Accelerated method of finding for the minimum of arbitrary Lipschitz convex function

The goal of the paper is development of an optimization method with the superlinear convergence rate for a nonsmooth convex function. For optimization an approximation is used that is similar to the Steklov integral averaging. The difference is that averaging is performed over a variable-dependent set, that is called a set-valued mapping (SVM) satisfying simple conditions. Novelty approach is that with such an approximation we obtain twice continuously differentiable convex functions, for optimizations of which are applied methods of the second order. The estimation of the convergence rate of the method is given.