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A Class of Nonmonotone Conjugate Gradient Methods for Unconstrained Optimization (1999)

Liu, G. H. ; Jing, L. L. ; Han, L. X. ; [et al.]

Springer

Journal of optimization theory and applications 101 (1999), S. 127-140

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ISSN: 1573-2878

Keywords: Nonmonotone conjugate gradient method ; nonmonotone line search ; global convergence ; unconstrained optimization

Source: Springer Online Journal Archives 1860-2000

Topics: Mathematics

Notes: Abstract In this paper, we introduce a class of nonmonotone conjugate gradient methods, which include the well-known Polak–Ribière method and Hestenes–Stiefel method as special cases. This class of nonmonotone conjugate gradient methods is proved to be globally convergent when it is applied to solve unconstrained optimization problems with convex objective functions. Numerical experiments show that the nonmonotone Polak–Ribière method and Hestenes–Stiefel method in this nonmonotone conjugate gradient class are competitive vis-à-vis their monotone counterparts.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1023/A:1021723128049

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