Several relaxed iteration methods for computing PageRank

被引：6
|
作者：
Tian, Zhaolu [1 ]
Zhang, Yan [2 ]
Wang, Junxin [1 ]
Gu, Chuanqing [3 ]
机构：
[1] Shanxi Univ Finance & Econ, Coll Appl Math, Taiyuan 030006, Peoples R China
[2] Shandong Univ Sci & Technol, Geomat Coll, Qingdao 266590, Peoples R China
[3] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2021年 / 388卷
关键词：
PageRank problem; Inner-outer iteration; Relaxed; Convergence; Matrix splitting; MATRIX SPLITTING ITERATION; INNER-OUTER ITERATION; CONVERGENCE; ALGORITHM;
D O I：
10.1016/j.cam.2020.113295
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, based on the iteration framework (Tian et al., 2019) and relaxed twostep 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. (C) 2020 Elsevier B.V. All rights reserved.
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页数：21
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