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https://github.com/xuzijian629/combopt-zero
A reinforcement learning based solver for combinatorial problems
https://github.com/xuzijian629/combopt-zero
alphago-zero feedback-vertex-set graph-neural-networks independent-sets maxcut maximum-clique reinforcement-learning vertex-cover
Last synced: about 2 months ago
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A reinforcement learning based solver for combinatorial problems
- Host: GitHub
- URL: https://github.com/xuzijian629/combopt-zero
- Owner: xuzijian629
- License: mit
- Created: 2020-05-03T09:47:07.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-22T01:54:25.000Z (over 2 years ago)
- Last Synced: 2023-03-05T16:53:45.755Z (almost 2 years ago)
- Topics: alphago-zero, feedback-vertex-set, graph-neural-networks, independent-sets, maxcut, maximum-clique, reinforcement-learning, vertex-cover
- Language: C++
- Homepage: https://arxiv.org/abs/1905.11623
- Size: 1.55 MB
- Stars: 36
- Watchers: 3
- Forks: 9
- Open Issues: 2
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.md
Awesome Lists containing this project
README
# CombOpt Zero
CombOpt Zero is a general-purpose solver based on AlphaGo Zero for combinatorial problems on graphs.
Paper: [Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero](https://arxiv.org/abs/1905.11623)
You can try *MinimumVertexCover*, *MaximumIndependentSet*, *FeedbackVertexSet*, *MaxCut* and *MaximumClique*, by running the code in this repository.
## Try on Docker
Install [Docker](https://docs.docker.com/get-docker/) and just run `docker/install.sh`, `docker/train.sh` and `docker/eval.sh`!
#### Note
- By default, it solves *MaximumClique*
- Change `docker/config.sh` and `{problem}/config.sh` for other settings
- Hyperparameters are modified so that the training and evaluation can be executed quickly on laptops without GPUs
- But still, **it will obtain pretty good solutions for real-world graphs of thousands of nodes even if trained for only a few minutes** (Try and check it by yourself!)
- `docker/train.sh` may yield some errors, possibly due to the file system of Docker. Please refer to [FAQs](https://github.com/xuzijian629/combopt-zero/wiki/FAQs).
## Build and Run
If you just want to try on docker, please ignore this section.
1. Download LibTorch from https://pytorch.org/
Download version `1.3.0`. Newer version may cause errors. If you use Linux, download `Pre-cxx11 ABI` version.
2. Build library
Please also refer to `docker/install.sh` if you have some problem.
```bash
$ cd max-clique/lib
$ mkdir build
$ cd build
$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
$ make
```
3. Generate scripts
First, modify hyperparameters and other parameters in `{problem}/config.sh`.
Then,
```bash
$ cd max-clique
# create two scripts for training and evalution, named t_sample.sh and e_sample.sh, based on config.sh
$ echo sample | python script_generator.py
```
4. Start training
You can terminate the training anytime. If you want to restart the training, just run the same command again.
Model files and temporary files are stored in `{problem}/results/{configuration}/`.
```bash
$ cd max-clique
$ ./t_sample.sh
```
5. Start evaluation
```bash
$ cd max-clique
$ ./e_sample.sh
```
## Dataset
All the test graphs used in our experiments are in `test_graphs/`. Some of them are collected from [Dimacs Vertex Cover instances](https://turing.cs.hbg.psu.edu/txn131/vertex_cover.html) and http://networkrepository.com/.
## Links
- Prototype for *MaximumIndependentSet* in Python: https://github.com/knshnb/MIS_solver
## Cite
Please cite [our paper](https://arxiv.org/abs/1905.11623) if you use our code in your work:
```
@article{Xu/Abe/2020,
title={Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero},
author={Zijian Xu and Kenshin Abe and Issei Sato and Masashi Sugiyama},
journal={arXiv preprint arXiv:1905.11623},
year={2020}
}
```