EF1 – Extracting dynamical Laws from Complex Data



On a Frank-Wolfe Approach for Abs-Smooth Optimization

Project Heads

Sebastian Pokutta, Andrea Walther, Zev Woodstock

Project Members


Project Duration

01.01.2023 − 31.12.2025

Located at

HU Berlin


Motivated by nonsmooth problems in machine learning, we solve the problem of minimizing an abs-smooth function subject to closed convex constraints. New theory and algorithms are developed using linear minimization oracles to enforce constraints and abs-linearization methods to handle nonsmoothness.

External Website

Related Publications

Related Pictures