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)
Python package designed to provide the essentials tools for off-the-grid inverse problem. This is the bedrock for future GUI implementation.
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
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
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
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.
Zeroth order Frank Wolfe algorithm. Project for the Optimization for Data Science exam.
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.
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