Author

A. Akhavan

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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

Preprint Jul 2026

Gradient-free stochastic optimization of derivatives under strong convexity

A kernel-based estimator of $\nabla f$ is proposed and the projected stochastic gradient algorithm driven by this estimator is analyzed, establishing a minimax lower bound and a non-asymptotic upper bound on the optimization error.

A. Akhavan, Sirine Louati, Alexandre B. Tsybakov · 0 citations