Boosting the Performance of SLS and CDCL Solvers by Preprocessor Tuning
Citations Over Time
Abstract
Preprocessing techniques are crucial for SAT solvers when it comes to reaching state- of-the-art performance as it was shown by the results of the last SAT Competitions. The usefulness of a preprocessing technique depends highly on its own parameters, on the in- stances on which it is applied and on the used solver. In this paper we first give an extended analysis of the performance gain reached by using different preprocessing techniques in- dividually in combination with CDCL solvers on application instances and SLS solvers on crafted instances. Further, we provide an analysis of combinations of preprocessing techniques by means of automated algorithm configuration, where we search for optimal preprocessor configurations for different scenarios. Our results show that the performance of CDCL and especially of SLS solvers can be further improved when using appropriate preprocessor configurations. The solvers augmented with the best found preprocessing configurations outperform the original solvers on the instances from the SAT Challenge 2012, achieving new state-of-the-art results.
Related Papers
- → Road Accident Data Analysis: Data Preprocessing for Better Model Building(2019)17 cited
- → Influence of Data Preprocessing(2016)24 cited
- → Data preprocessing based on missing value and discretisation(2020)2 cited
- → Study on Data Preprocessing for Daylight Climate Data(2012)
- Research and Application on Spatial Data Preprocessing Techniques in Logistics Area(2010)