Global convergence properties of conjugate gradient methods for optimization

Abstract : We study the convergence of nonlinear conjugate gradient methods without restarts and with practical line searches. The analysis covers two classes of methods that are globally convergent on smooth, non convex functions. Some properties of the Fletcher-Reeves method play an important role in the first family, whereas the second family shares an important property with the Polak-Ribiere method. Numerical experiments are presented.
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Rapport
RR-1268, INRIA. 1990
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Dernière modification le : vendredi 16 septembre 2016 - 15:11:33
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Jean Charles Gilbert, J. Nocedal. Global convergence properties of conjugate gradient methods for optimization. RR-1268, INRIA. 1990. 〈inria-00075291〉

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