我们提出了一种方法Monte Carlo抽样系统的离散变量（集中在伊辛的情况下），引入了一个先验的候选移动的Metropolis-Hastings计划，可以显着降低拒绝率，称为减少拒绝率（RRR）的方法。该方法采用与无拒绝方案（如Bortz、Kalos和Lebowitz（BKL）提出的方法[Bortz et al. J.Comput.Phys. 1975]）相同的概率分布来选择移动;然而，它将其用作标准Metropolis方案中的先验：因此，它不是完全无拒绝的，但在很大范围的情况下，它几乎是如此。这允许将该方法扩展到无拒绝方案变得低效的情况，特别是当图连通性不稀疏时，但是能量仍然可以表示为两个分量的总和，其中一个分量在稀疏图上计算并且主导测量。作为此类实例的示例，我们证明了当在Suzuki-Trotter形式主义中存在横向场的情况下对量子自旋模型进行蒙特卡罗模拟时，以及当探索Baldassi等人最近引入的所谓鲁棒系综时，该方法会产生出色的结果。[PNAS 2016]。我们用于Ising情况的代码是公开可用的[https：//github.com/carlobaldassi/RRRMC.jl]，并且可扩展到用户定义的模型：它提供标准Metropolis、RRR方法、BKL方法（扩展到连续能量谱的情况）和等待时间方法的有效实现[Dall和Sibani Comput.Phys.Commun. 2001年]。
英文摘要：
We present a method for Monte Carlo sampling on systems with discrete variables (focusing in the Ising case), introducing a prior on the candidate moves in a Metropolis-Hastings scheme which can significantly reduce the rejection rate, called the reduced-rejection-rate (RRR) method. The method employs same probability distribution for the choice of the moves as rejection-free schemes such as the method proposed by Bortz, Kalos and Lebowitz (BKL) [Bortz et al. J.Comput.Phys. 1975]; however, it uses it as a prior in an otherwise standard Metropolis scheme: it is thus not fully rejection-free, but in a wide range of scenarios it is nearly so. This allows to extend the method to cases for which rejection-free schemes become inefficient, in particular when the graph connectivity is not sparse, but the energy can nevertheless be expressed as a sum of two components, one of which is computed on a sparse graph and dominates the measure. As examples of such instances, we demonstrate that the method yields excellent results when performing Monte Carlo simulations of quantum spin models in presence of a transverse field in the Suzuki-Trotter formalism, and when exploring the so-called robust ensemble which was recently introduced in Baldassi et al. [PNAS 2016]. Our code for the Ising case is publicly available [https://github.com/carlobaldassi/RRRMC.jl], and extensible to user-defined models: it provides efficient implementations of standard Metropolis, the RRR method, the BKL method (extended to the case of continuous energy specra), and the waiting time method [Dall and Sibani Comput.Phys.Commun. 2001].
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
Perimeter Institute for Theoretical Physics（圆周理论物理研究所）；University of Cambridge（剑桥大学）；University of Waterloo（滑铁卢大学）；Massachusetts Institute of Technology（麻省理工学院）
