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Symbolic Graphs: Linear Solutions to Connectivity Related Problems

机译:符号图:连通性相关问题的线性解决方案

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摘要

The importance of symbolic data structures such as Ordered Binary Decision Diagrams (OBDD) is rapidly growing in many areas of Computer Science where the large dimensions of the input models is a challenging feature: OBDD based graph representations allowed to define truly new standards in the achievable dimensions for the Model Checking verification technique. However, OBDD representations pose strict constraints in the algorithm design issue. For example, Depth-First Search (DFS) is not feasible in a symbolic framework and, consequently, many state-of-the-art DFS based algorithms (e.g., connectivity procedures) cannot be easily rearranged to work on symbolically represented graphs. We devise here a symbolic algorithmic strategy, based on the new notion of spine-set, that is general enough to be the engine of linear symbolic step algorithms for both strongly connected components and biconnected components. Our procedures improve on previously designed connectivity symbolic algorithms. Moreover, by an application to the so-called “bad cycle detection problem”, our technique can be used to efficiently solve the emptiness problem for various kinds of ω-automata.
机译:在计算机科学的许多领域中,符号数据结构(例如有序二进制决策图(OBDD))的重要性正在迅速增长,在这些领域中,输入模型的大尺寸是一项具有挑战性的功能:基于OBDD的图形表示允许在可行的方法中定义真正的新标准模型检查验证技术的尺寸。但是,OBDD表示法在算法设计问题上提出了严格的约束。例如,深度优先搜索(DFS)在符号框架中不可行,因此,许多基于DFS的最新算法(例如,连接过程)无法轻松地重新排列以在符号表示的图上工作。在此,我们基于脊柱集的新概念设计了一种符号算法策略,该策略足够通用,可以成为用于强连接组件和双向组件的线性符号步进算法的引擎。我们的程序在以前设计的连接符号算法上有所改进。此外,通过应用到所谓的“不良循环检测问题”,我们的技术可以有效地解决各种ω-自动机的空度问题。

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