CDCLソルバーにおける学習節の深さに基づく節管理戦略

抄録

In this paper, we propose a new clause management strategy based on the depth of learnt clauses for CDCL solvers. A CDCL solver derives many learnt clauses from con icts occurred in the search process. These learnt clauses are useful to prevent similar con icts, but they are periodically reduced to avoid memory over ow and decreasing the speed of unit propagation. Hence, an evaluation criteria of learnt clauses is important for CDCL solvers. In this study, we propose a new evaluation criteria based on the depth of learnt clauses. Each learnt clause has the depth in the derivation tree. Our approach holds (1) learnt clauses which may become bottleneck, that is, there is no other clauses at the depth, and (2) learnt clauses at deepest part of the tree. The experimental results show that our criteria can help to identify useful learnt clauses compared with existing criteria used in CDCL solvers.