The alternating direction method of multipliers (ADMM) has emerged as a powerful
technique for large-scale structured optimization. Despite many recent results on the …

We propose a methodology for studying the performance of common splitting methods
through semidefinite programming. We prove tightness of the methodology and demonstrate …

Consider a set of N agents seeking to solve distributively the minimization problem inf∞ Σ
n= 1 N fn (x) where the convex functions fn are local to the agents. The popular Alternating …

Complex ADMM-Net, a complex-valued neural network architecture inspired by the
alternating direction method of multipliers (ADMM), is designed for interference removal in …

Microgrids, as individual controllable entities that can operate either islanded from or
interconnected to main power grid, have emerged as a promising solution to improving …

In this work, we study the problem of unconstrained convex optimization in a fully distributed
multiagent setting, which includes asynchronous computation and lossy communication. In …

This paper presents optimal parameter selection and preconditioning of the alternating
direction method of multipliers (ADMM) algorithm for a class of distributed quadratic …

We extend a multi-agent convex-optimization algorithm named Newton-Raphson consensus
to a network scenario that involves directed, asynchronous and lossy communications. We …

In this paper we analyze a zeroth-order proximal stochastic gradient method suitable for the
minimization of weakly convex stochastic optimization problems. We consider nonsmooth …

This paper presents a new method for estimating the eigenvalues of the Laplacian matrix
associated with the graph describing the network topology of a multi-agent system. Given an …