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Clause Learning is one of the most important components of a conflict drivenclause learning (CDCL) SAT solver that is effective on industrial instances.Since the number of learned clauses is proved to be exponential in the worsecase, it is necessary to identify the most relevant clauses to maintain anddelete the irrelevant ones. As reported in the literature, several learnedclauses deletion strategies have been proposed. However the diversity in boththe number of clauses to be removed at each step of reduction and the resultsobtained with each strategy creates confusion to determine which criterion isbetter. Thus, the problem to select which learned clauses are to be removedduring the search step remains very challenging. In this paper, we propose anovel approach to identify the most relevant learned clauses without favoringor excluding any of the proposed measures, but by adopting the notion ofdominance relationship among those measures. Our approach bypasses the problemof the diversity of results and reaches a compromise between the assessments ofthese measures. Furthermore, the proposed approach also avoids anothernon-trivial problem which is the amount of clauses to be deleted at eachreduction of the learned clause database.

Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship