ADMM based scalable machine learning on Spark

Most machine learning algorithms involve solving a convex optimization problem. Traditional in-memory convex optimization solvers do not scale well with the increase in data. This paper identifies a generic convex problem for most machine learning algorithms and solves it using the Alternating Direction Method of Multipliers (ADMM). Finally such an ADMM problem transforms to an iterative system of linear equations, which can be easily solved at scale in a distributed fashion. We implement this framework in Apache Spark and compare it with the widely used Machine Learning LIBrary (MLLIB) in Apache Spark 1.3.