We consider the task of decentralized minimization of the sum of smooth strongly convex
functions stored across the nodes of a network. For this problem, lower bounds on the …

Decentralization is a promising method of scaling up parallel machine learning systems. In
this paper, we provide a tight lower bound on the iteration complexity for such methods in a …

Gradient-free/zeroth-order methods for black-box convex optimization have been
extensively studied in the last decade with the main focus on oracle calls complexity. In this …

We consider the task of minimizing the sum of smooth and strongly convex functions stored
in a decentralized manner across the nodes of a communication network whose links are …

We consider stochastic convex optimization problems with affine constraints and develop
several methods using either primal or dual approach to solve it. In the primal case, we use …

We consider distributed convex-concave saddle point problems over arbitrary connected
undirected networks and propose a decentralized distributed algorithm for their solution. The …

In this paper, we focus on solving the decentralized optimization problem of minimizing the
sum of n objective functions over a multi-agent network. The agents are embedded in an …

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 …

Decentralized optimization over time-varying graphs has been increasingly common in
modern machine learning with massive data stored on millions of mobile devices, such as in …

Modern large-scale finite-sum optimization relies on two key aspects: distribution and
stochastic updates. For smooth and strongly convex problems, existing decentralized …