Current courses

  • [Spring 2024] Maths of OR (named: OR complementary)
    (UGA M1 Applied Maths); final exam

Books (in French)

Right Align

Objectif Agrégation
V. Beck, J. Malick, G. Peyré
Editions H&K, 2005
(towards success in "Agrégation de maths")

Data Science (cours et exercices)
9 co-authors from Grenoble
Editions Eyrolles, 2018
(overview of the foundations of data science)

Previous courses

  • [Springs from 2017 to 2023] Optimization (M1/2A ENSIMAG)
    Docs: Partial lectures notes -- Exercices
    Notebooks: tp1 -- tp2 -- tp3 -- tp4

  • [Winter 2018, 2019, 2020, 2021] Advanced Operations Research
    (M2 of CS, track Operations Research); Exercises

  • [Winters 2016, 2017, 2018, 2019] Convex and Distributed Optimization
    (Master 2 of Applied Maths)
    presentation of the course; more on course website

  • [Fall 2017] Basics on Matrix Analysis and Optimization (with F. Iutzeler)
    (M2 of CS of University Grenoble Alpes)
    Presentation of the course;
    Exercices; material for practical session on github

  • [Winters 2013, 2014, 2015] Mixed-integer linear programming
    (master 2 of CS of UGA, option operations research)
    List of research articles to study

  • [Spring 2012] I mention here this nice exam:
    "Epreuve du concours d'entrée" (ENS Cachan)

  • [Jan 2011] Optimization and convexity
    (master of computer sciences of ENS Lyon)
    Slides of introduction
    Tutorials: TP basis of numerical optimization --
    TD convex analysis -- TD duality -- Exam

  • [Winter 2010] Optimization methods in finance
    (master 2 Math-Fi of ENSIMAG)
    Expanded summary (in french): Summary (4 pages)

  • [2009] Basic numerical tools of optimization
    Winter School of fluid mechanics, organized LIMSI, CNRS
    lecture notes (in english with abstract in french)

  • [Every 1st semester from 2007 to 2015] Numerical Optimization
    (master of ENSIMAG and Grenoble University)
    Expanded summary: Summary (4 pages)
    lecture notes (in english with abstract in french)


  • cheminement de l'algorithme de
quasi-newton BFGS avec recherche linéaire sur la fonction de Rosenbrock
- l'algo converge en 26 itérations