Neural TSP Solver with Progressive Distillation
Authors: Dongxiang Zhang, Ziyang Xiao, Yuan Wang, Mingli Song, Gang Chen
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach establishes clear advantages over existing encoder-decoder models in terms of training effectiveness and solution quality. |
| Researcher Affiliation | Academia | 1 College of Computer Science and Technology, Zhejiang University 2 School of Business, Singapore University of Social Sciences |
| Pseudocode | Yes | The pseudo code of our model training based on progressive distillation is depicted in Algorithm 1. |
| Open Source Code | Yes | The datasets and source code are provided in the supplementary materials. |
| Open Datasets | Yes | For datasets, we follow previous learning-to-generate models to use uniform distribution in two-dimensional space [0, 1]2. In addition, we examine the performance in the Euclidean TSP instances in the TSPLIB repository3. |
| Dataset Splits | No | No explicit train/validation/test dataset splits (e.g., percentages or sample counts) are provided. |
| Hardware Specification | Yes | All the experiments are conducted on a single GPU (NVIDIA Tesla V100 with 32GB memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | As to hyper-parameter setting, we set N = 100, C = 60, r = 0.4, β = 0.1 and T = 2000 for the training framework based on progressive distillation. The Transformer has 6-layers encoder and 1-layer decoder, both with 128 hidden units in each layer.For batch size, we set 512 for TSP150, 128 for TSP300 and 32 for TSP500. |