Unsupervised Learning for Solving the Travelling Salesman Problem
Authors: Yimeng Min, Yiwei Bai, Carla P. Gomes
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that UTSP outperforms the existing data-driven TSP heuristics. |
| Researcher Affiliation | Academia | Yimeng Min Dept. of Computer Science Cornell University Ithaca, NY, USA min@cs.cornell.edu Yiwei Bai Dept. of Computer Science Cornell University Ithaca, NY, USA bywbilly@gmail.com Carla P. Gomes Dept. of Computer Science Cornell University Ithaca, NY, USA gomes@cs.cornell.edu |
| Pseudocode | No | The paper describes its methods through prose and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Our dataset contains 2,000 samples for training and 1,000 samples for validation. We use the same test dataset in Fu et al. [2021]. |
| Dataset Splits | Yes | Our dataset contains 2,000 samples for training and 1,000 samples for validation. |
| Hardware Specification | Yes | All models are trained using Nvidia V100 GPU. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not specify any software names with version numbers for reproducibility (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | All the search-related parameters are listed in Table 4. M is the size of the candidate set of each city. K is the maximal number of edges we can remove in one action, and for each round of local search, we randomly select one number from the listed interval. T is the total number of actions we will try to expand one node. |