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Sébastien Le Digabel
Dipl. Ing. (ISIMA, 1999), M.Sc. (Poly, 2002), Ph.D. (Poly, 2008)

Research interests and affiliations

Research interests My research focuses on the design of algorithms for the optimization of complex applications usually encountered in engineering. These problems are typically defined by functions viewed as blackboxes in the sense that no property of the function is available. In this context, usual optimization methods based on derivatives are prohibited, hence the choice of derivative-free optimization algorithms and in particular the use of models and surrogates less expensive to evaluate. My projects focus on three areas:
  1. Development of optimization methods
  2. Application to engineering problems
  3. Development of optimization software

Keywords: blackbox optimization, derivative-free methods, industrial applications, optimization software.

Functions at Polytechnique Montréal

  • Head of the Review Committee of Frauds
  • Member of the sub-committe for undergraduate studies
  • Member of the Board of governors
  • Member of the Probation Committee
Expertise type(s) (NSERC subjects)
  • 1601 Operations research and management science
  • 2715 Optimization
  • 2956 Optimization and optimal control theory


Journal article
Audet, C., Kokkolaras, M., Le Digabel, S. & Talgorn, B. (2018). Order-based error for managing ensembles of surrogates in mesh adaptive direct search. Journal of Global Optimization, 70(3), 645-675. Retrieved from
Journal article
Audet, C., Ianni, A., Le Digabel, S. & Tribes, C. (2014). Reducing the number of function evaluations in mesh adaptive direct search algorithms. SIAM Journal on Optimization, 24(2), 621-642. Retrieved from
Journal article
Conn, A.R. & Le Digabel, S. (2013). Use of quadratic models with mesh-adaptive direct search for constrained black box optimization. Optimization Methods and Software, 28(1), 139-158. Retrieved from
Journal article
Le Digabel, S. (2011). Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37(4). Retrieved from
Journal article
Perron, S., Hansen, P., Le Digabel, S. & Mladenovic, N. (2010). Exact and heuristic solutions of the global supply chain problem with transfer pricing. European Journal of Operational Research, 202(3), 864-879. Retrieved from
Journal article
Audet, C., Dennis Jr., J.E. & Le Digabel, S. (2010). Globalization strategies for mesh adaptive direct search. Computational Optimization and Applications, 46(2), 193-215. Retrieved from
Journal article
Abramson, M.A., Audet, C., Dennis Jr., J.E. & Le Digabel, S. (2009). Orthomads: A deterministic MADS instance with orthogonal direct ions. SIAM Journal on Optimization, 20(2), 948-966. Retrieved from
Journal article
Audet, C., Bechard, V. & Le Digabel, S. (2008). Nonsmooth Optimization Through Mesh Adaptive Direct Search and Variable Neighborhood Search. Journal of Global Optimization, 41(2), 299-318. Retrieved from
Journal article
Audet, C., Dennis, J.E. & Le Digabel, S. (2008). Parallel space decomposition of the mesh adaptive direct search algorithm. SIAM Journal on Optimization, 19(3), 1150-1170. Retrieved from
Journal article
Audet, C., Brimberg, J., Hansen, P., Le Digabel, S. & Mladenovic, N. (2004). Pooling Problem: Alternate Formulations and Solution Methods. Management Science, 50(6), 761-776. Retrieved from


Sébastien Le Digabel is an Associate Professor of Mathematics at the Ecole Polytechnique in Montreal, and a regular member of the GERAD research center. Before that, he obtained a Ph.D. in applied mathematics from the Ecole Polytechnique in 2008, and worked as a postdoctoral fellow at the IBM Watson Research Center and the University of Chicago in 2010 and 2011.

His research interests include the analysis and development of algorithms for blackbox optimization, and the design of related software. Blackbox optimization occurs when the functions to optimize are given by numerical simulations for which derivative information is not available. In this context, derivative-free optimization may be considered, and in particular the Mesh Adaptive Direct Search (MADS) method of Audet and Dennis, for which Le Digabel's thesis brought some extensions and upgrades. All of his work on MADS is included in the NOMAD software, a free package for blackbox optimization available at

S. Le Digabel's research is funded by the Canadian NSERC foundation, the Quebec FRQNT fund, InnovÉÉ, Hydro-Québec, and Rio Tinto.

Supervision at Polytechnique


  • Ph.D. (6)

    • Amaioua, Nadir. Amélioration des méthodes quadratiques et de la parallélisation pour l'optimisation des boîtes noires.
    • Bingane, Christian. Optimal Power Flow: Semidefinite Programming Approach.
    • De Souza Dutra, Michael David. Optimisation de la gestion de l'énergie des maisons intelligentes.
    • Lakhmiri, Dounia.
    • Dzahini, Kwassi Joseph.
    • Salomon, Ludovic. Multiobjective optimization.


  • Ph.D. Thesis (2)

  • Master's Thesis (4)

    • Lemyre Garneau, M. (2015). Modelling of a Solar Thermal Power Plant for Benchmarking Blackbox Optimization Solvers (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • Ihaddadene, A. (2014). Algorithme de recherche directe pour l'optimisation robuste de fonctions bruitées (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • Cartier, D. (2012). Optimisation sous contraintes d'un modèle hydrologique pour une représentation de la physique des processus (Master's Thesis, École Polytechnique de Montréal). Retrieved from
    • Duclos, E. (2012). ACRE: un générateur automatique d'aspect pour tester des logiciels écrits en C++ (Master's Thesis, École Polytechnique de Montréal). Retrieved from