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A space-efficient simulation algorithm on probabilistic automata

机译:概率自动机的空间高效仿真算法

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In the context of probabilistic automata, time-efficient algorithms for simulation have been proposed lately. The space complexity thereof is quadratic in the size of the transition relation, thus space requirements often become the practical bottleneck. In this paper, we propose a space-efficient algorithm for computing simulation based on partition refinement. Experimental evidence is given showing that not only the space efficiency is improved drastically: The experiments often require orders of magnitude less time. In practice, they are even faster than the (asymptotically) optimal algorithm by Crafa and Ranzato (2012).
机译:在概率自动机的背景下,最近提出了用于仿真的省时算法。其空间复杂度在过渡关系的大小上是平方的,因此空间需求经常成为实际的瓶颈。在本文中,我们提出了一种基于分区细化的高效空间计算仿真算法。实验表明,不仅空间效率得到了显着提高:实验通常需要少几个数量级的时间。实际上,它们甚至比Crafa和Ranzato(2012)提出的(渐近)最优算法还要快。

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