A class of one parameter conjugate gradient methods

Shengwei Yao, Xiwen Lu, Liangshuo Ning and Feifei Li

Applied Mathematics and Computation, 2015, vol. 265, issue C, 708-722

Abstract: This paper proposes a class of one parameter conjugate gradient methods, which can be regarded as some kinds of convex combinations of some modified form of PRP and HS methods. The scalar βk has the form of ϕkϕk−1μk. The convergence of the given methods is analyzed by some unified tools which show the global convergence of the proposed methods. Numerical experiments with the CUTE collections show that the proposed methods are promising.

Keywords: Unconstrained optimization; Continuous optimization; Conjugate gradient method; Global convergence; Wolfe line search (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:265:y:2015:i:c:p:708-722

DOI: 10.1016/j.amc.2015.05.115

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