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 …

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 …

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 …

Recently, there has been growing interest in solving consensus optimization problems in a
multiagent network. In this paper, we develop a decentralized algorithm for the consensus …

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 …

Nature provides splendid examples of real-time learning and adaptation behavior that
emerges from highly localized interactions among agents of limited capabilities. For …

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The
nodes interact with each other on a local level and diffuse information across the network to …

This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …