A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares

Yao Zhu; David F. Gleich

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arXiv · 1501.03105

A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares

Yao Zhu·David F. Gleich

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Abstract

We present a parallel algorithm for the undirected $s,t$-mincut problem with floating-point valued weights. Our overarching algorithm uses an iteratively reweighted least squares framework. This generates a sequence of Laplacian linear systems, which we solve using parallel matrix algorithms. Our overall implementation is up to 30-times faster than a serial solver when using 128 cores.

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BibTeX RIS

Yao Zhu, David F. Gleich. 2015-01-13. A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares. https://arxiv.org/abs/1501.03105

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