Open Sourcing SparkADMM: a Massively-parallel Framework for Solving Big Data Problems

Training machine learning models over massive amounts of data is a cornerstone of many data analytics tasks. Usually this involves solving large optimization problems involving millions of optimization variables and constraints. Doing so over a parallel platform, like Spark or Hadoop, is crucial to making such computations scalable. It is not always obvious how to solve large optimization problems in parallel. ADMM, which stands for the Alternating Directions Method of Multipliers, is a popular parallel optimization technique that provides a methodology for doing so.