arXiv stat.ML
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Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension
arXiv:2607.12938v1 Announce Type: cross Abstract: Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmic gap has persisted between known upper bounds and the $Omega(1/sqrt