Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator
Ускоренные безградиентные методы оптимизации с неевклидовым проксимальным оператором
Résumé
We propose an accelerated gradient-free method with a non-Euclidean proximal operator associated with the p-norm (1 ⩽ p ⩽ 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.
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