Library of Semi-Relaxed Optimal Transport
-
Updated
Feb 8, 2022
Library of Semi-Relaxed Optimal Transport
Routines for submodular set function minimization
This project was conducted as the final assignment for the Mathematical Optimization for Data Science course. The objective was to analyze and compare two variants of the Frank-Wolfe Method with the Projected Gradient Method in solving the Markowitz portfolio optimization problem.
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.
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.
The final project created for Optimization for Data Science course
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/
Final Project for Optimization for Datascence, UNIPD MSc program 23/24. Uses variants of Frank-Wolfe algorithms for projection-free white-box adversarial attacks on convolutional neural networks.
Implementation of three variants of the Frank-Wolfe method for solving the Minimum Enclosing Ball problem, and application to anomaly detection.
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
Study of four first order Frank Wolfe algorithms to solve constrained non-convex problems in the context of white box adversarial attacks.
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.
Code for the paper: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
Blind Image Deconvolution and Frank-Wolfe's algorithm to deblur a license plate for Crime Scene Investigation (CSI)
DOT
Constrained Optimization using Frank-Wolfe Method
Implementation of Frank Wolfe algoritm on python
Add a description, image, and links to the frank-wolfe topic page so that developers can more easily learn about it.
To associate your repository with the frank-wolfe topic, visit your repo's landing page and select "manage topics."