On the Dynamic Regret of Following the Regularized Leader: Optimism with History Pruning

This paper revisits the FTRL framework for Online Convex Optimization, demonstrating that with optimistic cost composition and strategic linearization, FTRL can achieve strong dynamic regret guarantees despite previous limitations. The authors highlight that the key to improved performance lies in synchronizing the algorithm's state with its iterates through pruning, enabling more agile and effective updates in dynamic environments.