A scalable modular convex solver for regularized risk minimization

A wide variety of machine learning problems can be de-scribed as minimizing a regularized risk functional, with dif-ferent algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Ma-chines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper de-scribes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for data-locality, and can deal with regularizers such as `1 and `2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox