In this paper, a small and necessary revision on an assumption condition of Aminifard and Babaie-Kafaki (Calcolo, 2019. https://doi.org/10.1007/s10092-019-0312-9) is made. By a little modification, a new conjugate gradient method is proposed, in which the search directions satisfy the sufficient descent condition with the strong Wolfe line search. The main difference between two algorithms is that the proposed method is globally convergent without boundedness assumption on the steplength. Comparative numerical results demonstrating efficiency of the proposed method are reported.

中文翻译：

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具有充分下降特性的改进的非线性Polak–Ribière–Polyak共轭梯度法

本文对Aminifard和Babaie-Kafaki的假设条件进行了小规模且必要的修订（Calcolo，2019.https：//doi.org/10.1007/s10092-019-0312-9）。通过稍加修改，提出了一种新的共轭梯度法，该方法在强沃尔夫线搜索的情况下满足搜索方向满足足够的下降条件。两种算法之间的主要区别在于，所提出的方法是全局收敛的，没有步长的有界假设。报告了比较数值结果，证明了该方法的有效性。