Introduction
This web page is dedicated to our CDCL-Based SAT solver, named Topk-LC-Glucose. It is based on Glucose 3.0 enhanced by a learned clauses database deletion strategy using Topk (unsigned int) undominated learned clauses. Our goal is to take advantage of several relevant learned clauses deletion strategies by seeking a compromise between them through a dominance relationship. In [JE'2017] , we proposed to search at each reduction step of the learned clauses database, the current reference learned clause according to a set of relevant measures and to delete all the learned clauses dominated by this clause. In this approach, we extend this idea by considering instead k current reference learned clauses i.e the Topk undominated learned clauses at each cleaning step and delete all the learned clauses dominated by at least one of the Topk undominated learned clauses.
Topk-LC-Glucose
Topk-LC-Glucose is a CDCL SAT solver based on Glucose 3.0. It is obtained by selecting at each cleaning step of the learned clauses database the Topk current undominated learned clauses (the k first Reference Learned Clauses) according to a set of learned clauses relevant measures, and to delete all the learned clauses dominated by at least one of the Topk current undominated learned clauses. Topk-LC-Glucose avoids another non-trivial problem which is the amount of learned clauses to be deleted at each reduction step of the learned clauses database by dynamically determining the number of learned clauses to delete at each cleaning step. An implementation of Topk-LC-Glucose integrating three learned clauses relevant measures (Size, LBD, CSIDS) is made avaibale here.
Publications
- [JE'2017] Jerry Lonlac , Engelbert Mephu Nguifo. Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship. In coRR, arXiv:1705.10898, May 2017.
Aknowledgments
Our research has been supported by Auvergne-Rhône-Alpes region and European Union through the European Regional Development Fund (ERDF) and University Clermont Auvergne. We also thank CRIL (Lens Computer Science Research Lab) for providing them computing server and the authors of Glucose SAT solver for putting the source code available.