We investigate the convergence of stochastic mirror descent (SMD) under interpolation in
relatively smooth and smooth convex optimization. In relatively smooth convex optimization …

We consider a non-convex constrained optimization problem, where the objective function is
weakly convex and the constraint function is either convex or weakly convex. To solve this …

Recently some specific classes of non-smooth and non-Lipsch-itz convex optimization
problems were considered by Yu. Nesterov and H. Lu. We consider convex programming …

We consider the problem of learning the optimal policy for infinite-horizon Markov decision
processes (MDPs). For this purpose, some variant of Stochastic Mirror Descent is proposed …

This paper is devoted to the variational inequality problems. We consider two classes of
problems, the first is classical constrained variational inequality and the second is the same …

Network utility maximization is the most important problem in network traffic management.
Given the growth of modern communication networks, we consider utility maximization …

For minimizing a sum of finitely many proper, convex and lower semicontinuous functions
over a nonempty closed convex set in an Euclidean space we propose a stochastic …

Abstract (EN) In this thesis, we develop and investigate numerical methods for solving
nonsmooth convex optimization problems in real Hilbert spaces. We construct algorithms …

Работа посвящена некоторым методам зеркального спуска для задач выпуклой
композитной оптимизации, а также теоретическим оценкам скорости сходимости для …

2.7 The results of Algorithms 2 (AMD-LG) and 5 (PAMD-LG), when Mg< 1, with m= 500, r=
100, n= 2000 and e= 0.05. The logarithmic scale on both axes in all figures2.8 The results of …