Material for course on An introduction to proximal solvers for engineers, to be taught internally at at Luna Innovations.
The main teaching material is available in the lectures directory the form of Jupyter slides, divided into 3 separate lectures.
Simply type jupyter nbconvert Lecture*.ipynb --output-dir='../html' --to slides --post serve --embed-images (where *=1,2,3) to obtain the slides in html format. Note that the html version of the slides is also already provided in the html subdirectory.
Alongside the lecture slides, a number of exercises will be presented during the course. These are in the form of Jupyter notebooks and can be accessed from the notebooks directory.
| Session | Link to material | Link to Colab |
|---|---|---|
| Lecture 1 | Link | |
| Lecture 2 | Link | |
| Lab1: FFTLinOp.ipynb | Link |  |
| Lab2: BasisPursuit.ipynb | Link | |
| Lab3: SignalDenoising.ipynb | Link | |
| Lab4: MRIReconstruction.ipynb | Link | |
| Lab5: Nonlinear.ipynb | Link | |
| Lab6: MRIPnP.ipynb | Link | |
To run the different Jupyter notebooks, participants can either use:
- local Python installation (simply run
./install_env.sh). - a Cloud-hosted environment such as Google Colab. Simply make sure to upload the relevant notebooks and run the first comment cell to install missing libraries.
The material in this repository is licensed under a Creative Commons Attribution 4.0 International License.
