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 …

We present a new class of decentralized first-order methods for nonsmooth and stochastic
optimization problems defined over multiagent networks. Considering that communication is …

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 …

In this paper, we consider distributed optimization problems over a multiagent network,
where each agent can only partially evaluate the objective function, and it is allowed to …

We consider a class of popular distributed non-convex optimization problems, in which
agents connected by a network ς collectively optimize a sum of smooth (possibly non …

Resource allocation and scheduling in multi-agent systems present challenges due to
complex interactions and decentralization. This survey paper provides a comprehensive …

In this paper, we propose a perturbed proximal primal–dual algorithm (PProx-PDA) for an
important class of linearly constrained optimization problems, whose objective is the sum of …

In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual
Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for …

In this article, we propose a novel dual inexact splitting algorithm (DISA) for distributed
convex composite optimization problems, where the local loss function consists of a smooth …

In this paper, we consider distributed optimization problems over a multi-agent network,
where each agent can only partially evaluate the objective function, and it is allowed to …