Between restarts and backjumps

Authors:
Antonio Ramos;Peter van der Tak;Marijn J. H. Heule
Affiliations:
Department of Software Technology, Delft University of Technology, The Netherlands;Department of Software Technology, Delft University of Technology, The Netherlands;Department of Software Technology, Delft University of Technology, The Netherlands
Venue:
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Year:
2011

Citing 9
Cited 1

GRASP: A Search Algorithm for Propositional Satisfiability

IEEE Transactions on Computers
Chaff: engineering an efficient SAT solver

Proceedings of the 38th annual Design Automation Conference
Dynamic backtracking

Journal of Artificial Intelligence Research
Search in a small world

IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
The effect of restarts on the efficiency of clause learning

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A lightweight component caching scheme for satisfiability solvers

SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
On the power of clause-learning SAT solvers with restarts

CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Assignment stack shrinking

SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
A complete random jump strategy with guiding paths

SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing

Off the trail: re-examining the CDCL algorithm

SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel technique that significantly reduces the computational costs to perform a restart in conflict-driven clause learning (CDCL) solvers. Our technique exploits the observation that CDCL solvers make many redundant propagations after a restart. It efficiently predicts which decisions will be made after a restart. This prediction is used to backtrack to the first level at which heuristics may select a new decision rather than performing a complete restart. In general, the number of conflicts that are encountered while solving a problem can be reduced by increasing the restart frequency, even though the solving time may increase. Our technique counters the latter effect. As a consequence CDCL solvers will favor more frequent restarts.