通过均匀采样改进支化聚合物模型的广义 Rosenbluth 采样收敛性
Improving Convergence of Generalised Rosenbluth Sampling for Branched Polymer Models by Uniform Sampling
AI总结：
针对 GARM 采样支化聚合物时偏向极端构型、重加权代价高的问题，本文分析二叉树情形并提出对大气采用均匀采样，同时指出其计算成本可能抵消收益。
摘要：
使用广义大气 Rosenbluth 方法（GARM）进行采样，是一种用于估计格点聚合物模型分布的技术，已在线性聚合物和格点多边形研究中取得一定成功。本文将解释此类采样为何以及如何在许多支化聚合物模型中表现不佳。通过在简单二叉树上分析该算法，我们认为根本问题在于对极端构型存在固有偏倚，而用重加权技术纠正这种偏倚代价高昂。我们通过对 GARM 核心的大气应用均匀采样方法给出解决方案；但我们也提醒，由此产生的计算复杂度往往会超过所获得的改进。
英文摘要：
Sampling with the Generalised Atmospheric Rosenbluth Method (GARM) is a technique for estimating the distributions of lattice polymer models that has had some success in the study of linear polymers and lattice polygons. In this paper we will explain how and why such sampling appears not to be effective for many models of branched polymers. Analysing the algorithm on a simple binary tree, we argue that the fundamental issue is an inherent bias towards extreme configurations that is costly to correct with reweighting techniques. We provide a solution to this by applying uniform sampling methods to the atmospheres that are central to GARM. We caution that the ensuing computational complexity often outweighs the improvements gained.
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。
