3 citations · 4 across the 2 of their papers we have counts for
5 papers
Lipschitz-Free Mirror Descent Methods for Relatively Strongly Convex Functions with/without Absolute and Relative Inexactness
Mohammad S. Alkousa, Fedor S. Stonyakin
In this paper, we analyze the mirror descent algorithm for non-smooth optimization problems in which the objective function is relatively strongly convex, without relying on the st…
Adaptation to Inexactness for some Gradient-type Methods
Fedor S. Stonyakin
We introduce a notion of inexact model of a convex objective function, which allows for errors both in the function and in its gradient. For this situation, a gradient method with…
Adaptive Mirror Descent Methods for Convex Programming Problems with delta-subgradients
Fedor S. Stonyakin
We propose some adaptive mirror descent dethods for convex programming problems with delta-subgradients and prove some theoretical results.
Some adaptive proximal method for a special class of variational inequalities and related problems
Fedor S. Stonyakin
An adaptive proximal method for a special class of variational inequalities and related problems is proposed. For example, the so-called mixed variational inequalities and composit…
Some adaptive analog of Yu. E. Nesterov's method for variational inequalities with a strongly monotone operator
Fedor S. Stonyakin
An adaptive analogue of the Yu. E. Nesterov method for variational inequalities with a strongly monotone operator is proposed. Some estimates are obtained for the parameters determ…