Radu-Alexandru Dragomir

- Post-doctoral researcher in computational mathematics -

About me

Since September 2021, I am a post-doctoral researcher working at Université Catholique de Louvain in Belgium under the supervision of Yurii Nesterov.

Prior to that, I did my PhD jointly with Jérôme Bolte at Université Toulouse Capitole and Alexandre d'Aspremont within the SIERRA team, which is part of the D.I. ENS in Paris.

Email: radu.dragomir [at] uclouvain.be


I study optimization methods for solving large-scale problems from various areas of engineering and science such as signal processing and machine learning. I mainly focus on algorithms which use non-Euclidean updates in order to adapt to the geometry of the objective function (such as mirror descent and Bregman methods). I also work on the computer-aided analysis technique called performance estimation.

Refereed publications

  • R-A. Dragomir, M. Even, H. Hendrikx. Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction.
    International Conference on Machine Learning, 2021. [PMLR] [arxiv] [slides]
  • R-A. Dragomir, A. B. Taylor, A. d'Aspremont, J. Bolte. Optimal Complexity and Certification of Bregman First-Order Methods.
    Mathematical Programming, 2021. [springer] [arxiv] [GeoGebra demo] [code]
  • R-A. Dragomir, A. d'Aspremont, J. Bolte. Quartic First-Order Methods for Low-Rank Minimization.
    Journal of Optimization Theory and Applications, 2021. [springer] [arxiv] [code]


  • R-A. Dragomir, Bregman Gradient Methods for Relatively-Smooth Optimization.
    PhD thesis, 2021. Advised by Jérôme Bolte and Alexandre d'Aspremont. [pdf] [slides] [video]