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arXiv 0906.0501physics.comp-phphysics.data-an
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扩散蒙特卡罗:大型系统计算成本的指数标度

Diffusion Monte Carlo: Exponential scaling of computational cost for large systems

Norbert Nemec

摘要:

蒙特卡罗算法的计算成本只有在考虑到所产生的统计误差的大小时才能有意义地讨论。以每个粒子的误差固定为目标,研究了大量子系统的扩散蒙特卡罗方法的标度行为。我们确定的相关性人口的步行者作为主导的缩放因子的大型系统。虽然这个因素对于通常研究的中小型系统可以忽略不计,但它最终显示出指数缩放。对于每个特定的系统,比例因子可以直接估计,我们发现它通常只与包含数百个原子的系统相关。

英文摘要:

The computational cost of a Monte Carlo algorithm can only be meaningfully discussed when taking into account the magnitude of the resulting statistical error. Aiming for a fixed error per particle, we study the scaling behavior of the diffusion Monte Carlo method for large quantum systems. We identify the correlation within the population of walkers as the dominant scaling factor for large systems. While this factor is negligible for small and medium sized systems that are typically studied, it ultimately shows exponential scaling. The scaling factor can be estimated straightforwardly for each specific system and we find that is typically only becomes relevant for systems containing more than several hundred atoms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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