论文标题
人口退火的重新采样方案 - 数值结果
Resampling schemes in population annealing -- numerical results
论文作者
论文摘要
人口退火（PA）是一种基于人群的算法，是为具有粗糙的自由能景观的热力学系统平衡模拟而设计的。众所周知，与单独的标准马尔特·卡洛（Monte Carlo）相比，它在这样做的效率更高。该算法具有许多参数可以进行微调以提高性能。尽管大多数这些参数都有一些理论和数值工作，但文献中似乎存在有关重新采样在PA中的作用的差距。在这里，我们介绍了2D ISING模型的PA模拟的许多重采样方案的数值比较。
Population annealing (PA) is a population-based algorithm that is designed for equilibrium simulations of thermodynamic systems with a rough free energy landscape. It is known to be more efficient in doing so than standard Markov chain Monte Carlo alone. The algorithm has a number of parameters that can be fine-tuned to improve performance. While there is some theoretical and numerical work regarding most of these parameters, there appears to be a gap in the literature concerning the role of resampling in PA. Here, we present a numerical comparison of a number of resampling schemes for PA simulations of the 2D Ising model.
