We consider a distributed consensus optimization problem over a server-client (federated)
network, where all clients are connected to a central server. Current distributed algorithms …

Consider a network of N decentralized computing agents collaboratively solving a
nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm …

This article considers the distributed nonconvex optimization problem of minimizing a global
cost function formed by a sum of local cost functions by using local information exchange …

Gradient tracking methods have emerged as one of the most popular approaches for solving
decentralized optimization problems over networks. In this setting, each node in the network …

Over the last decades, researchers have studied the Multi-Objective Optimization (MOO)
problem for Multi-Agent Systems (MASs). However, most of them consider the problem …

This article reports an algorithm for multiagent distributed optimization problems with a
common decision variable, local linear equality, and inequality, constraints and set …

Distributed economic dispatch (ED) has emerged as a critical issue in microgrid operations
due mainly to the wide application of various clean energy as well as energy storage units …

In this work, we introduce ADAPD, AD ecentr A lized P rimal-D ual algorithmic framework for
solving non-convex and smooth consensus optimization problems over a network of …

The computational bottleneck in distributed optimization methods, which is based on
projected gradient descent, is due to the computation of a full gradient vector and projection …

In decentralized optimization over networks, each node in the network has a portion of the
global objective function and the aim is to collectively optimize this function. Gradient …