We study structured convex optimization problems, with additive objective $ r:= p+ q $,
where $ r $ is ($\mu $-strongly) convex, $ q $ is $ L_q $-smooth and convex, and $ p $ is …

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

We consider the problem of solving stochastic monotone variational inequalities with a
separable structure using a stochastic first-order oracle. Building on standard extragradient …

Many convex optimization problems have structured objective functions written as a sum of
functions with different oracle types (eg, full gradient, coordinate derivative, stochastic …

This work is devoted to solving the composite optimization problem with the mixture oracle:
for the smooth part of the problem, we have access to the gradient, and for the non-smooth …

In this work, we focuses on the following saddle point problem $\min_x\max_y p (x)+ R (x, y)-
q (y) $ where $ R (x, y) $ is $ L_R $-smooth, $\mu_x $-strongly convex, $\mu_y $-strongly …

We revisit the smooth convex-concave bilinearly-coupled saddle-point problem of the form
$\min_x\max_y f (x)+\langle y,\mathbf {B} x\rangle-g (y) $. In the highly specific case where …

Variational inequalities offer a versatile and straightforward approach to analyzing a broad
range of equilibrium problems in both theoretical and practical fields. In this paper, we …

The saddle-point optimization problems have a lot of practical applications. This paper
focuses on such non-smooth problems in decentralized case. This work contains …

Optimization problems are ubiquitous in all quantitative scientific disciplines, from computer
science and engineering to operations research and economics. Developing algorithms for …