Distributed optimization provides a framework for deriving distributed algorithms for a variety
of multi-robot problems. This tutorial constitutes the first part of a two-part series on …

We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …

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

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

This paper develops a distributed optimization strategy with guaranteed exact convergence
for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy …

This article reviews significant advances in networked signal and information processing
(SIP), which have enabled in the last 25 years extending decision making and inference …

We study distributed optimization to minimize a sum of smooth and strongly-convex
functions. Recent work on this problem uses gradient tracking to achieve linear convergence …

This article studies a class of nonsmooth decentralized multiagent optimization problems
where the agents aim at minimizing a sum of local strongly-convex smooth components plus …

We study distributed composite optimization over networks: agents minimize a sum of
smooth (strongly) convex functions–the agents' sum-utility–plus a nonsmooth (extended …

Recently, there has been significant progress in the development of distributed first-order
methods. In particular, Shi et al.(2015) on the one hand and Qu and Li (2017) and Nedic et …