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Computing Vertex Eccentricity in Exponentially Large Graphs: QBF Formulation and Solution

机译:计算指数大图中的顶点偏心率:QBF公式和解决方案

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We formulate eccentricity computation for exponentially large graphs as a decision problem for Quantified Boolean Formulas (QBFs.) and demonstrate how the notion of eccentricity arises in the area of formal hardware verification. In practice, the QBFs obtained from the above formulation are difficult to solve because they span a huge Boolean space. We address this problem by proposing an eccentricity-preserving graph transformation that drastically reduces the Boolean search space by decreasing the number of variables in the generated formulas. Still, experimental analysis shows that the reduced formulas are not solvable by state-of-the-art QBF solvers. Thus, we propose a novel SAT-based decision procedure optimized for these formulas. Despite exponential worst-case behavior of this procedure, we present encouraging experimental evidence showing its superiority to other public-domain solvers.
机译:我们将指数大图的偏心率计算公式化为量化布尔公式(QBF)的决策问题,并演示了偏心率的概念如何在形式化硬件验证领域出现。实际上,由上述公式获得的QBF难以求解,因为它们跨越了巨大的布尔空间。我们通过提出一种保留离心率的图变换来解决此问题,该变换通过减少所生成公式中的变量数量来大大减少布尔搜索空间。尽管如此,实验分析表明,简化的公式无法通过最新的QBF求解器求解。因此,我们提出了一种针对这些公式进行优化的新颖的基于SAT的决策程序。尽管该程序在指数级的最坏情况下表现出色,但我们仍提供了令人鼓舞的实验证据,表明该程序优于其他公共领域的求解器。

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