Two New Dai–Liao-Type Conjugate Gradient Methods for Unconstrained Optimization Problems

Yutao Zheng () and Bing Zheng ()
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Yutao Zheng: Lanzhou University
Bing Zheng: Lanzhou University

Journal of Optimization Theory and Applications, 2017, vol. 175, issue 2, No 10, 502-509

Abstract: Abstract In this paper, we present two new Dai–Liao-type conjugate gradient methods for unconstrained optimization problems. Their convergence under the strong Wolfe line search conditions is analysed for uniformly convex objective functions and general objective functions, respectively. Numerical experiments show that our methods can outperform some existing Dai–Liao-type methods by using Dolan and Moré’s performance profile.

Keywords: Unconstrained optimization; Dai–Liao-type methods; Strong convergence; Global convergence; Strong Wolfe line search; 65K05; 90C26; 90C30 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10957-017-1140-1

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