- (March 2022) I have submitted my first journal paper to IEEE Transactions on Automatic Control
- Sebastien Colla, Julien M. Hendrickx, “Automatic Performance Estimation for Decentralized Optimization”, IEEE Transactions on Automatic Control, submitted. [PDF]
- We have extended the PESTO toolbox to make our results easy to use by anyone that would like to analyze worst-case performance of decentralized algorithms.
- 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.
Here is a Vulgarized Summary of my research project written in March 2021.