In this paper, based on the iteration framework (Tian et al., 2019) and relaxed two-step splitting (RTSS) iteration method (Xie and Ma, 2018), we present two relaxed iteration methods for solving the PageRank problem, which are the relaxed generalized inner–outer (RGIO) and relaxed generalized two-step splitting (RGTSS) iteration methods, respectively. Next, their overall convergence properties are analyzed in detail, and choices of the parameters in these algorithms are also discussed. Finally, several numerical examples are given to illustrate the effectiveness of the proposed algorithms.

中文翻译：

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几种轻松的迭代方法来计算PageRank

在本文中，基于迭代框架（Tian等人，2019）和宽松的两步拆分（RTSS）迭代方法（Xie and Ma，2018），我们提出了两种用于解决PageRank问题的宽松迭代方法：分别采用松弛广义内外（RGIO）和松弛广义两步拆分（RGTSS）迭代方法。接下来，详细分析它们的整体收敛特性，并讨论这些算法中的参数选择。最后，给出了几个数值例子来说明所提算法的有效性。