Accelerated gradient-free optimization methods with a non-Euclidean proximal operator

We propose an accelerated gradient-free method with a non-Euclidean proximal operator associated with the $p$-norm ($1\leqslant p\leqslant 2$). We obtain estimates for the rate of convergence of the method under low noise arising in the calculation of the function value. We present the results of computational experiments.