Rugged Metropolis sampling with simultaneous updating of two dynamical variables.
Rugged Metropolis sampling with simultaneous updating of two dynamical variables.
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坚固的 Metropolis 采样，同时更新两个动态变量。
DOI：
10.1103/physreve.72.016712
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Zhou,Huan-Xiang
中科院分区：
文献类型：
--
作者：
Berg,BerndA;Zhou,Huan-Xiang
粗糙大都会（RM）算法是一种有偏的更新方案，旨在直接击中最有可能的配置在一个粗糙的自由能景观。详细介绍了该算法的单变量实现。其次是扩展到同时更新两个动态变量。在一项测试中，在真空中使用脑肽甲硫氨酸脑啡肽将传统的大都会模拟提高了约4倍。三个或更多二面角之间的相关性似乎阻止了低温下的较大改善。我们还研究了多击大都会计划，花费更多的CPU时间与大的自相关时间的变量。
The rugged Metropolis (RM) algorithm is a biased updating scheme which aims at directly hitting the most likely configurations in a rugged free-energy landscape. Details of the one-variableimplementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables. In a test with the brain peptide Met-Enkephalin in vacuumimproves conventional Metropolis simulations by a factor of about 4. Correlations between three or more dihedral angles appear to prevent larger improvements at low temperatures. We also investigate a multihit Metropolis scheme, which spends more CPU time on variables with large autocorrelation times.