In this article, we consider a strongly convex finite-sum minimization problem over a
decentralized network and propose a communication-efficient decentralized Newton's …

This paper considers the problem of decentralized consensus optimization over a network,
where each node holds a strongly convex and twice-differentiable local objective function …

This paper tackles a challenging decentralized consensus optimization problem defined
over a network of interconnected devices. The devices work collaboratively to solve a …

We consider the problem of decentralized consensus optimization, where the sum of n
smooth and strongly convex functions are minimized over n distributed agents that form a …

In this paper, we devise a communication-efficient decentralized algorithm, named as
communication-censored alternating direction method of multipliers (ADMM)(COCA), to …

In this paper, we consider minimizing a sum of local convex objective functions in a
distributed setting, where the cost of communication and/or computation can be expensive …

Distributed optimization in resource constrained devices demands both communication
efficiency and fast convergence rates. Newton-type methods are getting preferable due to …

In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of
cost functions over a decentralized network. We first develop a general algorithmic …

We consider a standard distributed consensus optimization problem where a set of agents
connected over an undirected network minimize the sum of their individual (local) strongly …

Decentralized learning has recently attracted much research attention because of its
robustness and user privacy advantages. Decentralized algorithms play central roles in …