Implementation of Frank Wolfe algoritm on python

Routines for submodular set function minimization

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (AAAI'20)

Implementation of a novel 'helicality' algorithm that quantifies the octave equivalence of frequency sub-bands in an audio dataset.

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/

Zeroth order Frank Wolfe algorithm. Project for the Optimization for Data Science exam.

The Workspace Planning Tool helps facilities managers and other workspace planners optimize seating arrangements and floorplans using Workplace Analytics collaboration data. This stand-alone tool is a series of Jupyter notebooks you can run locally on your machine.

Program for obtaining the user equilibrium solution with Frank-Wolfe Algorithm in urban traffic assignment

Library of Semi-Relaxed Optimal Transport

Python package designed to provide the essentials tools for off-the-grid inverse problem. This is the bedrock for future GUI implementation.

Frank-Wolfe Algorithm : Find User Equilibrium in Traffic Assignment

Study of four first order Frank Wolfe algorithms to solve constrained non-convex problems in the context of white box adversarial attacks.

Blind Image Deconvolution and Frank-Wolfe's algorithm to deblur a license plate for Crime Scene Investigation (CSI)

Implementation of the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.

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: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)

Implementation of three variants of the Frank-Wolfe method for solving the Minimum Enclosing Ball problem, and application to anomaly detection.

This is the repo for Fast Pure Exploration via Frank-Wolfe (NeurIPS 2021).

The final project created for Optimization for Data Science course

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.