RADiSA

RADiSA is an optimization method for large scale machine learning. The current implementation assumes that the observations are distributed, but not the features. However, each partition only operates on a subset of features in each iteration. To understand how the algorithm can be extended to distributed features, see the following publication: https://arxiv.org/pdf/1610.10060.pdf

This code implements the following algorithms:

• RADiSA
• Gradient Descent (and mini-batch SGD)

The present code trains hinge-loss SVM for binary classification. It is fairly straightfoward to extend it in order to solve other objectives (e.g. linear or logistic regression).

Getting Started

To compile the code:

sbt compile; sbt package

To run the code:

./onlyRun