All the experiments presented in the article can be reproduced using the XP*-*.py files.

• XP1-1_sd1_dirichlet.py reproduces the study of the dirichlet kernel in sparse deconvolution.
• XP1-2_sd1_gaussian.py reproduces the study of the gaussian kernel in sparse deconvolution.
• XP1-3_sd1_lambda.py reproduces the study of the lambda in sparse deconvolution.
• XP1-4_sd1_initialization.py reproduces the study of initialization in sparse deconvolution.

• XP2-1_tln_relu_squared.py reproduces the study of the relu activation and squared loss in the two-layer network example
• XP2-2_tln_lambda.py reproduces the study of lambda in the two-layer network example
• XP2-3_tln_initialization.py reproduces the study of initialization in the two-layer network example
• XP2-4_tln_relu_logistic.py reproduces the study of the relu activation and logistic loss in the two-layer network example

The core classes for each of the two examples are implemented in:

• sparse_deconvolution_1D.py for the sparse deconvolution example.
• two_layer_nn.py for the two-layer neural network example.

The forward-backward algorithm and the stochastic gradient descent algorithm are implemented in

• optimizer.py.

Other files implement miscelanious functions and classes:

• activations.py the activation function classes.
• kernels.py the kernel classes.
• losses.py the loss function classes.
• parameters.py the classes structuring the parameters of each example, the common parameters and the custom parameters for each experiment.
• plot.py the functions used to plot the results.
• tests.py some tests.
• requirements.txt the required package for reproducting the examples.
• requirements_test.txt the additional packages required for testing.

[1] Lenaic Chizat and Francis Bach. On the Global Convergence of Gra-dient Descent for Over-parameterized Models using Optimal Transport.