Guo, Heng

Jerrum, Mark

Liu, Jingcheng

We propose a new algorithmic framework, called partial rejection sampling, to draw samples exactly from a product distribution, conditioned on none of a number of bad events occurring. Our framework builds new connections between the variable framework of the Lovász Local Lemma and some classical sampling algorithms such as the “cycle-popping” algorithm for rooted spanning trees. Among other applications, we discover new algorithms to sample satisfying assignments of k-CNF formulas with bounded variable occurrences

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Uniform Sampling Through the Lovasz Locál Lemma

https://www.pure.ed.ac.uk/ws/files/79630141/PRS.pdf

Uniform Sampling Through the Lovasz Locál Lemma

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