Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Authors: Minsu Kim, Junyoung Park, Jinkyoo Park
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section provides the experimental results of Sym-NCO for TSP, CVRP, PCTSP, and OP. |
| Researcher Affiliation | Academia | Minsu Kim Junyoung Park Jinkyoo Park Korea Advanced Institute of Science and Technology (KAIST) Dept. Industrial & Systems Engineering EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/alstn12088/Sym-NCO. |
| Open Datasets | Yes | Dataset and Computing Resources. We use the benchmark dataset [21] to evaluate the performance of the solvers. |
| Dataset Splits | No | The paper mentions using a 'benchmark dataset [21]' and shows 'Validation Cost' in figures, implying the use of validation data. However, it does not explicitly state the dataset splits (e.g., percentages or sample counts) within the paper's text. |
| Hardware Specification | Yes | To train the neural solvers, we use Nvidia A100 GPU. To evaluate the inference speed, we use an Intel Xeon E5-2630 CPU and Nvidia RTX2080Ti GPU to make fair comparisons with the existing methods as proposed in [26]. |
| Software Dependencies | No | The paper mentions the use of 'neural solvers' but does not specify any software dependencies (e.g., programming languages, libraries, or frameworks) with version numbers. |
| Experiment Setup | Yes | Hyperparameters. We apply Sym-NCO to POMO, AM, and Pointer Net. To make fair comparisons, we use the same network architectures and training-related hyperparameters from their original papers to train their Sym-NCO-augmented models. Please refer to Appendix Appendix C.1 for more details. |