A globally convergent version of the Polak-Ribière conjugate gradient method
Grippo, L.
Lucidi, S.

Data: 1997
Resum: In this paper we propose a new line search algorithm that ensures global convergence of the Polak-Ribière conjugate gradient method for the unconstrained minimization of nonconvex differentiable functions. In particular, we show that with this line search every limit point produced by the Polak-Ribière iteration is a stationary point of the objective function. Moreover, we define adaptive rules for the choice of the parameters in a way that the first stationary point along a search direction can be eventually accepted when the algorithm is converging to a minimum point with positive definite Hessian matrix. Under strong convexity assumptions, the known global convergence results can be reobtained as a special case. From a computational point of view, we may expect that an algorithm incorporating the step-size acceptance rules proposed here will retain the same good features of the Polak-Ribière method, while avoiding pathological situations. .
Drets: Tots els drets reservats.
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Unconstrained optimization ; Conjugate gradient method ; Polak-Ribière method
Publicat a: Mathematical Programming, vol. 78 n. 3 (1997) p. 375-391, ISSN 0025-5610

17 p, 724.4 KB
 Accés restringit a la UAB

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