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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.
Keutchayan, J., Munger, D., & Gendreau, M. (2020). On the scenario-tree optimal-value error for stochastic programming problems. Mathematics of Operations Research, 45(4), 1572-1595. External link
Keutchayan, J. (2018). Approximation d'espérances conditionnelles guidée par le problème en optimisation stochastique multi-étapes [Ph.D. thesis, École Polytechnique de Montréal]. Available
Keutchayan, J., Munger, D., & Gendreau, M. (2017). On the scenario-tree optimal-value error for stochastic programming problems. (Technical Report n° CIRRELT-2017-73). External link
Keutchayan, J., Munger, D., & Gendreau, M. (2017). On the scenario-tree optimal-value error for stochastic programming problems. (Technical Report n° CIRRELT-2017-05). External link
Keutchayan, J., Munger, D., Gendreau, M., & Bastin, F. (2017). A new scenario-tree generation approach for multistage stochastic programming problems based on a demerit criterion. (Technical Report n° CIRRELT-2017-74). External link
Keutchayan, J., Munger, D., & Gendreau, M. (2017). On the Scenario-Tree Optimal-Value Error for Stochastic Programming Problems. (Technical Report n° CIRRELT-2016-05). External link
Keutchayan, J., Gendreau, M., & Saucier, A. (2017). Quality evaluation of scenario-tree generation methods for solving stochastic programming problem. (Technical Report n° CIRRELT-2017-17). External link
Keutchayan, J., Gendreau, M., & Saucier, A. (2017). Quality evaluation of scenario-tree generation methods for solving stochastic programming problems. Computational Management Science, 14(3), 333-365. External link
Keutchayan, J., Gendreau, M., & Saucier, A. (2016). Quality Evaluation of Scenario-Tree Generation Methods for Solving High-Dimensional Stochastic Programs. (Technical Report n° CIRRELT-2016-46). Unavailable
