Sébastien Le Digabel
Dipl. Ing. (ISIMA, 1999), M.Sc. (Poly, 2002), Ph.D. (Poly, 2008)
Full Professor
Department of Mathematical and Industrial Engineering
Department of Mathematical and Industrial Engineering
Areas of expertise
Operations research and management science Optimization Optimization and optimal control theory
Operations research and management science Optimization Optimization and optimal control theory
Primary sphere of excellence in research
Modeling and Artificial Intelligence
Modeling and Artificial Intelligence
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:
My projects focus on three areas:
- Development of optimization methods
- Application to engineering problems
- Development of optimization software
Keywords: blackbox optimization, derivative-free methods, industrial applications, optimization software.
Affiliation(s)
Expertise type(s) (NSERC subjects)
- 1601 Operations research and management science
- 2715 Optimization
- 2956 Optimization and optimal control theory
Publications
Featured
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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.
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.
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.
Le Digabel, S. (2011). Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37(4), 1-15.
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.
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.
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.
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.
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.
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.
See all publications (80)
Biography
Sébastien Le Digabel is a Professor of Mathematics at Polytechnique Montreal and a regular member of the GERAD research center. Before that, he obtained a PhD in applied mathematics from 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 www.gerad.ca/nomad.
S. Le Digabel's research is funded by the Canadian NSERC foundation, the Quebec FRQNT fund, IVADO, InnovÉÉ, Hydro-Québec, Rio Tinto, and Huawei-Canada.
Supervision at Polytechnique
COMPLETED
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Ph.D. Thesis (8)
- Salomon, L. (2022). Contributions to Multiobjective Blackbox Optimization [Ph.D. thesis, Polytechnique Montréal].
- Lakhmiri, D. (2021). Optimisation des hyperparamètres des réseaux de neurones profonds [Ph.D. thesis, Polytechnique Montréal].
- Dzahini, K. J. (2020). Méthodes de recherche directe pour l'optimisation stochastique de boîtes noires [Ph.D. thesis, Polytechnique Montréal].
- Bingane, C. (2019). Application de l'optimisation conique au problème d'écoulement de puissance optimal [Ph.D. thesis, Polytechnique Montréal].
- De Souza Dutra, M. D. (2019). Demand response for smart homes [Ph.D. thesis, Polytechnique Montréal].
- Amaioua, N. (2018). Modèles quadratiques et décomposition parallèle pour l'optimisation sans dérivées [Ph.D. thesis, École Polytechnique de Montréal].
- Rodrigues de Sousa, V. J. (2018). Global Optimization of the Maximum K-Cut Problem [Ph.D. thesis, École Polytechnique de Montréal].
- Peyrega, M. (2016). Optimisation sans dérivées sous contraintes [Ph.D. thesis, École Polytechnique de Montréal].
- Salomon, L. (2022). Contributions to Multiobjective Blackbox Optimization [Ph.D. thesis, Polytechnique Montréal].
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Master's Thesis (9)
- Lebeuf, X. (2023). Optimisation de boîtes noires multifidélités avec contraintes hiérarchisées [Master's thesis, Polytechnique Montréal].
- Gervais-Dubé, M. (2022). Linear Inequality Constraints in Blackbox Optimization [Master's thesis, Polytechnique Montréal].
- Hallé-Hannan, E. (2022). Cadre mathématique pour l'optimisation de boîtes noires avec variables catégorielles et méta [Master's thesis, Polytechnique Montréal].
- Lameynardie, G. (2020). Sondes locales intensives lors de l'exécution de l'algorithme MADS dans un environnement parallèle [Master's thesis, Polytechnique Montréal].
- Vanden Bulcke, R. (2020). Analyse de sensibilité pour la réduction de dimension en optimisation sans dérivée [Master's thesis, Polytechnique Montréal].
- Lemyre Garneau, M. (2015). Modelling of a Solar Thermal Power Plant for Benchmarking Blackbox Optimization Solvers [Master's thesis, École Polytechnique de Montréal].
- Ihaddadene, A. (2014). Algorithme de recherche directe pour l'optimisation robuste de fonctions bruitées [Master's thesis, École Polytechnique de Montréal].
- 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].
- Duclos, E. (2012). ACRE: un générateur automatique d'aspect pour tester des logiciels écrits en C++ [Master's thesis, École Polytechnique de Montréal].
- Lebeuf, X. (2023). Optimisation de boîtes noires multifidélités avec contraintes hiérarchisées [Master's thesis, Polytechnique Montréal].
