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Uniform Sampling through the Lovász Local Lemma
release_ffrcshisp5c7fdf2bjo7beexli
by
Heng Guo, Mark Jerrum, Jingcheng Liu
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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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1611.01647v2
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