Global Convergence of Conjugate Gradient Methods without Line Search

Jie Sun () and Jiapu Zhang

Annals of Operations Research, 2001, vol. 103, issue 1, 173 pages

Abstract: Global convergence results are derived for well-known conjugate gradient methods in which the line search step is replaced by a step whose length is determined by a formula. The results include the following cases: (1) The Fletcher–Reeves method, the Hestenes–Stiefel method, and the Dai–Yuan method applied to a strongly convex LC 1 objective function; (2) The Polak–Ribière method and the Conjugate Descent method applied to a general, not necessarily convex, LC 1 objective function. Copyright Kluwer Academic Publishers 2001

Keywords: conjugate gradient methods; convergence of algorithms; line search (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1023/A:1012903105391

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