Overview

The Orthant-Wise Limited-memory Quasi-Newton optimizer (OWL-QN) implements an algorithm for optimizing L1-regularized objectives. It works with any smooth convex loss plus the L1 norm of the parameters, such as log-linear models (e.g. logistic regression and Markov random fields) and squared error (e.g. regression with the LASSO).

This package contains C++ source code allowing you to plug in any differentiable, convex loss you choose, just by defining a method to return the function’s value and gradient at any point. In addition, there is a standalone main method for optimizing the weights of logistic regression or least-squares models. For usage and other information, please see the enclosed file README.txt.

New Features

Here’s what’s new in this release:

• Implementation of the OWL-QN optimization algorithm

• Standalone trainer for logistic regression or least-squares (with compiled win32 executable)

• Related paper and ICML presentation

System Requirements

To run this software, you’ll need the following:

• A C++ compiler (if you want to define your own loss function or compile the standalone trainer yourself)

• or, a computer running MS Windows (if you only want to run the standalone logistic-regression or least-squares trainer using the included executable)

Installation Instructions

To install OWL-QN, do the following:

1. Download and unzip the package.

2. Read README.txt for usage and other information.

3. Run the standalone trainer, or link the source into your application and compile.