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Algorithms developed during my master thesis at the Universita' degli Studi di Padova. In order to run the tests, you can follow my the instructions at page 31. Download the thesis here: http://tesi.cab.unipd.it/65265/
Implementation of unconstrained and constrained convex optimization algorithms in Python, focusing on solving data science problems such as semi-supervised learning and Support Vector Machines.
Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
This project was carried out as the final assignment for the Mathematical Optimization for Data Science course. The goal of the analysis was to compare two variants of the Frank-Wolfe Method with the Projected Gradient Method on the Markowitz portfolio optimization problem.
This julia package addresses the membership problem for local polytopes: it constructs Bell inequalities and local models in multipartite Bell scenarios with binary outcomes.
Code for the paper: Wirth, E.S. and Pokutta, S., 2022, May. Conditional gradients for the approximately vanishing ideal. In International Conference on Artificial Intelligence and Statistics (pp. 2191-2209). PMLR.