While the emerging 6G networks are anticipated to meet the high-end service quality
demands of the mobile edge users in terms of data rate and delay satisfaction, new attack …

We consider a distributed non-convex optimization where a network of agents aims at
minimizing a global function over the Stiefel manifold. The global function is represented as …

In this article, we study decentralized nonconvex finite-sum minimization problems
described over a network of nodes, where each node possesses a local batch of data …

This paper studies delayed stochastic algorithms for weakly convex optimization in a
distributed network with workers connected to a master node. Recently, Xu~ et~ al.~ 2022 …

We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT)
for nonconvex stochastic composite problems, with data heterogeneously distributed on …

We consider decentralized gradient-free optimization of minimizing Lipschitz continuous
functions that satisfy neither smoothness nor convexity assumption. We propose two novel …

We focus on a class of non-smooth optimization problems over the Stiefel manifold in the
decentralized setting, where a connected network of $ n $ agents cooperatively minimize a …

We propose a first-order fast algorithm for the weighted max-min fair (MMF) multi-group
multicast beamforming problem in large-scale systems. Utilizing the optimal multicast …

In this article, a special class of distributed stochastic nonconvex optimization problem is
investigated. Each agent in the network only has access to a local stochastic weakly convex …

This paper develops a neurodynamic model for distributed nonconvex-constrained
optimization. In the distributed constrained optimization model, the objective function and …