a></td> </tr> <tr> <td> <a href=#graph-matching>2.5 Graph Matching (GM)</a></td> <td> <a href=#quadratic-assignment-problem>2.6 Quadratic Assignment Problem (QAP)</a></td> </tr> <tr> <td> <a href=#portfolio-optimization>2.7 Portfolio Optimization (PortOpt)</a></td> <td> <a href=#maximal-cut>2.8 Maximal Cut</a></td> </tr> <tr> <td> <a href=#vehicle-routing-problem>2.9 Vehicle Routing Problem (VRP)</a></td> <td> <a href=#maximum-independent-set>2.10 Maximum Independent Set</a></td> </tr> <tr> <td> <a href=#generalization>2.11 Generalization</a></td> <td> <a href=#orienteering-problem>2.12 Orienteering Problem (OP)</a></td> </tr> <tr> <td> <a href=#knapsack>2.13 Knapsack</a></td> <td> <a href=#boolean-satisfiability>2.14 Boolean Satisfiability (SAT)</a></td> </tr> <tr> <td> <a href=#computing-resource-allocation>2.15 Computing Resource Allocation</a></td> <td> <a href=#bin-packing-problem>2.16 Bin Packing Problem (BPP)</a></td> </tr> <tr> <td> <a href=#graph-edit-distance>2.17 Graph Edit Distance (GED)</a></td> <td> <a href=#hamiltonian-cycle-problem>2.18 Hamiltonian Cycle Problem (HCP)</a></td> </tr> <tr> <td> <a href=#graph-coloring>2.19 Graph Coloring</a></td> <td> <a href=#maximal-common-subgraph>2.20 Maximal Common Subgraph (MCS)</a></td> </tr> <tr> <td> <a href=#influence-maximization>2.21 Influence Maximization</a></td> <td> <a href=#max-clique>2.22 Max Clique</a></td> </tr> <tr> <td> <a href=#mixed-integer-programming>2.23 Mixed Integer Programming (MIP)</a></td> <td> <a href=#causal-discovery>2.24 Causal Discovery</a></td> </tr> <tr> <td> <a href=#game-theoretic-semantics>2.25 Game Theoretic Semantics</a></td> <td> <a href=#differentiable-optimization>2.26 Differentiable Optimization</a></td> </tr> <tr> <td> <a href=#car-dispatch>2.27 Car Dispatch</a></td> <td> <a href=#electronic-design-automation>2.28 Electronic Design Automation (EDA)</a></td> </tr> <tr> <td> <a href=#conjunctive-query-containment>2.29 Conjunctive Query Containment</a></td> <td> <a href=#virtual-network-embedding>2.30 Virtual Network Embedding (VNE)</a></td> </tr> <tr> <td> <a href=#predict+optimize>2.31 Predict+Optimize</a></td> <td> <a href=#optimal-power-flow>2.32 Optimal Power Flow</a></td> </tr> <tr> <td> <a href=#facility-location-problem>2.33 Facility Location Problem (FLP)</a></td> <td> <a href=#combinatorial-drug-recommendation>2.34 Combinatorial Drug Recommendation</a></td> </tr> <tr> <td> <a href=#stochastic-comb
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Zelin Zhao · United States
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Junyoung Park · United States
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Yuma Ichikawa · Fujitsu Limited · Japan
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Haoran Ye · Peking University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:201c20565a42dc67, llm:repository description and README: 'Awesome machine learning for combinatorial optimization papers.' Topics: combinatorial-optimization, machine-learning, operations-research, paper-list. It's a curated list of ML for combinatorial optimization resources/papers.
matched fp:201c20565a42dc67, llm:repository description and README: 'Awesome machine learning for combinatorial optimization papers.' Topics: combinatorial-optimization, machine-learning, operations-research, paper-list. It's a curated list of ML for combinatorial optimization resources/papers.