On a Frank-Wolfe Approach for Abs-Smooth Optimization

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.