Frank Wolfe Algorithm

The Frank-Wolfe algorithm is a projection-free method for constrained optimization, aiming to efficiently find optimal solutions without computationally expensive projection steps. Current research focuses on extending its applicability to various settings, including online and federated learning, stochastic and non-convex optimization, and problems with functional constraints, often employing variants like away-steps or incorporating variance reduction techniques. This algorithm's significance lies in its ability to handle high-dimensional and complexly constrained problems, finding applications in diverse fields such as machine learning, signal processing, and optimal transport, where its efficiency offers significant advantages over projection-based methods.