In this article, we focus on solving a distributed convex optimization problem in a network,
where each agent has its own convex cost function and the goal is to minimize the sum of …

This paper proposes a novel proximal-gradient algorithm for a decentralized optimization
problem with a composite objective containing smooth and nonsmooth terms. Specifically …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

There is growing interest in large-scale machine learning and optimization over
decentralized networks, eg in the context of multi-agent learning and federated learning …

This survey paper studies deterministic control systems that integrate three of the most active
research areas during the last years:(1) online learning control systems,(2) distributed …

We consider the problem of multiagent optimization wherein an unknown subset of agents
suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a …

We study the decentralized distributed computation of discrete approximations for the
regularized Wasserstein barycenter of a finite set of continuous probability measures …

Unsupervised feature selection is an important machine learning task and thus attracts
increasingly more attention. However, due to the absence of labels, unsupervised feature …

We consider stochastic convex optimization problems with affine constraints and develop
several methods using either primal or dual approach to solve it. In the primal case, we use …

We consider a class of popular distributed non-convex optimization problems, in which
agents connected by a network ς collectively optimize a sum of smooth (possibly non …