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Designing New Phase Selection Heuristics.
Arijit Shaw, and Kuldeep S. Meel.
In Proceedings of the International Conference on Theory and Applications of Satisfiability Testing (SAT), July 2020.
CDCL-based SAT solvers have transformed the field of automated reasoning owing to their demonstrated efficiency at handling problems arising from diverse domains. The success of CDCL solvers is owed to the design of clever heuristics that enable the tight coupling of different components. One of the core components is phase selection, wherein the solver, during branching, decides the polarity of the branch to be explored for a given variable. Most of the state-of-the-art CDCL SAT solvers employ phase-saving as a phase selection heuristic, which was proposed to address the potential inefficiencies arising from far-backtracking. In light of the emergence of chronological backtracking in CDCL solvers, we re-examine the efficiency of phase saving. Our empirical evaluation leads to a surprising conclusion: The usage of phase saving and random selection of polarity during chronological backtracking leads to indistinguishable runtime performance in terms of instances solved and PAR-2 score.We introduce Decaying Polarity Score (DPS) to capture the trend of the polarities attained by the variable, and upon observing lack of performance improvement due to DPS, we turn to a more sophisticated heuristic seeking to capture the activity of literals and the trend of polarities: Literal State Independent Decaying Sum (LSIDS). We find the 2019 winning SAT solver, Maple_LCM_Dist_ChronoBTv3, augmented with LSIDS solves 6 more instances while achieving a reduction of over 125 seconds in PAR-2 score, a significant improvement in the context of the SAT competition.
@inproceedings{SM20,
title={Designing New Phase Selection Heuristics},
author={Shaw, Arijit and Meel, Kuldeep S.},
booktitle=SAT,
month=jul,
bib2html_rescat={Solver Engineering},
bib2html_pubtype={Refereed Conference},
bib2html_dl_pdf={../Papers/sat20-sm.pdf},
code={https://github.com/meelgroup/duriansat},
year={2020},
abstract={CDCL-based SAT solvers have transformed the field of automated reasoning
owing to their demonstrated efficiency at handling problems arising from diverse
domains. The success of CDCL solvers is owed to the design of clever heuristics that
enable the tight coupling of different components. One of the core components is phase
selection, wherein the solver, during branching, decides the polarity of the branch to
be explored for a given variable. Most of the state-of-the-art CDCL SAT solvers employ
phase-saving as a phase selection heuristic, which was proposed to address the potential
inefficiencies arising from far-backtracking. In light of the emergence of chronological
backtracking in CDCL solvers, we re-examine the efficiency of phase saving. Our empirical
evaluation leads to a surprising conclusion: The usage of phase saving and random selection
of polarity during chronological backtracking leads to indistinguishable runtime performance
in terms of instances solved and PAR-2 score.
We introduce Decaying Polarity Score (DPS) to capture the trend of the polarities attained
by the variable, and upon observing lack of performance improvement due to DPS, we turn to
a more sophisticated heuristic seeking to capture the activity of literals and the trend of
polarities: Literal State Independent Decaying Sum (LSIDS). We find the 2019 winning SAT solver,
Maple_LCM_Dist_ChronoBTv3, augmented with LSIDS solves 6 more instances while achieving a reduction
of over 125 seconds in PAR-2 score, a significant improvement in the context of the SAT competition.
},
}
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