A class of modified FR conjugate gradient method and applications to non-negative matrix factorization

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• 作者：Xiangli Li^a ; ^c ; ^{lixiangli@guet.edu.cn} ; Wen Zhang^a ; Xiaoliang Dong^b
• 关键词：Modified Fletcher&ndash ; Reeves conjugate gradient method ; Non-negative matrix factorization ; Armijo-type line search
• 刊名：Computers & Mathematics with Applications
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：73
• 期：2
• 页码：270-276
• 全文大小：358 K
• 卷排序：73

文摘

Conjugate gradient method, as an efficient method, is used to solve unconstrained optimization problems. In this paper, we propose a class of modified Fletcher–Reeves conjugate gradient method, with Armijo-type line search, which generates the direction is descent for the objective function. Under mild conditions, we give that the proposed method is descent and globally convergent. Moreover, the method is applied to nonnegative matrix factorization, and the experimental results demonstrate the validity of the proposed method.