This package implements Anderson extrapolation for coordinate descent.

It also implements inertial proximal gradient descent (ie APPROX without parallelism).

In order to be able to reproduce the experiments you can install the package.

To be able to run the code you first need to run, in this folder (code folder, where the setup.py is): :

pip install -e .

You should now be able to run a friendly example: :

ipython -i expes/expe_ols/plot_intro_ols.py

• Figure 1: expes_ols/plot_intro_ols.py (less than 2 min)
• Figure 2: expes_eigvals/plot_rayleigh.py (around 10 minutes, main_raileygh.py must be run first)
• Figure 3: expes_cd_sym/plot_cd_sym.py, choosing pb = "lasso" (yes, even if the problem is OLS) (1 minute)
• Figure 4: expes_cd_sym/plot_cd_sym.py, choosing pb = "logreg" (1 minute)
• Figure 5: expes_ols/plot_influ_reg.py (less than 2 min)
• Figure 6: expes_ols/plot_influ_K.py (less than 2 min)

Figure 7, 8, 9 and 10, 11, 12 in appendix (convergence on various datasets and regularizers): These take quite a long time to run given the extensive validation, they should be run with multithreading. :

cd expes/expe_conv_logreg
run main.py
run plot.py

and the same for expes/expe_conv_enet and expes/expe_conv_lasso