2 papers
math.OC2026
Information-Theoretic Upper Bounds for Deterministic Noise in Zeroth-Order Convex Optimization
Dmitry Pasechnyuk-Vilensky, Igor Pavlov, Martin TakÃ¡Ä +1
We study deterministic adversarial noise in zeroth-order convex optimization on Euclidean balls. The maximum admissible level of noise is the largest uniform error in function-valu…
math.OC2026
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…