Uniform Sampling through the Lovász Local Lemma release_ffrcshisp5c7fdf2bjo7beexli

by Heng Guo, Mark Jerrum, Jingcheng Liu

Released as a article .

2017  

Abstract

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 (perhaps surprising) new connections between the variable framework of the Lov\'asz 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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Date   2017-03-18
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Language   en ?
arXiv  1611.01647v2
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