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math.OC2026
Adaptive two-points zero-order accelerated methods for stochastic convex optimization problems
Yuriy Dorn, Osman Osmanov, Timofei Loginov +3
We study stochastic convex optimization, where each sampled function can be evaluated at two arbitrary points. Our accelerated method chooses step sizes, batch sizes and smoothing…
math.OC2026
Minimax Oracle Call Complexity, Rounds and Admissible Noise in Zeroth-Order Optimization of Highly Smooth Functions
Timofei Loginov, Darina Dvinskikh, Nazarii Tupitsa +3
We study zeroth-order optimization of convex and strongly convex functions with Hölder smoothness of order from values corrupted by random noise with bias independent of the…
math.OC2026
Lower Bounds For Gradient-Free Convex Optimization And Convex-Concave Saddle-Point Problems
Yuriy Dorn, Darina Dvinskikh, Timofei Loginov +4
We study the query complexity of optimization with exact scalar-value information. For globally -smooth convex functions on with a minimizer in a Euclidean ball of…