Sébastien Colla


Ph.D. student
FRIA Research fellow

INMA - Mathematical Engineering Department
ICTEAM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics
UCLouvain - Université Catholique of Louvain
My advisor is Julien Hendrickx
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  • (March 2024) MT180 Contest
    I had the chance to take part in the French-speaking science popularization contest “Ma thèse en 180 secondes”, during which I tried to best explain my subject and research results 📄🔎 Summing up 4 years of research in 3 minutes was quite a challenge ⏱
    YouTube video.

  • (March 2024) I have submitted a paper to Open Journal of Mathematical Optimization
    S. Colla and J. M. Hendrickx, “Exploiting Agent Symmetries for Performance Analysis of Distributed Optimization Methods”, 2024. [PDF, arXiv ]

  • (February 2024) I have submitted a paper to MTNS conference
    S. Colla and J. M. Hendrickx, “On the Optimal Communication Weights in Distributed Optimization Algorithms”, submitted to MTNS 2024. [PDF, arXiv ]

  • (March 2023) My first journal paper has been accepted in IEEE Transactions on Automatic Control
    S. Colla, J. M. Hendrickx, “Automatic Performance Estimation for Decentralized Optimization”, IEEE Transactions on Automatic Control, accepted, 2023. [PDF, arXiv, DOI]

  • (February 2023) PEP-talks
    We have organized the first PEP-talks workshop in Louvain-la-Neuve. This workshop focused on the theory and applications of Performance Estimation Problems (PEP) in continuous optimization.

Research Interests

  • Decentralized Optimization
  • Computer-aided worst-case analyses
  • Multi-Agent system.

The goal of my thesis is to boost the research in decentralized optimization by creating tools allowing one to automatically compute the worst-case performance of any decentralized optimization algorithm, and to identify the bottleneck instances, providing insight on what limits the performance. This will contribute to a better understanding of decentralized optimization algorithms and enable rapid exploration of new algorithms ideas and their iterative improvement.

This project relies on the Performance Estimation Problem (PEP) approach developed by Adrien Taylor for classical centralized optimization during his thesis. It relies on formulating the evaluation of an algorithm worst-case performance as an optimization problem itself, whose variables are the objective functions and initial conditions. Such problems are very complex, but have been shown to be solvable exactly. The PESTO toolbox allows to write and solve them easily with Matlab. The equivalent Python toolbox, called PEPit, has been released in 2022.

Here is a Vulgarized Summary of my research project written in March 2021.