Sampling Random Spanning Trees Faster than Matrix Multiplication

STOC, pp. 730-742, 2017.

Cited by: 47|Bibtex|Views52|DOI:https://doi.org/10.1145/3055399.3055499
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com|arxiv.org

Abstract:

We present an algorithm that, with high probability, generates a random spanning tree from an edge-weighted undirected graph in (n5/3 m1/3) time. The tree is sampled from a distribution where the probability of each tree is proportional to the product of its edge weights. This improves upon the previous best algorithm due to Colbourn et a...More

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