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

collaborators
Showing 2022Show all

9 papers · 1 filter

eess.SY2022★ 2 cited

Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics

Wenjun Mei, Julien M. Hendrickx, Ge Chen +2

Mechanistic and tractable mathematical models play a key role in understanding how social influence shapes public opinions. Recently, a weighted-median mechanism has been proposed…

math.OC2022

On the Worst-Case Analysis of Cyclic Coordinate-Wise Algorithms on Smooth Convex Functions

Yassine Kamri, Julien M. Hendrickx, François Glineur

We propose a unifying framework for the automated computer-assisted worst-case analysis of cyclic block coordinate algorithms in the unconstrained smooth convex optimization setup.…

math.OC2022★ 5 cited

Automated Performance Estimation for Decentralized Optimization via Network Size Independent Problems

Sebastien Colla, Julien M. Hendrickx

We develop a novel formulation of the Performance Estimation Problem (PEP) for decentralized optimization whose size is independent of the number of agents in the network. The PEP…

cs.MA2022

Resource allocation in open multi-agent systems: an online optimization analysis

Renato Vizuete, Charles Monnoyer de Galland, Julien M. Hendrickx +2

The resource allocation problem consists of the optimal distribution of a budget between agents in a group. We consider such a problem in the context of open systems, where agents…

cs.MA2022★ 4 cited

Random Coordinate Descent for Resource Allocation in Open Multi-Agent Systems

Charles Monnoyer de Galland, Renato Vizuete, Julien M. Hendrickx +2

We propose a method for analyzing the distributed random coordinate descent algorithm for solving separable resource allocation problems in the context of an open multiagent system…

math.OC2022★ 7 cited

Automatic Performance Estimation for Decentralized Optimization

Sebastien Colla, Julien M. Hendrickx

We present a methodology to automatically compute worst-case performance bounds for a large class of first-order decentralized optimization algorithms. These algorithms aim at mini…