In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

Advances in wired and wireless technology have necessitated the development of theory,
models, and tools to cope with the new challenges posed by large-scale control and …

This paper considers the problem of distributed optimization over time-varying graphs. For
the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing …

There has been a growing effort in studying the distributed optimization problem over a
network. The objective is to optimize a global function formed by a sum of local functions …

We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …

In this article, we focus on solving a distributed convex optimization problem in a network,
where each agent has its own convex cost function and the goal is to minimize the sum of …

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization
problem with a composite objective containing smooth and nonsmooth terms. Specifically …

This paper considers the distributed optimization problem over a network, where the
objective is to optimize a global function formed by a sum of local functions, using only local …

This paper develops a distributed optimization strategy with guaranteed exact convergence
for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy …

We study distributed optimization to minimize a sum of smooth and strongly-convex
functions. Recent work on this problem uses gradient tracking to achieve linear convergence …