A Class of Nonmonotone Conjugate Gradient Methods for Unconstrained Optimization

G. H. Liu, L. L. Jing, L. X. Han and D. Han
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G. H. Liu: Northwestern University
L. L. Jing: Beijing Forestry University
L. X. Han: University of Connecticut
D. Han: City University of New York

Journal of Optimization Theory and Applications, 1999, vol. 101, issue 1, No 7, 127-140

Abstract: 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.

Keywords: Nonmonotone conjugate gradient method; nonmonotone line search; global convergence; unconstrained optimization (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1023/A:1021723128049

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