Emerging applications in multiagent environments such as internet-of-things, networked
sensing, autonomous systems, and federated learning, call for decentralized algorithms for …

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 …

Polyoxometalate-modified halloysite nanotubes (POM@ MHNTs) were synthesized and
doped into the polyamide (PA) matrix by interfacial polymerization to prepare doped POM …

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

Herein, a copper (II) ions-doped thin film composite (TFC-Cu) membrane with ridge-valley
morphology was prepared by interfacial polymerization (IP) via incorporating copper (II) ions …

Distributed and collaborative machine learning over emerging Internet of Things (IoT)
networks is complicated by resource constraints, device, and data heterogeneity, and the …

We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate
inexact computation and inaccurate information exchange to alleviate the problems of costly …

This paper proposes a scalable distributed policy gradient method and proves its
convergence to near-optimal solution in multi-agent linear quadratic networked systems …

We consider network-based decentralized optimization problems, where each node in the
network possesses a local function and the objective is to collectively attain a consensus …

We address distributed learning problems, both nonconvex and convex, over undirected
networks. In particular, we design a novel algorithm based on the distributed Alternating …