.. toctree:: :hidden: :maxdepth: 1 About Research Teaching Contact Sébastien Colla =============== .. container:: clearer .. figure:: data/images/seb.jpg :align: left :width: 222px | | **Ph.D. student** | |FRIA_link| Research fellow | | |INMA_link| - Mathematical Engineering Department | |ICTEAM_link| - Institute of Information and Communication Technologies, Electronics and Applied Mathematics | |UCLouvain_link| - Université Catholique of Louvain | My advisor is |Julien_link| | **Quick links** | |scholar_logo| |scholar_link| | |github| |github_link| | |linkedin_logo| |linkedin_link| News ----- + (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 <_static/MTNS2024.pdf>`_, `arXiv `_ ] |vspace| + (September 2023) Check out our tutorial paper for CDC 2023 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. [`PDF <_static/CDC_2023_Tutorial_PEP.pdf>`_, `arXiv `_ ] |vspace| + (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 <_static/Colla_Automatic_Performance_Estimation_for_Decentralized_Optimization.pdf>`_, `arXiv `_, `DOI `_] |vspace| + (February 2023) We have organized the first `PEP-talks `_ workshop in Louvain-la-Neuve. This workshop is 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_link| 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 <_static/research_vulagrized_summary.pdf>`_ written in March 2021. .. |vspace| raw:: latex \vspace{3mm} .. |cdc_link| raw:: html PDF .. |Julien_link| raw:: html Julien Hendrickx .. |ICTEAM_link| raw:: html ICTEAM .. |UCLouvain_link| raw:: html UCLouvain .. |FRIA_link| raw:: html FRIA .. |scholar_link| raw:: html Google Scholar .. |github_link| raw:: html GitHub .. |INMA_link| raw:: html INMA .. |linkedin_link| raw:: html Linkedin .. |Adrien_link| raw:: html Adrien Taylor .. |PEP| raw:: html PEP .. |PESTO| raw:: html PESTO .. |linkedin_logo| image:: data/images/linkedin.png :width: 15px :class: no-scaled-link .. |scholar_logo| image:: data/images/Google_Scholar_logo.png :width: 15px :class: no-scaled-link