document: A scalable modular convex solver for regularized risk minimization A wide variety of machine learning problems can be described as minimizing a regularized risk functional , with different algorithms using different notions of risk and different regularizers . Examples include linear Support Vector Machines ( SVMs ) , Logistic Regression , Conditional Random Fields ( CRFs ) , and Lasso amongst others . This paper describes 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 l1 and l2 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 .
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