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Exact and Efficient Generation of Geometric Random Variates and Random Graphs

机译:精确有效地生成几何随机变量和随机图

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The standard algorithm for fast generation of Erdos-Renyi random graphs only works in the Real RAM model. The critical point is the generation of geometric random variates Geo(p), for which there is no algorithm that is both exact and efficient in any bounded precision machine model. For a RAM model with word size ω = Ω(log log(1/p)), we show that this is possible and present an exact algorithm for sampling Geo(p) in optimal expected time O(1 + log(1/p)/ω). We also give an exact algorithm for sampling min{n, Geo(p)} in optimal expected time 0(1 + log(min{1/p, n})/ω). This yields a new exact algorithm for sampling Erdoes-Renyi and Chung-Lu random graphs of n vertices and m (expected) edges in optimal expected runtime O(n + m) on a RAM with word size ω = θ(log n).
机译:快速生成Erdos-Renyi随机图的标准算法仅在Real RAM模型中有效。关键是几何随机变量Geo(p)的生成,为此,在任何有界精度机器模型中都没有既精确又有效的算法。对于单词大小为ω=Ω(log log(1 / p))的RAM模型,我们证明了这是可能的,并给出了在最佳预期时间O(1 + log(1 / p)中对Geo(p)进行采样的精确算法)/ω)。我们还给出了在最佳预期时间0(1 + log(min {1 / p,n})/ω)中对min {n,Geo(p)}进行采样的精确算法。这产生了一种新的精确算法,用于在字大小为ω=θ(log n)的RAM上以最佳预期运行时间O(n + m)对n个顶点和m个(预期)边的Erdoes-Renyi和Chung-Lu随机图进行采样。

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