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Expand Up @@ -56,3 +56,16 @@ If you find our work useful in your research, please consider citing:
Some of the code and datasets are derived from works in historical literature, as annotated in the appendix of the paper.
When you use this benchmark dataset, please comply with the terms of use specified in Appendix C.

## Our Systematic Benchmark Works

We are systematically building a foundational framework for ML4CO with a collection of resources that complement each other in a cohesive manner.

* [Awesome-ML4CO](https://github.com/Thinklab-SJTU/awesome-ml4co), a curated collection of literature in the ML4CO field, organized to support researchers in accessing both foundational and recent developments.

* [ML4CO-Kit](https://github.com/Thinklab-SJTU/ML4CO-Kit), a general-purpose toolkit that provides implementations of common algorithms used in ML4CO, along with basic training frameworks, traditional solvers and data generation tools. It aims to simplify the implementation of key techniques and offer a solid base for developing machine learning models for COPs.

* [ML4TSPBench](https://github.com/Thinklab-SJTU/ML4TSPBench): a benchmark focusing on exploring the TSP for representativeness. It offers a deep dive into various methodology designs, enabling comparisons and the development of specialized algorithms.

* [ML4CO-Bench-101](https://github.com/Thinklab-SJTU/ML4CO-Bench-101): a benchmark that categorizes neural combinatorial optimization (NCO) solvers by solving paradigms, model designs, and learning strategies. It evaluates applicability and generalization of different NCO approaches across a broad range of combinatorial optimization problems to uncover universal insights that can be transferred across various domains of ML4CO.

* [PredictiveCO-Benchmark](https://github.com/Thinklab-SJTU/PredictiveCO-Benchmark): a benchmark for decision-focused learning (DFL) approaches on predictive combinatorial optimization problems.