Projection Free Online

Projection-free online learning aims to improve the efficiency of online optimization algorithms by replacing computationally expensive projection operations with more efficient alternatives, such as linear optimization oracles. Current research focuses on extending these methods to handle non-stationary data, curved spaces (Riemannian manifolds), and delayed feedback, often employing algorithms like Online Frank-Wolfe and variants of Online Gradient Descent. This research is significant because it enables the application of online learning to high-dimensional problems and complex constraint sets where traditional projection-based methods are impractical, impacting fields like control systems and online decision-making.