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首页> 外文期刊>Journal of Automated Reasoning >A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality
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A Verified SAT Solver Framework with Learn, Forget, Restart, and Incrementality

机译:具有学习,忘记,重新启动和增量功能的经过验证的SAT解算器框架

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We developed a formal framework for conflict-driven clause learning (CDCL) using the Isabelle/HOL proof assistant. Through a chain of refinements, an abstract CDCL calculus is connected first to a more concrete calculus, then to a SAT solver expressed in a functional programming language, and finally to a SAT solver in an imperative language, with total correctness guarantees. The framework offers a convenient way to prove metatheorems and experiment with variants, including the Davis–Putnam–Logemann–Loveland (DPLL) calculus. The imperative program relies on the two-watched-literal data structure and other optimizations found in modern solvers. We used Isabelle’s Refinement Framework to automate the most tedious refinement steps. The most noteworthy aspects of our work are the inclusion of rules for forget, restart, and incremental solving and the application of stepwise refinement.
机译:我们使用Isabelle / HOL证明助手为冲突驱动子句学习(CDCL)开发了一个正式框架。通过一系列改进,抽象的CDCL演算首先连接到更具体的演算,然后连接到以功能性编程语言表示的SAT求解器,最后连接到以命令式语言表示的SAT求解器,并保证完全正确。该框架提供了一种方便的方法来证明元定理并进行变体实验,包括戴维斯-普特南-洛格曼-洛夫兰(DPLL)演算。命令式程序依赖于两个观察对象的数据结构以及现代求解器中发现的其他优化。我们使用Isabelle的优化框架来自动化最繁琐的优化步骤。我们工作中最值得注意的方面是包含忘记,重新启动和增量求解的规则以及逐步完善的应用。

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