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 …

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 …

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 …

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

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 …

We consider constraint-coupled optimization problems in which agents of a network aim to
cooperatively minimize the sum of local objective functions subject to individual constraints …

Decentralized optimization is gaining increased traction due to its widespread applications
in large-scale machine learning and multiagent systems. The same mechanism that enables …

We study distributed composite optimization over networks: agents minimize a sum of
smooth (strongly) convex functions–the agents' sum-utility–plus a nonsmooth (extended …

This article studies the problem of distributed optimization in multiagent systems where each
agent seeks to minimize the sum of all agents' objective functions using only local …

The presence of embedded electronics and communication capabilities as well as sensing
and control in smart devices has given rise to the novel concept of cyber-physical networks …