论文标题
用于取样的近端算法
A Proximal Algorithm for Sampling
论文作者
论文摘要
我们研究了与缺乏平稳性的潜力相关的采样问题。电势可以是凸或非凸。偏离标准平滑设置，仅假定电势是弱平滑的或不平滑的，或者是多个此类功能的求和。我们开发了一种采样算法，该算法类似于针对此具有挑战性的抽样任务的优化近端算法。我们的算法基于一种被称为交替采样框架（ASF）的吉布斯采样的特殊情况。这项工作的关键贡献是基于对非凸电势和不一定光滑的非凸电势的排斥抽样的实践实现。在这项工作中几乎所有采样的情况下，我们的近端采样算法都比所有现有方法都能达到更好的复杂性。
We study sampling problems associated with potentials that lack smoothness. The potentials can be either convex or non-convex. Departing from the standard smooth setting, the potentials are only assumed to be weakly smooth or non-smooth, or the summation of multiple such functions. We develop a sampling algorithm that resembles proximal algorithms in optimization for this challenging sampling task. Our algorithm is based on a special case of Gibbs sampling known as the alternating sampling framework (ASF). The key contribution of this work is a practical realization of the ASF based on rejection sampling for both non-convex and convex potentials that are not necessarily smooth. In almost all the cases of sampling considered in this work, our proximal sampling algorithm achieves better complexity than all existing methods.
