Julia implementation for various Frank-Wolfe and Conditional Gradient variants
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Updated
May 22, 2024 - Julia
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Differentiable wrapper for FrankWolfe.jl convex optimization routines
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Algorithms for Routing and Solving the Traffic Assignment Problem
This julia package addresses the membership problem for local polytopes: it constructs Bell inequalities and local models in multipartite Bell scenarios with binary outcomes.
DOT
Code for the paper Accelerated Affine-Invariant Vonvergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes
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.
Constrained Optimization using Frank-Wolfe Method
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.
The final project created for Optimization for Data Science course
This is the repo for Fast Pure Exploration via Frank-Wolfe (NeurIPS 2021).
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: [Wirth, E., Kera, H., and Pokutta, S. (2022). Approximate vanishing ideal computations at scale.](https://arxiv.org/abs/2207.01236)
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 the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.
Blind Image Deconvolution and Frank-Wolfe's algorithm to deblur a license plate for Crime Scene Investigation (CSI)
Study of four first order Frank Wolfe algorithms to solve constrained non-convex problems in the context of white box adversarial attacks.
Frank-Wolfe Algorithm : Find User Equilibrium in Traffic Assignment
Python package designed to provide the essentials tools for off-the-grid inverse problem. This is the bedrock for future GUI implementation.
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