This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for
solving convex composite optimization problems over undirected and connected networks …

This article aims to address the problem of distributed energy management for both the
generation and demand sides in smart grid. Different from many existing works, we …

This paper designs continuous-time algorithms with linear convergence for solving
distributed convex optimization problems without a strongly convex condition. The proposed …

In this paper, we address a novel and comprehensive social welfare maximization (SWM)
problem for the optimal energy management in a smart grid. The objective is to maximize the …

This paper considers a class of multi-agent distributed convex optimization with a common
set of constraints and provides several continuous-time neurodynamic approaches. In …

Existing asynchronous distributed optimization algorithms often use diminishing step-sizes
that cause slow practical convergence, or use fixed step-sizes that depend on and decrease …

We study decentralized smooth optimization problems over compact submanifolds.
Recasting it as a composite optimization problem, we propose a decentralized Douglas …

Distributed optimization aims to effectively complete specified tasks through cooperation
among multi-agent systems, which has achieved great success in large-scale optimization …

In this article, we investigate a constrained composition optimization problem in a directed
communication network. Each agent is equipped with a local objective function composed of …

The nonsmooth finite-sum minimization is a fundamental problem in machine learning. This
article develops a distributed stochastic proximal-gradient algorithm with random reshuffling …