Direct finite-horizon high-probability analysis of normalized two-point Gaussian zeroth-order gradient descent yields explicit query complexities for strongly convex, convex, and non-convex smooth objectives under smoothing-radius conditions.
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High-Probability Guarantees for Random Zeroth-Order Gradient Descent on Smooth Functions
Direct finite-horizon high-probability analysis of normalized two-point Gaussian zeroth-order gradient descent yields explicit query complexities for strongly convex, convex, and non-convex smooth objectives under smoothing-radius conditions.