  Science about optimization methods is rapidly developing today. In machine
learning, computer vision, biology, medicine, construction and in many other
different areas optimization methods have vast popularity and they appear as
important tool. One of the most important goals in optimization: create some
"universal" method, which will have good performance in all problems regardless
smoothness of a task, computation precision of gradient and other parameters
which characterize a problem. In this thesis we propose a method which is
"universal" for different problems and, at the same time, is simple for
understanding.
