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 …

Motivated by large-scale optimization problems arising in the context of machine learning,
there have been several advances in the study of asynchronous parallel and distributed …

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 …

Distributed data-parallel algorithms aim to accelerate the training of deep neural networks
by parallelizing the computation of large mini-batch gradient updates across multiple nodes …

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 …

In this article, we develop distributed iterative algorithms that enable the components of a
multicomponent system, each with some integer initial value, to asymptotically compute the …

This work is concerned with stochastic consensus conditions of multi-agent systems with
both time-delays and measurement noises. For the case of additive noises, we develop …

In this paper a distributed control strategy for coordinating multiple battery energy storage
systems to support frequency regulation in power systems with high penetration of …

This paper considers distributed estimation methods to enable the formation of Unmanned-
Aerial-Vehicles (UAVs) that track a moving target. The UAVs (or agents) are equipped with …

Most algorithms for distributed averaging only guarantee asymptotic convergence. This
paper introduces a distributed protocol that allows nodes to find the exact average of the …