Collaboration! Towards Robust Neural Methods for Routing Problems
Authors: Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen
NeurIPS 2024 | Venue 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 EMAIL Yaoxin Wu Eindhoven University of Technology EMAIL Zhiguang Cao Singapore Management University EMAIL Wen Song Shandong University EMAIL Jie Zhang, Zhiqi Shen Nanyang Technological University EMAIL |
| 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]. |