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arXiv · 2012.09502

Sampling Arborescences in Parallel

Nima Anari·Nathan Hu·Amin Saberi·Aaron Schild
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Abstract

We study the problem of sampling a uniformly random directed rooted spanning tree, also known as an arborescence, from a possibly weighted directed graph. Classically, this problem has long been known to be polynomial-time solvable; the exact number of arborescences can be computed by a determinant [Tut48], and sampling can be reduced to counting [JVV86, JS96]. However, the classic reduction from sampling to counting seems to be inherently sequential. This raises the question of designing efficient parallel algorithms for sampling. We show that sampling arborescences can be done in RNC. For several well-studied combinatorial structures, counting can be reduced to the computation of a determinant, which is known to be in NC [Csa75]. These include arborescences, planar graph perfect matchings, Eulerian tours in digraphs, and determinantal point processes. However, not much is known about efficient parallel sampling of these structures. Our work is a step towards resolving this mystery.

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Nima Anari, Nathan Hu, Amin Saberi, Aaron Schild. 2020-12-17. Sampling Arborescences in Parallel. https://arxiv.org/abs/2012.09502

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