Library of Semi-Relaxed Optimal Transport
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Updated
Feb 8, 2022
Library of Semi-Relaxed Optimal Transport
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/
Routines for submodular set function minimization
The final project created for Optimization for Data Science course
Implementation of three variants of the Frank-Wolfe method for solving the Minimum Enclosing Ball problem, and application to anomaly detection.
Code for the paper Accelerated Affine-Invariant Vonvergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes
Zeroth order Frank Wolfe algorithm. Project for the Optimization for Data Science exam.
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.
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.
Differentiable wrapper for FrankWolfe.jl convex optimization routines
Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
DOT
Constrained Optimization using Frank-Wolfe Method
Blind Image Deconvolution and Frank-Wolfe's algorithm to deblur a license plate for Crime Scene Investigation (CSI)
Implementation of a novel 'helicality' algorithm that quantifies the octave equivalence of frequency sub-bands in an audio dataset.
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.
Study of four first order Frank Wolfe algorithms to solve constrained non-convex problems in the context of white box adversarial attacks.
Implementation of Frank Wolfe algoritm on python
This is the repo for Fast Pure Exploration via Frank-Wolfe (NeurIPS 2021).
This julia package addresses the membership problem for local polytopes: it constructs Bell inequalities and local models in multipartite Bell scenarios with binary outcomes.
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