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

Mirror version of similar triangles method for constrained optimization problems