Fast and stable determinant quantum Monte Carlo

C. Bauer
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引用次数: 4

Abstract

We assess numerical stabilization methods employed in fermion many-body quantum Monte Carlo simulations. In particular, we empirically compare various matrix decomposition and inversion schemes to gain control over numerical instabilities arising in the computation of equal-time and time-displaced Green's functions within the determinant quantum Monte Carlo (DQMC) framework. Based on this comparison, we identify a procedure based on pivoted QR decompositions which is both efficient and accurate to machine precision. The Julia programming language is used for the assessment and implementations of all discussed algorithms are provided in the open-source software library StableDQMC.jl [this http URL].
快速稳定的行列式量子蒙特卡罗
我们评估了费米子多体量子蒙特卡罗模拟中采用的数值稳定方法。特别是,我们经验地比较了各种矩阵分解和反演方案,以获得对行列式量子蒙特卡罗(DQMC)框架内等时间和时间位移格林函数计算中产生的数值不稳定性的控制。在此基础上,我们提出了一种既高效又能达到机器精度的旋转QR分解方法。Julia编程语言用于评估和实现所有讨论的算法,这些算法在开源软件库StableDQMC中提供。jl[此http URL]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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