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 …

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

In this paper, a distributed optimization problem with general differentiable convex objective
functions is studied for continuous-time multi-agent systems with single-integrator dynamics …

This paper considers nonconvex distributed constrained optimization over networks,
modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic …

This paper studies the distributed optimization problem for continuous-time multiagent
systems with general linear dynamics. The objective is to cooperatively optimize a team …

In this paper, we study a distributed optimization problem using a subgradient projection
algorithm for multi-agent systems subject to nonidentical constraints and communication …

Consensus optimization has received considerable attention in recent years. A number of
decentralized algorithms have been proposed for convex consensus optimization. However …

In this paper we consider nonconvex optimization and learning over a network of distributed
nodes. We develop a Proximal Primal-Dual Algorithm (Prox-PDA), which enables the …