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Two descent hybrid conjugate gradient methods for optimization

✍ Scribed by Li Zhang; Weijun Zhou

Publisher: Elsevier Science
Year: 2008
Tongue: English
Weight: 183 KB
Volume: 216
Category: Article
ISSN: 0377-0427
DOI: 10.1016/j.cam.2007.04.028

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✦ Synopsis

In this paper, we propose two new hybrid nonlinear conjugate gradient methods, which produce sufficient descent search direction at every iteration. This property depends neither on the line search used nor on the convexity of the objective function. Under suitable conditions, we prove that the proposed methods converge globally for general nonconvex functions. The numerical results show that both hybrid methods are efficient for the given test problems from the CUTE library.

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