Research projects

The goal of my thesis is to build a systematic procedure for finding the best performing optimization methods for a given structured problem.This research went into two directions:

First, I worked on applications where I analyzed specific structured problems to identify the best performing optimization methods.
Second, I focused on developing the systematic procedure mentioned earlier.

The projects below are in reverse chronological order.

A principled approach to automatically recommend optimization methods

With François Glineur, we propose a modeling language that aims at describing oracle-based optimization problem formulations. Using this language, we propose a framework that automatically checks whether a user-provided optimization problem fits a known template. Leveraging on an extensive library of optimization methods with their associated convergence rates, the framework allows automatically ranking applicable optimization methods according to their worst-case theoretical guarantees.

I have presented this work at ISMP2024 [slides], ALGOPT2024 [poster], INFORMS2024 [slides].

Faster optimization methods for day-ahead electricity markets

With Yassine Kamri, Mehdi Madani and François Glineur, we conduct extensive benchmarking experiments for CH prices computation on a large panel of first-order methods. We then suggest methods combined with heuristics to most efficiently solve CH pricing.

I have presented this work at EUROPT2024 [slides]. You can take a look at the [code] on Github.

Faster solver for large-scale kernel support vector machines

With Andrea Della Vecchia, Silvia Villa and François Glineur, we propose a kernel SVM solver relying on a Nyström approximation and an accelerated variant of the stochastic subgradient method to solve large-scale kernel SVMs.

This work has been accepted and presented at ECC 2023 [article], [arxiv], [slides], [code].