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A 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.
Berthold, T., Lodi, A., & Salvagnin, D. (2019). Ten years of feasibility pump, and counting. EURO Journal on Computational Optimization, 7(1), 1-14. External link
Berthold, T., Lodi, A., & Salvagnin, D. (2017). Ten years of feasibility pump, and counting. (Technical Report n° DS4DM-2017-009). External link
Belotti, P., Bonami, P., Fischetti, M., Lodi, A., Monaci, M., Nogales-Gomez, A., & Salvagnin, D. (2016). On handling indicator constraints in mixed integer programming. Computational Optimization and Applications, 65(3), 545-566. External link
Belotti, P., Bonami, P., Fischetti, M., Lodi, A., Monaci, M., Nogales-Gómez, A., & Salvagnin, D. (2015). On handling indicator constraints in mixed-integer programming. (Technical Report). Unavailable
Fischetti, M., Lodi, A., Monaci, M., Salvagnin, D., & Tramontani, A. (2016). Improving branch-and-cut performance by random sampling. Mathematical Programming Computation, 8(1), 113-132. External link
Fischetti, M., Lodi, A., Monaci, M., Salvagnin, D., & Tramontani, A. (2013). Tree search stabilization by random sampling. (Technical Report). Unavailable
Fischetti, M., Lodi, A., & Salvagnin, D. (2010). Just MIP it! In Maniezzo, V., Stützle, T., & Voß, S. (eds.), Matheuristics (Vol. 10, pp. 39-70). External link
Koch, T., Achterberg, T., Andersen, E., Bastert, O., Berthold, T., Bixby, R. E., Danna, E., Gamrath, G., Gleixner, A. M., Heinz, S., Lodi, A., Mittelmann, H., Ralphs, T., Salvagnin, D., Steffy, D. E., & Wolter, K. (2011). MIPLIB 2010: Mixed integer programming library version 5. Mathematical Programming Computation, 3(2), 103-163. External link
Pommerening, F., Roger, G., Helmert, M., Cambazard, H., Rousseau, L.-M., & Salvagnin, D. (2021, January). Lagrangian decomposition for classical planning (extended abstract) [Paper]. 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan. External link
Pommerening, F., Röger, G., Helmert, M., Cambazard, H., Rousseau, L.-M., & Salvagnin, D. (2019, July). Lagrangian decomposition for optimal cost partitioning [Paper]. 29th International Conference on Automated Planning and Scheduling (ICAPS 2019), Berkeley, CA. Published in Proceedings of the International Conference on Automated Planning and Scheduling, 29. External link