Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity
The Composite O2NC framework addresses finding Goldstein-type stationary points in complex composite optimization problems using stochastic gradients or function values. It introduces new losses for an online learner that incorporate the convex regularizer, leading to optimal complexity bounds for both first-order and zeroth-order access methods, proving the regularizer doesn't increase computational cost.