This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

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 …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

We consider decentralized stochastic optimization with the objective function (eg data
samples for machine learning tasks) being distributed over n machines that can only …

Technological advances in ad hoc networking and the availability of low-cost reliable
computing, data storage, and sensing devices have made scenarios possible where the …

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 work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

This paper proposes a novel class of distributed continuous-time coordination algorithms to
solve network optimization problems whose cost function is a sum of local cost functions …

This work addresses decentralized online optimization in nonstationary environments. A
network of agents aim to track the minimizer of a global, time-varying, and convex function …

We study a variation of vanilla stochastic gradient descent where the optimizer only has
access to a Markovian sampling scheme. These schemes encompass applications that …