Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving variants via proof complexity techniques. Our results show that almost all of the QCDCL variants are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.

中文翻译：

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QCDCL 具有立方体学习或纯粹的字面消除——什么是最好的？


量化冲突驱动子句学习（QCDCL）是解决量化布尔公式（QBF）的主要方法之一。我们形式化并研究了 QCDCL 的几个版本，其中包括立方体学习和/或纯文字消除，并通过证明复杂性技术正式比较所得的解决方案变体。我们的结果表明，几乎所有 QCDCL 变体在证明大小（以及求解器运行时间）方面都呈指数级不可比，这表明了如何实际实现 QCDCL 的不同正交方式。