Python implementation of numerical experiments from the paper:
These codes are credited jointly to the first two authors. See Section 5 of the paper for the description of the experimental setup.
To run the experiments, clone or download the repository and launch the file run_all.py.
The data for the curves will appear in data/gauss. The plots will appear in plots/gauss.
The experiments that reproduce the curves reported in the paper take a few days to run. To obtain (less accurate) results faster,
change the number of Monte-Carlo trials: parameter T in run_all.py. See /plots/T-150 for an example.
You can also play with the values of r and kappa in the nested loop to specify the rank and condition number of the design covariance matrix.