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This paper is devoted to the study of the solution of a stochastic convexblack box optimization problem. Where the black box problem means that thegradient-free oracle only returns the value of objective function, not itsgradient. We consider non-smooth and smooth setting of the solution to theblack box problem under adversarial stochastic noise. For two techniquescreating gradient-free methods: smoothing schemes via $L_1$ and $L_2$randomizations, we find the maximum allowable level of adversarial stochasticnoise that guarantees convergence. Finally, we analyze the convergence behaviorof the algorithms under the condition of a large value of noise level.

Stochastic Adversarial Noise in the "Black Box" Optimization Problem