摘要翻译：
在这封信中，我们使用一个一般的重整化群算法来实现Propp和Wilson的“从过去耦合”方法到复杂物理系统。在Markov链Monte Carlo动力学下，我们的算法从部分构型（斑块）在不断增加的长度尺度上演化整个构型空间，并允许我们生成Boltzmann分布的“精确样本”，这些样本被严格证明与初始条件无关。我们在尺寸为64×64的二维伊辛自旋玻璃中验证了我们的方法。
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英文标题：
《Renormalization group approach to exact sampling》
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作者：
Cedric Chanal, Werner Krauth
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最新提交年份：
2008
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分类信息：

一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics        物理学
二级分类：Disordered Systems and Neural Networks        无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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英文摘要：
  In this Letter, we use a general renormalization-group algorithm to implement Propp and Wilson's "coupling from the past" approach to complex physical systems. Our algorithm follows the evolution of the entire configuration space under the Markov chain Monte Carlo dynamics from parts of the configurations (patches) on increasing length scales, and it allows us to generate "exact samples" of the Boltzmann distribution, which are rigorously proven to be uncorrelated with the initial condition. We validate our approach in the two-dimensional Ising spin glass on lattices of size 64 x 64.
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PDF链接：
https://arxiv.org/pdf/707.4117