---
license: apache-2.0
---
# ML4TSP Pretrained Files 2024-02-02

**This repository primarily stores the pretrained files for ML4TSP.** 
**All the files in this repository have a last update date prior to 2024-02-02.**

## 1. Dataset

#### 1.1 Supervised Learning Training Dataset

**File Naming Convention:** tsp{nodes_num}\_{distribution}\_{solver(params)}\_{size}.txt

| Problem Scale | Train File |
|:-------------:|:----------:|
| TSP50 | [tsp50_uniform_lkh_5k_1.28m.txt](https://huggingface.co/datasets/ML4CO/ML4TSPDataset/blob/main/tsp50_uniform_lkh_5k_1.28m.txt) | LKH-5K |
| TSP100 | [tsp100_uniform_lkh_5k_1.28m.txt](https://huggingface.co/datasets/ML4CO/ML4TSPDataset/blob/main/tsp100_uniform_lkh_5k_1.28m.txt) | LKH-5K |
| TSP500 | [tsp500_uniform_lkh_50k_128k.txt](https://huggingface.co/datasets/ML4CO/ML4TSPDataset/blob/main/tsp500_uniform_lkh_50k_128k.txt) | LKH-50k |
| TSP1000 | [tsp1000_uniform_lkh_100k_64k.txt](https://huggingface.co/datasets/ML4CO/ML4TSPDataset/blob/main/tsp1000_uniform_lkh_100k_64k.txt) | LKH-100k |

#### 1.2 Test Dataset (Uniform)

**File Naming Convention:** tsp{nodes_num}\_{solver(params)}\_{avg_length}.txt

| Problem Scale | Test | Distribution | Size |
|:-------------:|:----:|:------------:|:----:|
| TSP50 | [tsp50_concorde_5.68759.txt](https://huggingface.co/datasets/ML4CO/TSPUniformDataset/blob/main/tsp_uniform/tsp50_concorde_5.68759.txt) | uniform | 1280 |
| TSP100 | [tsp100_concorde_7.75585.txt](https://huggingface.co/datasets/ML4CO/TSPUniformDataset/blob/main/tsp_uniform/tsp100_concorde_7.75585.txt) | uniform | 1280 |
| TSP500 | [tsp500_concorde_16.54581.txt](https://huggingface.co/datasets/ML4CO/TSPUniformDataset/blob/main/tsp_uniform/tsp500_concorde_16.54581.txt) | uniform | 128 |
| TSP1000 | [tsp1000_concorde_23.11812.txt](https://huggingface.co/datasets/ML4CO/TSPUniformDataset/blob/main/tsp_uniform/tsp1000_concorde_23.11812.txt) | uniform | 128 |
| TSP10000 | [tsp10000_concorde_large_71.84185.txt](https://huggingface.co/datasets/ML4CO/TSPUniformDataset/blob/main/tsp_uniform/tsp10000_concorde_large_71.84185.txt) | uniform | 16 |

## 2. Model Parameters

#### 2.1 NAR Model Parameters

| Pretrained File | Net Type | Layer | Embed | Hidden | Out | Epoch(select)|
|:----------------|:--------:|:-----:|:-----:|:------:|:---:|:------------:|
| tsp50_diffusion.pt | gnn | 12 | 128 | 256 | 2 | 100(?) |
| tsp50_dimes.pt | gnn | 12 | 128 | 256 | 2 | 405/500step |
| tsp50_gnn.pt | gnn | 12 | 128 | 256 | 2 | 100(96) |
| tsp50_gnn_wise.pt | gnn | 12 | 128 | 256 | 2 | 100(76) |
| tsp50_gnn4reg.pt | gnn | 12 | 128 | 256 | 2 | 100(62) |
| tsp50_us.pt | sag | 3 | 64 | 64 | 50 | 100(3) |
| tsp100_diffusion.pt | gnn | 12 | 128 | 256 | 2 | 50(?) |
| tsp100_dimes.pt | gnn | 12 | 128 | 256 | 2 | 240/250step |
| tsp100_gnn.pt | gnn | 12 | 128 | 256 | 2 | 50(50) |
| tsp100_gnn_wise.pt | gnn | 12 | 128 | 256 | 2 | 50(48) |
| tsp100_gnn4reg.pt | gnn | 12 | 128 | 256 | 2 | 50(18) |
| tsp100_us.pt | sag | 3 | 64 | 64 | 50 | 50(3) |
| tsp500_diffusion.pt | gnn | 12 | 128 | 256 | 2 | 50(?) |
| tsp500_dimes.pt | gnn | 12 | 128 | 256 | 2 | 66/100step |
| tsp500_gnn.pt | gnn | 12 | 128 | 256 | 2 | 50(22) |
| tsp500_gnn_wise.pt | gnn | 12 | 128 | 256 | 2 | 50(14) |
| tsp1000_diffusion.pt | gnn | 12 | 128 | 256 | 2 | 50(?) |
| tsp1000_gnn_wise.pt | gnn | 12 | 128 | 256 | 2 | 50(44) |


#### 2.2 AR Model Parameters

| Pretrained File | Net Type | Layer | Embed | Heads | Baseline | Epoch(select)|
|:----------------|:--------:|:-----:|:-----:|:-----:|:--------:|:------------:|
| tsp50_am.pt | gat | 3 | 128 | 8 | rollout | 360(360) |
| tsp50_pomo.pt | gat | 3 | 128 | 8 | shared | 360(360) |
| tsp50_symnco.pt | gat | 3 | 128 | 8 | no | 360(360) |
| tsp100_am.pt | gat | 3 | 128 | 8 | rollout | 500(500) |
| tsp100_pomo.pt | gat | 3 | 128 | 8 | shared | 100(100) |
| tsp100_symnco.pt | gat | 3 | 128 | 8 | no | 330(329) |

## 3. Training Details

### 3.1 NAR Model

* lr_scheduler: "cosine-decay" (torch.optim.lr_scheduler.CosineAnnealingLR)
* learning-rate: 0.003(initial)
* optimizer: "AdamW" (torch.optim.AdamW)
  
### 3.2 AR Model

* lr_scheduler: None
* learning-rate: 0.0001(fix)
* optimizer: "Adam" (torch.optim.Adam)