Research

Follow me on scholar_logo Google Scholar to be informed of my new publications.

Paper and proceedings

  • S. Colla and J. M. Hendrickx, “Exploiting Agent Symmetries for Performance Analysis of Distributed Optimization Methods”, submitted to Open Journal of Mathematical Optimization, 2024.

  • S. Colla and J. M. Hendrickx, “On the Optimal Communication Weights in Distributed Optimization Algorithms”, submitted to MTNS 2024.

  • A. Rubbens, N. Bousselmi, S. Colla and J. M. Hendrickx, “Interpolation Constraints for Computing Worst-Case Bounds in Performance Estimation Problems”, tutorial paper for 62th IEEE Conference on Decision and Control (CDC), 2023.

  • S. Colla, J. M. Hendrickx, “Automatic Performance Estimation for Decentralized Optimization”, IEEE Transactions on Automatic Control, accepted, 2023.

  • S. Colla, J. M. Hendrickx, “Automated Performance Estimation for Decentralized Optimization via Network Size Independent Problems”, in 61th IEEE Conference on Decision and Control (CDC), 2022.

  • S. Colla, J. M. Hendrickx, “Automated Worst-Case Performance Analysis of Decentralized Gradient Descent”, in 60th IEEE Conference on Decision and Control (CDC), 2021.

Talks

  • 20th EUROpt Workshop, August 2023 in Budapest (Hungary).
    Exploiting Agent Symmetries for Automatic Performance Analysis of Distributed Optimization Methods. [slides]

Poster

Toolbox

The PESTO toolbox allows to easily formulate and solve Performance Estimation Problems (PEP) in order to automatically find the worst-case performance of optimization methods. I have contributed to this toolbox by implementing my recent results on PEP for decentralized optimization. This allows anyone to easily analyze worst-case performance of many decentralized algorithms.

Master Thesis

  • Sebastien Colla, “Data fusion in Open Multi-Agent Systems for decentralized estimation”, École polytechnique de Louvain, Université catholique de Louvain, 2020. (Advisor: Julien M. Hendrickx) [PDF].