MixSATGEN: Learning Graph Mixing for SAT Instance Generation
Authors: Xinyan Chen, Yang Li, Runzhong Wang, Junchi Yan
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the superiority of our method with both resemblance in structure and hardness, and general applicability. |
| Researcher Affiliation | Academia | Xinyan Chen 12, Yang Li 1, Runzhong Wang1, Junchi Yan 12 1Department of Computer Science and Engineering & 2Zhiyuan College Shanghai Jiao Tong University {moss_chen,yanglily,runzhong.wang,yanjunchi}@sjtu.edu.cn |
| Pseudocode | Yes | The algorithms for the training and generating process of Mix SATGEN in Sec. 3 are presented in Alg. 1 and Alg. 2. |
| Open Source Code | Yes | https://github.com/Thinklab-SJTU/Mix SATGEN |
| Open Datasets | Yes | The real-world SAT instances are collected from SATLIB benchmark library (Hoos & Stützle, 2000) and SAT Competition 2021 (Balyo et al., 2021). |
| Dataset Splits | No | The paper discusses the datasets used and refers to 'training dataset' and 'test instances' but does not provide specific training/validation/test dataset splits, percentages, or absolute counts for reproducibility. |
| Hardware Specification | Yes | All the experiments are performed on a single GPU of Ge Force RTX 3090. The affinity matrix K is calculated on an AMD Ryzen Threadripper 3970X 32-core CPU with 128GB memory. |
| Software Dependencies | No | The paper mentions software tools and solvers like Ca Di Ca L, Kissat, SBVA-Cadical, and Pygmtools, but it does not specify exact version numbers for these or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We first finetune the pretrained model with 0.0001 learning rate, 200 epoches and 4 iterations of message passing (Selsam et al., 2019). |