In this paper, we study the problem of decentralized optimization to minimize a finite sum of
local convex cost functions over an undirected network. Compared with the existing works …

In this article, we study convex optimization problems where agents of a network
cooperatively minimize the global objective function which consists of multiple local …

Decentralized stochastic optimization has become a crucial tool for addressing large-scale
machine learning and control problems. In decentralized algorithms, all computing nodes …

Decentralized optimization problems consist of multiple agents connected by a network. The
agents have each local cost function, and the goal is to minimize the sum of the functions …

This article is concerned with minimizing the average of cost functions over a network, in
which agents may communicate and exchange information with each other. We consider the …

Existing decentralized algorithms usually require knowledge of problem parameters for
updating local iterates. For example, the hyperparameters (such as learning rate) usually …

We consider decentralized stochastic optimization problems, where a network of n nodes
cooperates to find a minimizer of the globally-averaged cost. A widely studied decentralized …

In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and
strongly convex cost functions when the functions are distributed over a directed network of …

We consider the distributed optimization problem where agents, each possessing a local
cost function, collaboratively minimize the average of the cost functions over a connected …

In this paper, we introduce an asynchronous decentralized accelerated stochastic gradient
descent type of algorithm for decentralized stochastic optimization. Considering …