A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares
Distributed, Parallel, and Cluster Computing
2015-01-14 v1 Data Structures and Algorithms
Numerical Analysis
Abstract
We present a parallel algorithm for the undirected -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.
Cite
@article{arxiv.1501.03105,
title = {A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares},
author = {Yao Zhu and David F. Gleich},
journal= {arXiv preprint arXiv:1501.03105},
year = {2015}
}