DataBack: Dataset of SAT Formulas and Backbone Variable Phases
What is DataBack
DataBack is a dataset that consists of 120,286 SAT formulas (in CNF format), each labeled with the phases of its backbone variables.
DataBack contains two distinct subsets: the pre-training set, named DataBack-PT, and the fine-tuning set, named DataBack-FT, for pre-training and fine-tuning our NeuroBack model, respectively. To learn more about NeuroBack and DataBack, please refer to our NeuroBack paper.
The state-of-the-art backbone extractor, CadiBack, has been employed to extract the backbone variable phases. To learn more about CadiBack, please refer to the CadiBack paper.
Directory Structure
|- original # Original CNF formulas and their backbone variable phases
| |- cnf_pt.tar.gz # CNF formulas for pre-training
| |- bb_pt.tar.gz # Backbone phases for pre-training formulas
| |- cnf_ft.tar.gz # CNF formulas for fine-tuning
| |- bb_ft.tar.gz # Backbone phases for fine-tuning formulas
|
|- dual # Dual CNF formulas and their backbone variable phases
| |- d_cnf_pt.tar.gz # Dual CNF formulas for pre-training
| |- d_bb_pt.tar.gz # Backbone phases for dual pre-training formulas
| |- d_cnf_ft.tar.gz # Dual CNF formulas for fine-tuning
| |- d_bb_ft.tar.gz # Backbone phases for dual fine-tuning formulas
File Naming Convention
In the original directory, each CNF tar file (cnf_*.tar.gz) contains compressed CNF files named: [cnf_name].[compression_format], where [compression_format] could be bz2, lzma, xz, gz, etc. Correspondingly, each backbone tar file (bb_*.tar.gz) comprises compressed backbone files named: [cnf_name].backbone.xz. It is important to note that a compressed CNF file will always share its [cnf_name] with its associated compressed backbone file.
For dual formulas and their corresponding backbone files, the naming convention remains consistent, but with an added d_ prefix.
Format of the Extracted Backbone File
The extracted backbone file (*.backbone) adheres to the output format of CadiBack.
References
If you use DataBack in your research, please kindly cite the following papers.
@article{wang2023neuroback,
author = {Wang, Wenxi and
Hu, Yang and
Tiwari, Mohit and
Khurshid, Sarfraz and
McMillan, Kenneth L. and
Miikkulainen, Risto},
title = {NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks},
journal={arXiv preprint arXiv:2110.14053},
year={2021}
}
@inproceedings{biere2023cadiback,
title={CadiBack: Extracting Backbones with CaDiCaL},
author={Biere, Armin and Froleyks, Nils and Wang, Wenxi},
booktitle={26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
year={2023},
organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}
}
Contributors
Wenxi Wang (wenxiw@utexas.edu), Yang Hu (huyang@utexas.edu)