MatNet

This repository provides a reference implementation of MatNet and saved trained models as described in the paper:<br>

Matrix Encoding Networks for Neural Combinatorial Optimization <br> (NeurIPS 2021, accepted)<br> https://arxiv.org/abs/2106.11113

The code is written using Pytorch.<br> <br>

Getting Started

We provide codes for two CO (combinatorial optimization) problems:

• Asymmetric Traveling Salesman Problem (ATSP)
• Flexible Flow Shop Problem (FFSP)

Basic Usage

For both ATSP_MatNet and FFSP_MatNet, <br>

i. To train a model,

python3 train.py

train.py contains parameters you can modify. <br> At the moment, it is set to train N=20 problems.

ii. To test a model,

python3  test.py

You can specify the model as a parameter contained in test.py. <br> At the moment, it is set to use the saved model (N=20) we have provided (in "result" folder), but you can easily use the one you have trained from running train.py.

To test for the N=50 model, make sure that saved problem files exist in the path (see below for Datasets). Also, modify test.py so that

• all "20"'s are changed to "50"
• "path" and "epoch" in the "tester_params" are correctly pointing to the saved model
• test batch size is decreased (by a factor of something like 4)

Saved Trained Models

Trained models for ATSP are provided with the codes.<br> However, the sizes of the trained model files for FFSP-MatNet are too large to upload here. So we provide them as links below.

• MatNet_saved_models_FFSP.zip (size: 358MB) <-- a zip file containing all three models
  • MatNet_saved_models_N100 (size: 119MB)
  • MatNet_saved_models_N50 (size: 119MB)
  • MatNet_saved_models_N20 (size: 119MB)

Datatsets

Test datasets for larger N (N=50, N=100) problems are given as links below.<br> Download the dataset and add them to the "data" directories under ATSP and FFSP folders. <br>

• MatNet_dataset_ATSP (size: 374MB)
• MatNet_dataset_FFSP (size: 19.6MB)

Used Libraries

python v3.7.6 <br> torch==1.7.0 <br>