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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Audet, C., Bigeon, J., Couderc, R., & Kokkolaras, M. (2025). Risk-averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties. Computational Optimization and Applications, 61 pages. External link
Audet, C., Bigeon, J., Couderc, R., & Kokkolaras, M. (2023). Risk averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties. (Technical Report n° G-2023-46). External link
Audet, C., Bigeon, J., Couderc, R., & Kokkolaras, M. (2023). Sequential stochastic blackbox optimization with zeroth-order gradient estimators. AIMS Mathematics, 8(11), 25922-25956. Available
Audet, C., Bigeon, J., Couderc, R., & Kokkolaras, M. (2023). Sequential stochastic blackbox optimization with zeroth order gradient estimator. (Technical Report n° G-2023-16). External link
Audet, C., Bigeon, J., Couderc, R., & Kokkolaras, M. (2022). Risk-adverse optimization by Conditional Value-at-Risk and stochastic approximation. (Technical Report n° G-2022-04). External link
Audet, C., Bigeon, J., & Couderc, R. (2021). Combining Cross-Entropy and MADS Methods for Inequality Constrained Global Optimization. Operations Research Forum, 2(3), 26 pages. External link
Audet, C., Bigeon, J., Cartier, D., Le Digabel, S., & Salomon, L. (2021). Performance indicators in multiobjective optimization. European Journal of Operational Research, 292(2), 397-422. External link
Audet, C., Bigeon, J., & Couderc, R. (2020). Combining Cross Entropy and MADS methods for inequality constrained optimization. (Technical Report n° G-2020-59). External link
Angla, C., Bigeon, J., & Orban, D. (2020). Modeling and solving bundle adjustment problems. (Technical Report n° G-2020-42). External link
Audet, C., Bigeon, J., Cartier, D., Le Digabel, S., & Salomon, L. (2018). Performance indicators in multiobjective optimization. (Technical Report n° G-2018-90). External link
Bigeon, J., Le Digabel, S., & Salomon, L. (2024). Handling of constraints in multiobjective blackbox optimization. Computational Optimization and Applications, 45 pages. External link
Bigeon, J., Orban, D., & Raynaud, P. (2023). A framework around limited-memory partitioned quasi-Newton methods. (Technical Report n° G-2023-17). External link
Bigeon, J., Le Digabel, S., & Salomon, L. (2022). Handling of constraints in multiobjective blackbox optimization. (Technical Report n° G-2022-10). External link
Bigeon, J., Le Digabel, S., & Salomon, L. (2021). DMulti-MADS: mesh adaptive direct multisearch for bound-constrained blackbox multiobjective optimization. Computational Optimization and Applications, 79(2), 301-338. External link
Bigeon, J., Le Digabel, S., & Salomon, L. (2020). DMulti-MADS: Mesh adaptive direct multisearch for blackbox multiobjective optimization. (Technical Report n° G-2020-25). External link
Raynaud, P., Orban, D., & Bigeon, J. (2023). PLSR1 : a limited-memory partioned quasi-Newton optimizer for partially-separable loss functions. (Technical Report n° G-2023-41). External link
