A scalable system for primal-dual optimization

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 $\Vert \cdot {\Vert }^2$ 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.

This has been withdrawn by the author due since it is not fully complete to reach a publication on arxiv.org

References in corpus (3)

• Composite Self-Concordant Minimization
• Convexity in source separation: Models, geometry, and algorithms
• A Primal-Dual Algorithmic Framework for Constrained Convex Minimization