Collaboration! Towards Robust Neural Methods for Routing Problems
Authors: Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the effectiveness and versatility of CNF in defending against various attacks across different neural VRP methods. |
| Researcher Affiliation | Academia | Jianan Zhou Nanyang Technological University jianan004@e.ntu.edu.sg Yaoxin Wu Eindhoven University of Technology y.wu2@tue.nl Zhiguang Cao Singapore Management University zgcao@smu.edu.sg Wen Song Shandong University wensong@email.sdu.edu.cn Jie Zhang, Zhiqi Shen Nanyang Technological University {zhangj,zqshen}@ntu.edu.sg |
| Pseudocode | Yes | We present the pseudocode of CNF in Algorithm 1, and elaborate each part in the following subsections. |
| Open Source Code | Yes | The source code is available at https://github.com/Royal Skye/Routing-CNF. |
| Open Datasets | Yes | We further evaluate all neural methods on the real-world benchmark datasets, such as TSPLIB [58] and CVRPLIB [67]. |
| Dataset Splits | No | We report the average optimality (opt.) gap over 1000 test instances. |
| Hardware Specification | Yes | All experiments are conducted on a machine with NVIDIA V100S-PCIE cards and Intel Xeon Gold 6226 CPU at 2.70GHz. |
| Software Dependencies | No | No specific version numbers for key software components or libraries (e.g., PyTorch version, Python version) are provided for their own method's implementation. |
| Experiment Setup | Yes | Adam optimizer is used with the learning rate of 1e 4, the weight decay of 1e 6 and the batch size of B = 64. To achieve full convergence, we pretrain the model on 300M and 100M clean instances for TSP200 and CVRP200, respectively. After obtaining the pretrained model, we use it to initialize M = 3 models, and further adversarially train them on 5M and 2.5M instances for n = 100 and n = 200, respectively. To save the GPU memory, we reduce the batch size to B = 32 for n = 200. The optimizer setting is the same as the one employed in the pretraining stage, except that the learning rate is decayed by 10 for the last 40% training instances. For neural methods, we use the greedy rollout with x8 instance augmentations following [38]. |