Geodesic
Absolute Minimizer in Convex Programming by Exponential Penalty
Journal of convex analysis, Tome 7 (2000) no. 1, pp. 197-202.

Voir la notice de l'article provenant de la source Heldermann Verlag

We consider a nonlinear convex program. Under some general hypotheses, we prove that approximate solutions obtained by exponential penalty converge toward a particular solution of the original convex program as the penalty parameter goes to zero. This particular solution is called the absolute minimizer and is characterized as the unique solution of a hierarchical scheme of minimax problems.
Classification : 90C25, 90C31
Mots-clés : Convexity, minimax problems, penalty methods, nonuniqueness, optimal trajectory, convergence 
@article{JCA_2000_7_1_JCA_2000_7_1_a9,
     author = {F. Alvarez},
     title = {Absolute {Minimizer} in {Convex} {Programming} by {Exponential} {Penalty}},
     journal = {Journal of convex analysis},
     pages = {197--202},
     publisher = {mathdoc},
     volume = {7},
     number = {1},
     year = {2000},
     url = {http://geodesic.mathdoc.fr/item/JCA_2000_7_1_JCA_2000_7_1_a9/}
}
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F. Alvarez. Absolute Minimizer in Convex Programming by Exponential Penalty. Journal of convex analysis, Tome 7 (2000) no. 1, pp. 197-202. http://geodesic.mathdoc.fr/item/JCA_2000_7_1_JCA_2000_7_1_a9/