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Publications

Refereed Journals

1. J. Bigeon, S. Le Digabel, and L. Salomon,
   Handling of constraints in multiobjective blackbox optimization.
   To appear in Computational Optimization and Applications.
   [bibtex]



2. S. Le Digabel and S.M. Wild,
   A taxonomy of constraints in black-box simulation-based optimization.
   Optimization and Engineering, 25(2), p. 1125-1143, 2024.
   [bibtex] [supplemental information]



3. C. Audet, E. Hallé-Hannan, and S. Le Digabel,
   A General Mathematical Framework for Constrained Mixed-variable Blackbox Optimization Problems with Meta and Categorical Variables.
   Operations Research Forum, 4(12), 2023.
   [bibtex]



4. K.J. Dzahini, M. Kokkolaras, and S. Le Digabel,
   Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates.
   Mathematical Programming, 198, p. 675-732, 2023.
   [bibtex]



5. C. Audet, S. Le Digabel, V. Rochon Montplaisir, and C. Tribes,
   Algorithm 1027: NOMAD version 4: Nonlinear optimization with the MADS algorithm.
   ACM Transactions on Mathematical Software, 48(3), p. 35:1-35:22, 2022.
   [bibtex]



6. C. Audet, S. Le Digabel, and R. Saltet,
   Quantifying uncertainty with ensembles of surrogates for blackbox optimization.
   Computational Optimization and Applications, 83, p. 29-66, 2022.
   [bibtex]



7. S. Alarie, C. Audet, P. Jacquot, and S. Le Digabel,
   Hierarchically constrained blackbox optimization.
   Operations Research Letters, 50(5), 2022.
   [bibtex]



8. V.J. Rodrigues de Sousa, M.F. Anjos, and S. Le Digabel,
   Computational study of a branching algorithm for the maximum k-cut problem.
   Discrete Optimization, 44(2), 2022.
   [bibtex]



9. D. Lakhmiri and S. Le Digabel,
   Use of static surrogates in hyperparameter optimization.
   Operations Research Forum, 3(11), 2022.
   [bibtex]



10. D. Lakhmiri, R. Alimo, and S. Le Digabel,
    Anomaly detection for data accountability of Mars telemetry data.
    Expert Systems With Applications, 189, 2022.
    [bibtex]



11. S. Alarie, C. Audet, P.-Y. Bouchet, and S. Le Digabel,
    Optimisation of stochastic blackboxes with adaptive precision.
    SIAM Journal on Optimization, 31(4), p. 3127-3156, 2021.
    [bibtex]



12. S. Alarie, C. Audet, A.E. Gheribi, M. Kokkolaras, and S. Le Digabel,
    Two decades of blackbox optimization applications.
    EURO Journal on Computational Optimization, 9, 2021.
    [bibtex]



13. D. Lakhmiri, S. Le Digabel, and C. Tribes,
    HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search.
    ACM Transactions on Mathematical Software, 47(3), 2021.
    [bibtex] [the HyperNOMAD package]



14. J. Bigeon, S. Le Digabel, and L. Salomon,
    DMulti-MADS: Mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization.
    Computational Optimization and Applications, 79(2), 301-338, 2021.
    [bibtex] [Code and results]



15. C. Audet, K.J. Dzahini, M. Kokkolaras, and S. Le Digabel,
    Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates.
    Computational Optimization and Applications, 79(1), 1-34, 2021.
    [bibtex]



16. C. Audet, J. Bigeon, D. Cartier, S. Le Digabel, and L. Salomon,
    Performance indicators in multiobjective optimization.
    European Journal of Operational Research, Invited Review, 292(2), 397-422, 2021.
    [bibtex]



17. C. Bingane, M.F. Anjos, and S. Le Digabel,
    Tight-and-Cheap Conic Relaxation for the Optimal Reactive Power Dispatch Problem.
    IEEE Transactions on Power Systems, 34(6), 4684-4693, 2019.
    [bibtex]



18. M.D. de Souza Dutra, M.F. Anjos, and S. Le Digabel,
    A general framework for customized transition to smart homes.
    Energy, 189(116138), 2019.
    [bibtex]



19. M.D. de Souza Dutra, M.F. Anjos, and S. Le Digabel,
    A realistic energy optimization model for smart-home appliances.
    International Journal of Energy Research, 43(8), p. 3237-3262, 2019.
    [bibtex][read online]



20. V.J. Rodrigues de Sousa, M.F. Anjos, and S. Le Digabel,
    Improving the linear relaxation of maximum k-cut with semidefinite-based constraints.
    EURO Journal on Computational Optimization, 7(2), p. 123-151, 2019.
    [bibtex] [read online]



21. C. Audet, S. Le Digabel, and C. Tribes,
    The Mesh Adaptive Direct Search Algorithm for Granular and Discrete Variables.
    SIAM Journal on Optimization, 29(2), p. 1164-1189, 2019.
    [bibtex]



22. C. Bingane, M.F. Anjos, and S. Le Digabel,
    Tight-and-Cheap Conic Relaxation for the AC Optimal Power Flow Problem.
    IEEE Transactions on Power Systems , 33(6), p. 7181-7188, 2018.
    [bibtex]
    CORS 2019 Best Student Paper (C. Bingane).
    Description of the project (GERAD).



23. C. Audet, A.R. Conn, S. Le Digabel, and M. Peyrega,
    A progressive barrier derivative-free trust-region algorithm for constrained optimization.
    Computational Optimization and Applications, 71(2), p. 307-329, 2018.
    [bibtex] [read online]



24. A.E. Gheribi, A.D. Pelton, E. Bélisle, S. Le Digabel, and J.-P. Harvey,
    On the prediction of low-cost high entropy alloys using new thermodynamic multi-objective criteria.
    Acta Materialia, 161, p. 73-82, 2018.
    [bibtex]



25. C. Audet, A. Ihaddadene, S. Le Digabel, and C. Tribes,
    Robust optimization of noisy blackbox problems using the Mesh Adaptive Direct Search algorithm.
    Optimization Letters, 12(4), p. 675-689, 2018.
    [bibtex] [read online]



26. V.J. Rodrigues de Sousa, M.F. Anjos, and S. Le Digabel,
    Computational Study of Valid Inequalities for the Maximum k-Cut Problem.
    Annals of Operations Research, 265(1), p. 5-27, 2018.
    [bibtex]



27. N. Amaioua, C. Audet, A.R. Conn, and S. Le Digabel,
    Efficient solution of quadratically constrained quadratic subproblems within the MADS algorithm.
    European Journal of Operational Research, 268(1), p. 13-24, 2018.
    [bibtex]



28. C. Audet, M. Kokkolaras, S. Le Digabel, and B. Talgorn,
    Order-based error for managing ensembles of surrogates in mesh adaptive direct search.
    Journal of Global Optimization, 70(3), p. 645-675, 2018.
    [bibtex] [read online]



29. B. Talgorn, C. Audet, M. Kokkolaras, and S. Le Digabel,
    Locally weighted regression models for surrogate-assisted design optimization.
    Optimization and Engineering, 19(1), p. 213-238, 2018.
    [bibtex] [read online]



30. F. Bélisle, N. Saunier, G.-A. Bilodeau, and S. Le Digabel,
    Optimized Video Tracking for Automated Vehicle Turning Movement Counts.
    Transportation Research Record, 2645, p. 104-112, 2017.
    [bibtex]



31. C. Audet, S. Le Digabel, and C. Tribes,
    Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization.
    Optimization and Engineering, 17(2), p. 333-358, 2016.
    [bibtex] [read online]



32. R.B. Gramacy, G.A. Gray, S. Le Digabel, H.K.H. Lee, P. Ranjan, G.N. Wells, and S.M. Wild,
    Modeling an Augmented Lagrangian for Blackbox Constrained Optimization (with discussion).
    Technometrics, 58(1), p. 1-11, 2016.
    [bibtex]



33. R.B. Gramacy, G.A. Gray, S. Le Digabel, H.K.H. Lee, P. Ranjan, G.N. Wells, and S.M. Wild,
    Rejoinder for Modeling an Augmented Lagrangian for Blackbox Constrained Optimization.
    Technometrics, 58(1), p. 26-29, 2016.
    [bibtex]



34. A.E. Gheribi, J.-P. Harvey, E. Bélisle, C. Robelin, P. Chartrand, A.D. Pelton, C.W. Bale, and S. Le Digabel,
    Use of a biobjective direct search algorithm in the process design of material science applications.
    Optimization and Engineering, 17(1), p. 27-45, 2016.
    [bibtex] [read online]



35. M. Pourbagian, B. Talgorn, W.G. Habashi, M. Kokkolaras, and S. Le Digabel,
    Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems.
    Optimization and Engineering, 16(4), p. 663-693, 2015.
    [bibtex] [read online]



36. R.B. Gramacy and S. Le Digabel,
    The mesh adaptive direct search algorithm with treed Gaussian process surrogates.
    Pacific Journal of Optimization, 11(3), p. 419-447, 2015.
    STYRENE test problem.
    MDO test problem.
    [bibtex]



37. C. Audet, S. Le Digabel, and M. Peyrega,
    Linear equalities in blackbox optimization.
    Computational Optimization and Applications, 61(1), p. 1-23, 2015.
    Numerical results.
    [bibtex] [read online]



38. E. Bélisle, Z. Huang, S. Le Digabel, and A.E. Gheribi,
    Evaluation of machine learning interpolation techniques for prediction of physical properties.
    Computational Materials Science, 98, p. 170-177, 2015.
    [bibtex]



39. B. Talgorn, S. Le Digabel, and M. Kokkolaras,
    Statistical Surrogate Formulations for Simulation-Based Design Optimization.
    Journal of Mechanical Design, 137(2), p. 021405-1-021405-18, 2015.
    [bibtex]



40. M. Minville, D. Cartier, C. Guay, L.-A. Leclaire, C. Audet, S. Le Digabel, and J. Merleau,
    Improving process representation in conceptual hydrological model calibration using climate simulations.
    Water Resources Research, 50(6), p. 5044-5073, 2014.
    [bibtex]



41. E.M. Gertz, T. Hiekkalinna, S. Le Digabel, C. Audet, J.D. Terwilliger, and A.A. Schaffer,
    PSEUDOMARKER 2.0: efficient computation of likelihoods using NOMAD.
    BMC Bioinformatics, 15(47), p. 1-8, 2014.
    [bibtex] [read online]



42. C. Audet, A. Ianni, S. Le Digabel, and C. Tribes,
    Reducing the Number of Function Evaluations in Mesh Adaptive Direct Search Algorithms.
    SIAM Journal on Optimization, 24(2), p. 621–642, 2014.
    Numerical results.
    [bibtex]



43. A.E. Gheribi, S. Le Digabel, C. Audet, and P. Chartrand,
    Identifying optimal conditions for Magnesium based alloy design using the Mesh Adaptive Direct Search algorithm.
    Thermochimica Acta, 559, p. 107-110, 2013.
    [bibtex]



44. S. Alarie, C. Audet, V. Garnier, S. Le Digabel, and L.A. Leclaire,
    Snow water equivalent estimation using blackbox optimization.
    Pacific Journal of Optimization, 9(1), p. 1-21, 2013.
    [bibtex]
    The GMON stations (Radio-Canada, in French).



45. A.R. Conn and S. Le Digabel,
    Use of quadratic models with mesh adaptive direct search for constrained black box optimization.
    Optimization Methods and Software, 28(1), p. 139-158, 2013.
    [bibtex]



46. C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
    Trade-off studies in blackbox optimization.
    Optimization Methods and Software, 27(4-5), p. 613-624, 2012.
    [bibtex]



47. A.E. Gheribi, C. Audet, S. Le Digabel, E. Bélisle, C.W. Bale, and A.D. Pelton,
    Calculating optimal conditions for alloy and process design using thermodynamic and property databases, the FactSage software and the Mesh Adaptive Direct Search algorithm.
    CALPHAD: Computer Coupling of Phase Diagrams and Thermochemistry, 36, p. 135-143, 2012.
    [bibtex]



48. C. Audet and S. Le Digabel,
    The mesh adaptive direct search algorithm for periodic variables.
    Pacific Journal of Optimization, 8(1), p. 103-119, 2012.
    [bibtex]



49. A.E. Gheribi, C. Robelin, S. Le Digabel, C. Audet, and A.D. Pelton,
    Calculating all local minima on liquidus surfaces using the FactSage software and databases and the Mesh Adaptive Direct Search algorithm.
    Journal of Chemical Thermodynamics, 43(9), p. 1323-1330, 2011.
    [bibtex]



50. S. Le Digabel,
    Algorithm 909: NOMAD: Nonlinear Optimization with the MADS algorithm.
    ACM Transactions on Mathematical Software, 37(4), p. 44:1-44:15, 2011.
    [bibtex]



51. C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
    Globalization strategies for Mesh Adaptive Direct Search.
    Computational Optimization and Applications, 46(2), p. 193-215, 2010.
    [bibtex] [read online]



52. S. Perron, P. Hansen, S. Le Digabel, and N. Mladenović,
    Exact and Heuristic Solutions of the Global Supply Chain Problem with Transfer Pricing.
    European Journal of Operational Research, 202(3), p. 864-879, 2010.
    [bibtex]



53. M.A. Abramson, C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
    OrthoMADS: A Deterministic MADS Instance with Orthogonal Directions.
    SIAM Journal on Optimization, 20(2), p. 948–966, 2009.
    [bibtex]



54. C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
    Parallel Space Decomposition of the Mesh Adaptive Direct Search algorithm.
    SIAM Journal on Optimization, 19(3), p. 1150-1170, 2008.
    [bibtex]



55. C. Audet, V. Béchard, and S. Le Digabel,
    Nonsmooth Optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search.
    Journal of Global Optimization, 41(2), p. 299-318, 2008.
    STYRENE test problem.
    [bibtex] [read online]



56. C. Audet, J. Brimberg, P. Hansen, S. Le Digabel, and N. Mladenović,
    Pooling problem: Alternate formulations and solution methods.
    Management Science, 50(6), p. 761-776, 2004.
    [bibtex]

Refereed Book Chapters

57. S. Al-Maskari, E. Bélisle, X. Li, S. Le Digabel, A. Nawahda, and J. Zhong,
    Classification with Quantification for Air Quality Monitoring.
    In Advances in Knowledge Discovery and Data Mining, Volume 9651 of Lecture Notes in Computer Science , p. 578-590, Springer, 2016.
    [bibtex]



58. B. Talgorn, S. Le Digabel, and M. Kokkolaras,
    Blackbox Optimization in Engineering Design: Adaptive Statistical Surrogates and Direct Search Algorithms.
    In Engineering and Applied Sciences Optimization, Volume 38 of Computational Methods in Applied Sciences , p. 359-383, Springer, 2015.
    [bibtex]



59. C. Audet, K. Diest, S. Le Digabel, L.A. Sweatlock, and D.E. Marthaler,
    Metamaterial Design by Mesh Adaptive Direct Search.
    In Numerical Methods for Metamaterial Design, Volume 127 of Topics in Applied Physics , p. 71-96, Springer, 2013.
    [bibtex]



60. C. Audet, P. Hansen, and S. Le Digabel,
    Exact solution of three nonconvex quadratic programming problems.
    In Frontiers in Global Optimization, Volume 74 of Nonconvex Optimization and Applications , p. 25-43, Kluwer, 2004.
    [bibtex]

Refereed Conference Papers

61. C. Audet, S. Le Digabel, L. Salomon, and C. Tribes,
    Constrained Blackbox Optimization with the NOMAD Solver on the COCO Constrained Test Suite.
    Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), 2022.
    [bibtex]



62. C. Bingane, M.F. Anjos, and S. Le Digabel,
    CONICOPF: Conic Relaxations for AC Optimal Power Flow Computations.
    IEEE Power Energy Society General Meeting (PESGM), 2021.
    [bibtex] [code]



63. C. Bingane, M.F. Anjos, and S. Le Digabel,
    Tight-and-Cheap Conic Relaxation for the Optimal Reactive Power Dispatch Problem.
    IEEE Power Energy Society General Meeting (PESGM), 2021.
    [bibtex]



64. M.D. de Souza Dutra, M.F. Anjos, and S. Le Digabel,
    A Framework for Peak Shaving Through the Coordination of Smart Homes.
    Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference, Paper No. ISGT2019-0005, 2019.
    [bibtex]



65. V. Picheny, R.B. Gramacy, S.M. Wild, and S. Le Digabel,
    Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian.
    Advances in Neural Information Processing Systems 29 (NIPS 2016), Paper No. 6439, p. 1435-1443, 2016.
    [bibtex]



66. L. Gauthier, N. Saunier, and S. Le Digabel and G. Cao,
    Calibration of Driving Behavior Models using Derivative-Free Optimization and Video Data for Montreal Highways.
    Transportation Research Board 95th Annual Meeting, Paper No. 16-2988, 2016.
    [bibtex]



67. B. Talgorn, S. Le Digabel, and M. Kokkolaras,
    Problem Formulations for Simulation-Based Design Optimization Using Statistical Surrogates and Direct Search.
    Proceedings of The ASME 2014 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference.
    Paper No. DETC2014-34778, 2014.
    [bibtex]



68. E. Duclos, S. Le Digabel, Y.-G. Guéhéneuc, and B. Adams,
    ACRE: An Automated Aspect Creator for Testing C++ Applications.
    Proceedings of the 17th European Conference on Software Maintenance and Reengineering (CSMR), IEEE Computer Society, p. 121-130, 2013.
    [bibtex]



69. N. Bhattacharya, O. El-Mahi, E. Duclos, G. Beltrame, S. Le Digabel, Y.-G. Guéhéneuc, and G. Antoniol,
    Optimizing Threads Schedule Alignments to Expose the Interference Bug Pattern.
    Lecture Notes in Computer Science vol. 7515, 4th Symposium on Search Based Software Engineering (SSBSE),
    G. Fraser and J. Teixeira de Souza eds., Springer-Verlag, p. 90-104, 2012.
    [bibtex]

Submitted

70. N. Andrés-Thió, C. Audet, M. Diago, A.E. Gheribi, S. Le Digabel, X. Lebeuf, M. Lemyre Garneau, and C. Tribes,
    solar: A solar thermal power plant simulator for blackbox optimization benchmarking.
    Technical report, Les Cahiers du GERAD G-2024-37.
    [bibtex] [the solar package]



71. S. Alarie, C. Audet, M. Diago, S. Le Digabel, and X. Lebeuf,
    Hierarchically constrained multi-fidelity blackbox optimization.
    Technical report, Les Cahiers du GERAD G-2023-66.
    [bibtex]

Unpublished Technical Reports

72. M. Zolnouri, D. Lakhmiri, C. Tribes, E. Sari, and S. Le Digabel
    Efficient Training Under Limited Resources.
    Technical report, ArXiv 2301.09264, 2023.
    Second prize challenge winner at the Hardware Aware Efficient Training workshop
    of the International Conference on Learning Representations (ICLR 2021)
    [bibtex]



73. D. Lakhmiri, M. Zolnouri, V. Partovi Nia, C. Tribes, and S. Le Digabel
    Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm.
    Technical report, ArXiv 2301.06641, 2023.
    [bibtex]



74. S. Alarie, N. Amaioua, C. Audet, S. Le Digabel, and L.-A. Leclaire,
    Selection of variables in parallel space decomposition for the mesh adaptive direct search algorithm.
    Technical report, Les Cahiers du GERAD G-2018-38.
    [bibtex]



75. C. Audet, S. Le Digabel, and M. Peyrega,
    A derivative-free trust-region augmented Lagrangian algorithm.
    Technical report, Les Cahiers du GERAD G-2016-53.
    [bibtex]

Editorials

D. Aloise, G. Caporossi, and S. Le Digabel,
Preface to the special issue of JOGO on the occasion of the 40th anniversary of the Group for Research in Decision Analysis (GERAD).
Journal of Global Optimization, 2021.
[bibtex]

M.F. Anjos, F. Bastin, S. Le Digabel, and A. Lodi,
Preface to the special issue of INFOR on “continuous optimization and applications in machine learning and data analytics”.
INFOR: Information Systems and Operational Research, 58(2), p. 167-167, 2020.
[bibtex]

Ph.D. Thesis

Extensions of the MADS algorithm for nonsmooth optimization.
Polytechnique Montréal, 2008.
[bibtex]

Communications

1. Blackbox optimization with the MADS algorithm and the NOMAD software.
   33rd European Conference on Operational Research (EURO 24), Copenhagen, 2024.



2. Parallel versions of the mesh adaptive direct search algorithm.
   2nd Derivative-Free Optimization Symposium (DFOS'24), Padova, 2024.



3. Blackbox optimization.
   ETICS 2023 research school, Lège-Cap-Ferret, 2023.



4. Blackbox optimization: Algorithms and applications.
   ENAC seminar, Toulouse, 2023.



5. Seminar and Training.
   CIROQUO research visit, INRIA Saclay and IFPEN, 2023.



6. solar: A solar thermal power plant simulator for blackbox optimization benchmarking.
   SIAM Conference on Optimization (OP23), Seattle, 2023.



7. Blackbox optimization: Algorithms and applications.
   The Optimization Days, Tutorial, Montréal, 2023.



8. solar: A solar thermal power plant simulator for blackbox optimization benchmarking.
   SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, 2023.
   [extended version]



9. solar: A solar thermal power plant simulator for blackbox optimization benchmarking.
   Derivative-Free Optimization: Linking Algorithms and Applications (DFOS), Kelowna, 2022.
   [video]



10. Blackbox optimization with the MADS algorithm and the NOMAD software.
    ORPA-AFROS Interdisplinary Summer School (OASIS’2022), online, 2022.



11. solar: A solar thermal power plant simulator for blackbox optimization benchmarking.
    The Optimization Days, Montréal, 2022.



12. Blackbox optimization with the MADS algorithm and the NOMAD software.
    CRM-McGill Applied Mathematics Seminar, online, 2022.



13. Blackbox optimization with the MADS algorithm and the NOMAD software.
    ROADEF, Tutorial, online, 2021.
    [video]



14. Blackbox optimization with MADS and NOMAD: Application to Hyperparameters Optimization (HPO).
    Hydro-Québec, IREQ, online, 2020.



15. Optimisation de boîtes noires.
    IVADO Reverse Pitch, online, 2020.



16. Blackbox optimization with the MADS algorithm and the NOMAD software.
    MILA (MTL MLOpt), online, 2020.



17. Blackbox optimization with the MADS algorithm and the NOMAD software.
    IVADO Seminar, online, 2020.
    [capsule]



18. Blackbox optimization with NOMAD.
    Edge Intelligence Workshop, Montréal, 2020.



19. Derivative-Free Optimization and BlackBox Optimization.
    Institute of Innovation and Design in Aerospace of Polytechnique (IICAP), Polytechnique Montréal, 2019.



20. HYPERNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search.
    International Conference on Continuous Optimization (ICCOPT), Berlin, 2019.



21. HYPERNOMAD: Hyper-parameter optimization of deep neural networks using mesh adaptive direct search.
    CORS 61st Annual Conference, Saskatoon, 2019.



22. HYPERNOMAD: Hyper-parameter optimization of deep neural networks using mesh adaptive direct search.
    The Optimization Days, Montréal, 2019.



23. Démystifier l’optimisation mathématique.
    Conférence Science des données, Montréal, 2019.
    Fichiers.



24. Blackbox Optimization with NOMAD: Applications and Software.
    Huawei, Montréal, 2019.



25. NOMAD, a blackbox optimization software.
    16th EUROPT Workshop on Advances in Continuous Optimization, Almería, 2018.



26. The mesh adaptive direct search algorithm for granular and discrete variables.
    23rd International Symposium on Mathematical Programming (ISMP 2018), Bordeaux, 2018.



27. Blackbox optimization with the MADS algorithm and the NOMAD software.
    Hydro-Québec, IREQ, Varennes, 2018.



28. The mesh adaptive direct search algorithm for granular and discrete variables.
    The Optimization Days, Montréal, 2018.



29. Blackbox optimization with the NOMAD software.
    UQAC, Chicoutimi, 2018.



30. The mesh adaptive direct search algorithm for granular and discrete variables.
    Rio Tinto, Saguenay, 2018.



31. Order-Based Error for Managing Ensembles of Surrogates in Derivative-Free Optimization.
    21st conference of the International Federation of Operational Research Societies (IFORS), Quebec City, 2017.



32. Order-Based Error for Managing Ensembles of Surrogates in Derivative-Free Optimization.
    SIAM Conference on Optimization (OP17), Vancouver, 2017.



33. Order-Based Error for Managing Ensembles of Surrogates in Derivative-Free Optimization.
    The Optimization Days, Montréal, 2017.



34. The Mesh Adaptive Direct Search Algorithm for Discrete Blackbox Optimization.
    International Conference on Continuous Optimization (ICCOPT), Tokyo, 2016.



35. BlackBox Optimization: Algorithm and Applications.
    Workshop on Nonlinear Optimization Algorithms and Industrial Applications, The Fields Institute, Toronto, 2016.



36. The Mesh Adaptive Direct Search Algorithm for Discrete Blackbox Optimization.
    CORS 58th Annual Conference, Banff, 2016.



37. BlackBox Optimization: Algorithm and Applications.
    7ème colloque québécois sur le développement numérique de produits, Polytechnique Montréal, 2016.



38. BlackBox Optimization: Algorithm and Applications.
    UBC Okanagan, Kelowna, 2016.



39. Blackbox optimization with the NOMAD software.
    CanmetENERGY, Varennes, 2015.



40. Blackbox optimization with the NOMAD software.
    Bombardier, Montréal, 2014.



41. Formulations for Surrogate-Based Constrained Blackbox Optimization.
    12th EUROPT Workshop on Advances in Continuous Optimization, Perpignan, 2014.



42. Formulations for Surrogate-Based Constrained Blackbox Optimization.
    SIAM Conference on Optimization (OP14), San Diego, 2014.



43. Formulations for Surrogate-Based Constrained Blackbox Optimization.
    The Optimization Days, Montréal, 2014.



44. Black-Box Optimization: Algorithm and Applications.
    LANL, Los Alamos, 2014.



45. Engineering applications treated with the MADS algorithm.
    International Conference on Continuous Optimization (ICCOPT), Lisbon, 2013.



46. The mesh adaptive direct search algorithm with reduced number of directions.
    21th International Symposium of Mathematical Programming (ISMP), Berlin, 2012.



47. The Mesh Adaptive Direct Search Algorithm with Reduced Number of Directions.
    The Optimization Days, Montréal, 2012.



48. Use of models with the MADS algorithm for blackbox optimization.
    The Optimization Days, Montréal, 2011.



49. Snow water equivalent estimation using blackbox optimization.
    SIAM Conference on Computational Science and Engineering (CSE11), Reno, 2011.



50. Blackbox Optimization.
    ICiS workshop: Optimization in Energy Systems, Snowbird, 2010.



51. Parallel Versions of the MADS Algorithm for Black-Box Optimization.
    The Optimization Days, Montréal, 2010.
    [bibtex]



52. Black-Box Optimization with the NOMAD Software.
    The Boeing Company, Seattle, 2010.



53. Parallel Algorithms for Black-Box Optimization.
    14th SIAM Conference on Parallel Processing for Scientific Computing (PP10), Seattle, 2010.



54. Black-box Optimization with the NOMAD Software.
    20th International Symposium of Mathematical Programming (ISMP), Chicago, 2009.



55. Black-Box Optimization with the NOMAD Software.
    The Optimization Days, Montréal, 2009.



56. Parallel Variable Distribution for Mesh Adaptive Direct Search.
    ICCOPT II – MOPTA 07, McMaster University, Hamilton, 2007.



57. Parallel Variable Distribution for Mesh Adaptive Direct Search.
    The Optimization Days, Montréal, 2007.



58. Non-Smooth Optimization by Combining MADS Algorithm and VNS Metaheuristic.
    The Optimization Days, Montréal, 2006.



59. The Pooling Problem: Alternate Formulations and Solution Methods.
    The Optimization Days, Montréal, 2003.



60. Minimizing the Congestion in Link Assignment and Routing Problems with Degree Constraints.
    The Optimization Days, Montréal, 2002.

Other

1. F. Benoît and S. Le Digabel,
   GERAD and the Optimization Days.
   The GERAD newsletters, 11(1), p. 7, 2014.
   [bibtex]



2. S. Le Digabel and C. Tribes,
   The Nomad software for blackbox optimization.
   The GERAD newsletters (Software), 9(2), p. 6, 2013.
   [bibtex]



3. M.A. Abramson, C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
   Ortho-MADS: A Deterministic MADS Instance with Orthogonal Directions.
   The GERAD newsletters (Articles in first-class journals), 6(1&2), p. 3, 2009.
   [bibtex]



4. C. Audet, S. Le Digabel, and C. Tribes,
   NOMAD user guide.
   Technical report, Les Cahiers du GERAD G-2009-37.
   [bibtex]



5. C. Audet, S. Alarie, S. Le Digabel, Q. Lequy, M. Sylla, and O. Marcotte,
   Localisation de stations de mesure automatisée du couvert nival.
   Proceedings of the Second Montreal Industrial Problem Solving Workshop, CRM-3277, p. 9-18, 2008.
   [bibtex]



6. C. Audet, J.E. Dennis, Jr., and S. Le Digabel,
   Parallel Space Decomposition of the Mesh Adaptive Direct Search algorithm.
   The GERAD newsletters (Eleven articles published in leading journals), 5(2), p. 3, 2008.
   [bibtex]

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