A scalable system for primal-dual optimization

We present some of the most widely used architectures for Big Data, $\mathit{\text{Hadoop}}$ and $\mathit{\text{Spark}}$, 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 $\mathrm{\parallel }\cdot {\mathrm{\parallel }}^2$ 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.