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An adaptive Power-GArnoldi algorithm for computing PageRank
Journal of Computational and Applied Mathematics ( IF 2.4 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.cam.2020.113209
Chun Wen , Qian-Ying Hu , Guo-Jian Yin , Xian-Ming Gu , Zhao-Li Shen

In this paper, we present a new algorithm by combining the cheap but slow power method with the fast but expensive Arnoldi procedure periodically. The main feature of our method is that a weighted inner product is introduced when using an Arnoldi procedure to construct a basis for a Krylov subspace. Particularly, in each cycle, the weight matrix is changed adaptively according to the residual information obtained from the power iteration, with the aim of accelerating the computation of PageRank problems. The implementation and the convergence analysis of our new method are discussed in detail. Numerical experiments are reported to show the efficiency and convergence behavior of our proposed algorithm.



中文翻译:

计算PageRank的自适应Power-GArnoldi算法

在本文中,我们提出了一种新算法,它将廉价但慢功耗的方法与快速但昂贵的Arnoldi程序进行了定期组合。我们方法的主要特点是,在使用Arnoldi过程构建Krylov子空间的基础时,引入了加权内积。特别地,在每个周期中,根据从功率迭代获得的残差信息来自适应地改变权重矩阵,以加速对PageRank问题的计算。详细讨论了我们新方法的实现和收敛性分析。数值实验表明,该算法具有较高的效率和收敛性。

更新日期:2020-10-16
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