The fundamental problem of weighted sampling involves sampling of satisfying assignments of Boolean formulas, which specify sampling sets, and according to distributions defined by pre-specified weight functions to weight functions. The tight integration of sampling routines in various applications has highlighted the need for samplers to be incremental, i.e., samplers are expected to handle updates to weight functions. The primary contribution of this work is an efficient knowledge compilation-based weighted sampler, INC, designed for incremental sampling. INC builds on top of the recently proposed knowledge compilation language, OBDD[AND], and is accompanied by rigorous theoretical guarantees. Our extensive experiments demonstrate that INC is faster than state-of-the-art approach for majority of the evaluation. In particular, we observed a median of 1.69X runtime improvement over the prior state-of-the-art approach.

翻译：加权采样的基本问题涉及对布尔公式的可满足赋值的采样，这些公式指定了采样集，并根据由预先指定的权重函数定义的分布进行采样。采样程序在不同应用中的紧密集成凸显了采样器需要具备增量能力，即采样器需能处理权重函数的更新。本研究的主要贡献是一种基于知识编译的高效加权采样器INC，专为增量采样设计。INC构建于最近提出的知识编译语言OBDD[AND]之上，并具备严格的理论保证。我们的大量实验表明，在大多数评估中，INC比现有最优方法更快。具体而言，我们观察到相较于先前的最优方法，运行时间中位数提升了1.69倍。