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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2representative citing papers
Natural Riemannian gradient descent enables optimization of functional tensor networks for general losses and shows improved convergence on classification tasks.
citing papers explorer
-
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.
-
Natural Riemannian gradient for learning functional tensor networks
Natural Riemannian gradient descent enables optimization of functional tensor networks for general losses and shows improved convergence on classification tasks.