This is the repository that accompanies the manuscript "An exact solver for the Weston-Watkins SVM subproblem".

To train the WW-SVM on mnist.scale -- downloadable from LIBSVM Data: Classification (Multi-class) -- run the following in console:

make
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.scale.bz2
bunzip2 mnist.scale.bz2
./train -s 30 -c 1 -t 64 mnist.scale

The -s 30 flag is for choosing the Walrus solver for the linear WW-SVM. Alternatively, the -s 31 flag is for choosing the Shark solver for the linear WW-SVM. The -c 1 flag is for setting the regularizer to 1. The -t 64 sets the max outer iterations to 64.

To run the trained model on test set:

wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.scale.t.bz2
bunzip2 mnist.scale.t.bz2
./predict mnist.scale.t mnist.scale.model output

The default stopping criterion is when the duality gap decays by a factor of 0.0009 relative to right after the first iteration. See the manuscript for more details.

This can be changed by modifying

#define STOPPING_CRITERION 0.0009

to a different value.

After cloning the repository run the following in the console

chmod +x reproduce_figures.sh
./reproduce_figures.sh

This script performs the following:

1. Download all the datasets
2. Run the Walrus and Shark solver on each dataset over the hyperparameter grid of size 10: [2^(-6), 2^(-5), ..., 2^3]

After the above script finishes, make sure the following Python packages are installed:

pandas
numpy
matplotlib
seaborn
re

Finally, go to the Jupyter notebook experiments/analyze.ipynb and hit run all.