Mathieu Besançon

Mathieu Besançon

Researcher in mathematical optimization

Zuse Institute Berlin


I am a researcher in computational optimization at the Zuse Institute Berlin, in the IOL department. I am associated with the MODAL-SynLab DFG project and a member of the MATH+ Berlin Mathematics Research Center.

My research interests span the theory, methods, and algorithms for several flavours of mathematical optimization. More specifically, I have been interested in exact solution methods for constrained optimization with constraint structures we can exploit. Those include solution methods, computational models, and software in mixed-integer (non-)linear and convex optimization and in particular around the SCIP framework and Frank-Wolfe related approaches. Recently, I have been exploring applications for these classes of problems in quantum information, systems biology, networks and infrastructure, and data science.

I graduated with a double PhD (cotutelle) between Polytechnique Montréal, at the GERAD lab and Centrale Lille, at INRIA & the Cristal lab, in mathematical optimization. My thesis focused on bilevel optimization, an extension coined near-optimality robustness, and pricing for demand response in smart grids. It was co-supervised by Luce Brotcorne (INRIA) & Miguel F. Anjos (University of Edinburgh).

I am involved in several open-source projects around optimization and scientific computing in the Julia programming language and around JuMP but like looking around on new development. Before starting the PhD, I worked in various industries, from a hardware startup to steel manufacturing. I did my joint Bachelor-Master in Process Engineering at the UTC in France with a semester at the TUBS in Germany and Polytechnique Montreal.

On a personal note, I read both fiction (mostly history, detective, thrillers and fantasy) and non-fiction books, on economic policy, education, and entrepreneurship, a more detailed list can be found on goodread. I also enjoy games in various formats (tabletop, video, board, card) and cooking.

  • Bilevel Optimization
  • Convex Optimization
  • Mixed-Integer (Non-)Linear Optimization
  • Power Systems
  • Mathematical Optimization
  • Optimization and Engineering
  • Optimization Software
  • Algorithm Design
  • Joint PhD, Applied Mathematics & Computer Science, 2017-2020

    Polytechnique Montreal, INRIA, Centrale Lille, GERAD

  • Joint Bachelor & Master of Science in Process Engineering, 2011-2016

    University of Technology of Compiègne (UTC), France

  • Exchange program, applied mathematics, computer science & industrial engineering, 2015

    Polytechnique Montréal, Canada

  • Exchange semester, Process & Energy Engineering (Bachelor), 2013

    Technische Universität Braunschweig, Germany


Postdoctoral Researcher
Zuse Institute Berlin
January 2021 – Present Berlin, Germany
Research in optimization methods and computation.
Doctoral Researcher
Polytechnique Montréal, Inria Lille
September 2017 – December 2020 Montréal, Canada & Lille, France
Double PhD program in mathematical optimization for pricing of Demand Response programs in a smart grid context.
Research Engineer, Data Scientist
Equisense SAS
July 2016 – August 2017 Lille, France
Research and development for a startup building connected devices and associated products for horse-riders.
Master’s Thesis
Siemens AG, Digital Industries
February 2016 – July 2016 Karslruhe, Germany
Stochastic models for event monitoring in automated systems.
Junior Engineer Placement
ArcelorMittal Hamburg GmbH
August 2014 – January 2015 Hamburg, Germany
Quantification and analysis of material losses in a steel rolling mill.


Filter publications here.

(2023). Cutting Plane Selection with Analytic Centers and Multiregression. CPAIOR 2023.


(2023). How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem.

Cite arXiv

(2023). Improved local models and new Bell inequalities via Frank-Wolfe algorithms. arXiv.


(2022). Convex mixed-integer optimization with Frank-Wolfe algorithms.


(2022). Flexible Differentiable Optimization via Model Transformations. In minor revision.



The most reliable way to reach me is per email.