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
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