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 …

In decentralized optimization, nodes cooperate to minimize an overall objective function that
is the sum (or average) of per-node private objective functions. Algorithms interleave local …

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 …

We study nonconvex distributed optimization in multiagent networks with time-varying
(nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

This article provides an overview of distributed gradient methods for solving convex machine
learning problems of the form minxRn (1/m) ΣR i= 1 fi (x) in a system consisting of mm …

We consider distributed optimization by a collection of nodes, each having access to its own
convex function, whose collective goal is to minimize the sum of the functions. The …

In decentralized consensus optimization, a connected network of agents collaboratively
minimize the sum of their local objective functions over a common decision variable, where …

In this letter, we study distributed optimization, where a network of agents, abstracted as a
directed graph, collaborates to minimize the average of locally known convex functions …