This software package is for solving the distance weighted discrimination problem of the following form:

where is the sample size, is the feature dimension, and is the penalty parameter. Here, the input is a matrix whose columns are the feature vectors, and is a vector containing binary classification label . The notation is defined to be .

The main algorithm for solving is a symmetric Gauss-Seidel based ADMM method. It will output the result for which is a hyperplane separating the two classes of data and is a slack variable to allow the possibility that the positive and negative data points may not be separated cleanly by the hyperplane.

For more details, please see the 'README' file in the package.

Important note:

The software is still under development. Thus it will invariably be buggy. We would appreciate your feedback and bugs' report to the corresponding author:Kim-Chuan Toh, email: mattohkc@nus.edu.sg.

This is a research software. It is not intended nor designed to be a general purpose software at the moment.

Citation:

Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018), Fast algorithms for large scale generalized distance weighted discrimination, Journal of Computational and Graphical Statistics, 27(2), 368-379, doi: 10.1080/10618600.2017.1366915.

Copyright: This version of DWDLarge is distributed under the GNU General Public License 2.0. For commercial applications that may be incompatible with this license, please contact the authors to discuss alternatives.