论文标题
通过无拒绝的部分邻居搜索采样
Sampling via Rejection-Free Partial Neighbor Search
论文作者
论文摘要
大都市算法涉及生产马尔可夫链以收敛到指定的目标密度$π$。为了提高其效率，我们可以使用Metropolis算法的无排斥版本，该版本可以通过评估所有邻居来避免拒绝效率低下。通过使用并行性硬件，可以使无拒绝的效率更加有效。但是，对于某些专门的硬件，例如数字退火单元，单位数量将限制每个步骤中考虑的邻居数量。因此，我们提出了一种称为部分邻居搜索的增强版本的无排斥不足的版本，该版本仅在使用无排斥技术的同时考虑一部分邻居。该方法将在几个示例上进行测试，以证明其在不同情况下的有效性和优势。
The Metropolis algorithm involves producing a Markov chain to converge to a specified target density $π$. In order to improve its efficiency, we can use the Rejection-Free version of the Metropolis algorithm, which avoids the inefficiency of rejections by evaluating all neighbors. Rejection-Free can be made more efficient through the use of parallelism hardware. However, for some specialized hardware, such as Digital Annealing Unit, the number of units will limit the number of neighbors being considered at each step. Hence, we propose an enhanced version of Rejection-Free known as Partial Neighbor Search, which only considers a portion of the neighbors while using the Rejection-Free technique. This method will be tested on several examples to demonstrate its effectiveness and advantages under different circumstances.
