Convex Optimization Notebooks

Quick links: GitHub repository with the notebooks and a detailed README describing each of them.

In fall 2022, I took a graduate-level applied mathematics course on convex optimization at Claremont Graduate University. Taught by Prof. Marina Chugunova, the class covered both theory and practice, so in addition to more standard math problems our assignments included implementation of certain computational algorithms. I did my implementations in Python as Jupyter Notebooks and put extra effort into visualizing what was going on. Selected notebooks, including implementations of the ellipsoid method and various forms of gradient descent, can be seen in this GitHub repository, which also contains a README with more technical details.

In spring 2023, I took a second graduate-level course with Prof. Chugunova, Mathematics of Machine Learning.