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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.
Gauvin, J. (2001). Formulae for the sensitivity analysis of linear programming problems. In Lassonde, M. (ed.), Approximation, optimization and mathematical economics (pp. 117-120). External link
Gauvin, J. (1995). Leçons de programmation mathématique. Unavailable
Gauvin, J. (1994, June). Degeneracy, normality, stability in mathematical programming [Paper]. Recent developments in optimization, Dijon, France. External link
Gauvin, J. (1994). Theory of Nonconvex programming. [Théorie de la programmation mathématique non convexe]. Unavailable
Gauvin, J., & Janin, R. (1989). Directional Lipschitzian Optimal-Solutions and Directional Derivative for the Optimal Value Function in Nonlinear Mathematical Programming. Annales de l'Institut Henri Poincare, 6S, 305-324. External link
Gauvin, J., & Janin, R. (1988). Directional Behaviour of Optimal Solutions in Nonlinear Mathematical Programming. Mathematics of Operations Research, 13(4), 629-649. External link
Gauvin, J., Parent, P., & Savard, G. (1986). Répartition optimale de la puissance dans une centrale hydraulique à réserve pompée. RAIRO - Operations Research, 20(1), 1-18. External link
Gauvin, J. (1980). Shadow prices in nonconvex mathematical programming. Mathematical Programming, 19(1), 300-312. External link
Gauvin, J. (1979). The Generalized Gradient of a Marginal Function in Mathematical Programming. Mathematics of Operations Research, 4(4), 458-463. External link
Gauvin, J. (1977). The shadow prices in nonconvex mathematical programming. (Technical Report n° EP-R-77-37). Restricted access
Gauvin, J., & Tolle, J. W. (1975). Differential stability in nonlinear programming. (Technical Report n° EP-R-75-63). Restricted access
Gauvin, J. (1974). Directional derivatives for an extremal-value function. (Technical Report n° EP-R-74-43). Restricted access
Janin, R., & Gauvin, J. (1999). Lipschitz-Type Stability in Nonsmooth Convex Programs. SIAM Journal on Control and Optimization, 38(1), 124-137. External link
Janin, R., & Gauvin, J. (1995, January). Lipschitz dependence of the optimal solution to elementary convex programs [Paper]. 2nd Catalan Days on Applied Mathematics, Odeillo. Unavailable
Janin, R., & Gauvin, J. (1982). Directional derivative of the marginal function in nonlinear programming. (Technical Report n° EP-R-82-22). Restricted access
Savard, G., & Gauvin, J. (1994). Steepest descent direction for the nonlinear bilevel programming problem. Operations Research Letters, 15(5), 265-273. External link
Savard, G., & Gauvin, J. (1993). The Steepest Descent Direction for the Nonlinear Bilevel Programming Problem. (Technical Report n° G-90-37). External link