Program for obtaining the user equilibrium solution with Frank-Wolfe Algorithm in urban traffic assignment
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Updated
Jan 23, 2022 - Python
Program for obtaining the user equilibrium solution with Frank-Wolfe Algorithm in urban traffic assignment
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Algorithms for Routing and Solving the Traffic Assignment Problem
Implementation of the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.
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.
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks (AAAI'20)
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Python package designed to provide the essentials tools for off-the-grid inverse problem. This is the bedrock for future GUI implementation.
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.
Frank-Wolfe Algorithm : Find User Equilibrium in Traffic Assignment
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.
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