Preprint
Jul 2026
Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension
This work proposes a computationally efficient algorithm that achieves the optimal $O(1/\sqrt{T})$ convergence rate, matching the lower bound, and closes the existing gap in one dimension, providing the first sharp rate guarantee in this setting.
A. Carpentier, Chloé Rouyer, Alexandre B. Tsybakov et al.
· 0 citations