Randomized LU Decomposition Using Sparse Projections
Numerical Analysis
2016-01-19 v1 Numerical Analysis
Abstract
A fast algorithm for the approximation of a low rank LU decomposition is presented. In order to achieve a low complexity, the algorithm uses sparse random projections combined with FFT-based random projections. The asymptotic approximation error of the algorithm is analyzed and a theoretical error bound is presented. Finally, numerical examples illustrate that for a similar approximation error, the sparse LU algorithm is faster than recent state-of-the-art methods. The algorithm is completely parallelizable that enables to run on a GPU. The performance is tested on a GPU card, showing a significant improvement in the running time in comparison to sequential execution.
Cite
@article{arxiv.1601.04280,
title = {Randomized LU Decomposition Using Sparse Projections},
author = {Yariv Aizenbud and Gil Shabat and Amir Averbuch},
journal= {arXiv preprint arXiv:1601.04280},
year = {2016}
}