Thinklab-SJTU/awesome-ml4co
A curated collection of academic papers applying machine learning, reinforcement learning, and graph neural networks to combinatorial optimization problems.
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This repository aggregates research on using ML techniques to solve combinatorial optimization problems such as the traveling salesman problem, vehicle routing, job shop scheduling, and graph matching. Maintained by SJTU-Thinklab research group, it catalogs survey papers, benchmark problems, and learning-based solvers including deep reinforcement learning and graph neural network approaches. The list also covers generalization challenges in learned combinatorial solvers.
Frequently asked
- What is Thinklab-SJTU/awesome-ml4co?
- A curated collection of academic papers applying machine learning, reinforcement learning, and graph neural networks to combinatorial optimization problems.
- Is awesome-ml4co open source?
- Yes — Thinklab-SJTU/awesome-ml4co is an open-source project tracked on heatdrop.
- What language is awesome-ml4co written in?
- Thinklab-SJTU/awesome-ml4co is primarily written in Python.
- How popular is awesome-ml4co?
- Thinklab-SJTU/awesome-ml4co has 2.1k stars on GitHub.
- Where can I find awesome-ml4co?
- Thinklab-SJTU/awesome-ml4co is on GitHub at https://github.com/Thinklab-SJTU/awesome-ml4co.