Homework for the course Optimization theory with applications
📅 Date: Dec 2019
🏫 Master in Data Science and Engineering at EURECOM
Convex sets and functions
Assignment: here
My solution: done on paper
Implementation of gradient descent and dual ascent
Assignment: here
My solution: exercise 1: hmw2_1.ipynb, exercise 2: hmw2_2.ipynb
To run the code, you need Python 3, Jupyter Notebook (or JupyterLab) and the Python packages listed in requirements.txt.
Create a virtual environment, install the package dependencies and add a custom kernel to Jupyter:
$ python -m venv venv
$ source venv/bin/activate
(venv) $ pip install -r requirements.txt ipykernel
(venv) $ ipython kernel install --user --name=project-optim
(venv) $ deactivate
Now you can simply run:
$ jupyter-lab
and open the two notebook files.
The source code is licensed under the GNU GPLv3. The content of the report is licensed under the CC BY-NC-SA 4.0