Projection-Free Methods for Stochastic Constrained Compositional Optimization

A new arXiv paper develops projection-free algorithms for stochastic optimization problems whose objectives are nested compositions of smooth functions over a closed convex decision set. The work targets the multi-level compositional setting, where gradients must be estimated through several layers of functions. It aims to avoid costly projection steps while still handling constraints.