PF-AGD is the first parameter-free deterministic accelerated first-order method with Õ(ε^{-5/3} log(1/ε)) complexity for smooth non-convex optimization.
Improved complexity for smooth nonconvex optimization: A two-level online learning approach with quasi-Newton methods
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A Parameter-Free First-Order Algorithm for Non-Convex Optimization with $\tilde{\mkern1mu O}(\epsilon^{-5/3})$ Global Rate
PF-AGD is the first parameter-free deterministic accelerated first-order method with Õ(ε^{-5/3} log(1/ε)) complexity for smooth non-convex optimization.