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.
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Liu, G.H., Jing, L.L., Han, L.X. et al. A Class of Nonmonotone Conjugate Gradient Methods for Unconstrained Optimization. Journal of Optimization Theory and Applications 101, 127–140 (1999). https://doi.org/10.1023/A:1021723128049
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DOI: https://doi.org/10.1023/A:1021723128049