This paper studies the decentralized stochastic optimization problem over an undirected
network, where each agent owns its local private functions made up of two non-smooth …

This paper focuses on an emerging topic that current neural dynamics methods generally
fail to accurately solve time-varying nonconvex optimization problems especially when …

This study addresses a distributed optimization with a novel class of coupling of variables,
called clique-wise coupling. A clique is a node set of a complete subgraph of an undirected …

Recently, many deep convolutional dictionary learning-based methods, integrating the
traditional image representation methods with deep neural networks, have achieved great …

Decentralized algorithms to solve the economic dispatch problem (EDP) in smart grids have
been a significant focus within engineering research due to their advantages in scalability …

This paper proposes a distributed charging strategy that considers load constraints and
driver behaviours for plug-in electric vehicles (PEVs) in a smart charging station (SCS). Due …

In this paper, we consider the problem of distributed optimization over time-varying directed
graphs, where each agent maintains a private objective function and the goal of all agents is …

In this paper, we focus on the nonsmooth composite optimization problems over networks,
which consist of a smooth term and a nonsmooth term. Both equality constraints and box …

The complexity of background noise and the scarcity of real fault samples seriously affect the
diagnostic accuracy of the model. To address this, a noise-robust two-dimensional feature …

This article develops a novel distributed primal-dua l proximal algorithm (PDPA-Dist) and its
corresponding randomized version (Rand-PDPA-Dist) for solving convex optimization …