activity
20062026
most citedExact Worst-case Performance of First-order Methods for Composite Convex Optimization

109 citations · 430 across the 61 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

math.OC2020

Local Network Identifiability with Partial Excitation and Measurement

Antoine Legat, Julien M. Hendrickx

This work focuses on the identifiability of dynamical networks with partial excitation and measurement: a set of nodes are interconnected by unknown transfer functions according to…

eess.SY2020

Optimal Minimax Mobile Sensor Scheduling Over a Network

Samuel C. Pinto, Sean B. Andersson, Julien M. Hendrickx +1

We investigate the problem of monitoring multiple targets using a single mobile sensor, with the goal of minimizing the maximum estimation error among all the targets over long tim…

math.OC2020

Stability of Decentralized Gradient Descent in Open Multi-Agent Systems

Julien M. Hendrickx, Michael G. Rabbat

The aim of decentralized gradient descent (DGD) is to minimize a sum of functions held by interconnected agents. We study the stability of DGD in open contexts where agents can…

cs.MA2020★ 4 cited

Evolution of Descriptors in Open Multi-Agent Systems for Gossip Interactions and Beyond

Charles Monnoyer de Galland, Samuel Martin, Julien M. Hendrickx +1

We consider open multi-agent systems, which are systems subject to frequent arrivals and departures of agents while the studied process takes place. We study the behavior of all-to…

stat.CO2020

Low-rank multi-parametric covariance identification

Antoni Musolas, Estelle Massart, Julien M. Hendrickx +2

We propose a differential geometric construction for families of low-rank covariance matrices, via interpolation on low-rank matrix manifolds. In contrast with standard parametric…

eess.SY2020

Multi-Agent Persistent Monitoring of Targets with Uncertain States

Samuel C. Pinto, Sean B. Andersson, Julien M. Hendrickx +1

We address the problem of persistent monitoring, where a finite set of mobile agents has to persistently visit a finite set of targets. Each of these targets has an internal state…