The final project for the course "Optimization for Data Science" with Chia-man and Salma.
The goal of the project is to:
- derive the duals for SVMs with and without intercept
- implement an SVM using a blackbox convex toolbox (cvxopt in Python)
- implement your own solvers for the without intercept case: Proximal gradient, Coordinate Descent, Newton, Quasi-Newton
- present a clear benchmark of the different strategies on small and medium scale datasets