We study nonconvex distributed optimization in multiagent networks with time-varying
(nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed …

This paper proposes a decentralized algorithm for solving a consensus optimization
problem defined in a static networked multi-agent system, where the local objective …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Consensus optimization has received considerable attention in recent years. A number of
decentralized algorithms have been proposed for convex consensus optimization. However …

Given an undirected graph G=(N, E) of agents N={1,..., N} connected with edges in E, we
study how to compute an optimal decision on which there is consensus among agents and …

Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is
a key step of treatment planning for US-guided brachytherapy. However, most current …

This is an introduction to multicast routing, which is the study of methods for routing from one
source to many destinations, or from many sources to many destinations. Multicast is …

The alternating direction method of multipliers (ADMM) has been recently recognized as a
promising optimizer for large-scale machine learning models. However, there are very few …

We study the sparse signal reconstruction problem in a wireless sensor network (WSN)
using distributed compressed sensing. The sparse signals from multiple sensors are …

The development of online algorithms to track time-varying systems has drawn a lot of
attention in the last years, in particular in the framework of online convex optimization …