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Near-Optimal Deterministic Exact-Value Complexity for Smooth Convex Optimization
We study the deterministic oracle complexity of smooth convex optimization when the algorithm receives only exact function values. The objective is a globally $β$-smooth convex function, all queries and the final output are restricted to the Euclidean ball of radius $R$, and the unique minimizer lie
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