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A Faster Algorithm for Propositional Model Counting Parameterized by Incidence Treewidth

机译:基于事件树宽参数化的命题模型计数的快速算法

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The propositional model counting problem (#SAT) is known to be fixed-parameter-tractable (FPT) when parameterized by the width k of a given tree decomposition of the incidence graph. The running time of the fastest known FPT algorithm contains the exponential factor of 4~k. We improve this factor to 2~k by utilizing fast algorithms for computing the zeta transform and covering product of functions representing partial model counts, thereby achieving the same running time as FPT algorithms that are parameterized by the less general treewidth of the primal graph. Our new algorithm is asymptotically optimal unless the Strong Exponential Time Hypothesis (SETH) fails.
机译:命题模型计数问题(#SAT)在通过入射图的给定树分解的宽度k进行参数化时已知是固定参数可处理的(FPT)。已知最快的FPT算法的运行时间包含4〜k的指数因子。通过利用快速算法来计算zeta变换并覆盖代表部分模型计数的函数的乘积,我们将该因子提高到2k,从而实现了与FPT算法相同的运行时间,而FPT算法是通过原始图的较小树宽来进行参数化的。除非强指数时间假说(SETH)失败,否则我们的新算法是渐近最优的。

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