A sufficient descent conjugate gradient method and its global convergence. (3rd May 2016)

• Record Type: Journal Article
• Authors: Cheng, Yunlong; Mou, Qiong; Pan, Xianbing; Yao, Shengwei
• Is Part Of: Optimization methods and software. Volume 31:Number 3(2016)
• Page Start: 577
• Page End: 590
• Publication Date: 2016-05-03
• Subjects: conjugate gradient method -- unconstrained optimization -- global convergence -- Wolfe linesearch; 90C06 -- 90C26 -- 65Y20; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2015.1124431
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : In this paper, a DL-type conjugate gradient method is presented. The given method is a modification of the Dai–Liao conjugate gradient method. It can also be considered as a modified LS conjugate gradient method. For general objective functions, the proposed method possesses the sufficient descent condition under the Wolfe line search and is globally convergent. Numerical comparisons show that the proposed algorithm slightly outperforms the PRP+ and CG-descent gradient algorithms as well as the Barzilai–Borwein gradient algorithm.