A dynamical-system analysis of the optimum s-gradient algorithm
Résumé
We study the asymptotic behaviour of Forsythe's s-optimum gradient algorithm for the minimization of a quadratic function in R^d using a renormalization that converts the algorithm into iterations applied to a probability measure. Bounds on the performance of the algorithm (rate of convergence) are obtained through optimum design theory and the limiting behaviour of the algorithm for s=2 is investigated into details. Algorithms that switch periodically between s=1 and s=2 are shown to converge much faster than when s is fixed at 2.
Domaines
Statistiques [math.ST]
Origine : Fichiers produits par l'(les) auteur(s)
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