Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

Recently, there has been growing interest in solving consensus optimization problems in a
multiagent network. In this paper, we develop a decentralized algorithm for the consensus …

Distributed optimization algorithms are highly attractive for solving big data problems. In
particular, many machine learning problems can be formulated as the global consensus …

Higher-order low-rank tensors naturally arise in many applications including hyperspectral
data recovery, video inpainting, seismic data recon-struction, and so on. We propose a new …

Nonconvex optimization arises in many areas of computational science and engineering.
However, most nonconvex optimization algorithms are only known to have local …

Aiming at solving large-scale optimization problems, this paper studies distributed
optimization methods based on the alternating direction method of multipliers (ADMM). By …

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 …

We propose Directed-Distributed Projected Subgradient (D-DPS) to solve a constrained
optimization problem over a multi-agent network, where the goal of agents is to collectively …

We propose an asynchronous, decentralized algorithm for consensus optimization. The
algorithm runs over a network in which the agents communicate with their neighbors and …

Although comprehensive knowledge of cyber-physical systems (CPS) is becoming a must
for researchers, practitioners, system designers, policy makers, system managers, and …