Francisco Patitucci

Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism

A recent breakthrough in nonconvex optimization is the online-to-nonconvex conversion framework of Cutkosky et al. (2023), which reformulates the task of finding an $\varepsilon$-first-order stationary point as an online learning problem. When both the gradient and the Hessian are Lipschitz continu…