A scalable system for primal-dual optimization
Distributed, Parallel, and Cluster Computing
2015-08-10 v2
Abstract
We present some of the most widely used architectures for Big Data, \textit{Hadoop} and \textit{Spark}, and develop several implementations exploiting, the advantages of each. We implement a simplified version of the primal-dual optimization algorithm, described briefly in this paper, by choosing the smoothing functions to be with a zero center point. Under the assumption that data is provided as a sparse matrix, we assess the scalability of the designed systems empirically by running them on sample tests.
Keywords
Cite
@article{arxiv.1507.01456,
title = {A scalable system for primal-dual optimization},
author = {Radu Cristian Ionescu},
journal= {arXiv preprint arXiv:1507.01456},
year = {2015}
}
Comments
This has been withdrawn by the author due since it is not fully complete to reach a publication on arxiv.org