Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Authors: Minsu Kim, Junyoung Park, Jinkyoo Park
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {min-su, Junyoungpark, jinkyoo.park}@kaist.ac.kr |
| 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. |