Anderson Acceleration of the Arnoldi-Inout Method for Computing PageRank
Autor: | Chun Wen, Zhao-Li Shen, Xian-Ming Gu, Xia Tang |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Physics and Astronomy (miscellaneous)
Computer science General Mathematics Computation lcsh:Mathematics Arnoldi-Inout method Extrapolation MathematicsofComputing_GENERAL Acceleration (differential geometry) 010103 numerical & computational mathematics lcsh:QA1-939 01 natural sciences law.invention 010101 applied mathematics TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES PageRank Chemistry (miscellaneous) law Convergence (routing) Computer Science (miscellaneous) Applied mathematics Anderson acceleration PageRank problems 0101 mathematics Linear combination |
Zdroj: | Symmetry, Vol 13, Iss 636, p 636 (2021) Symmetry Volume 13 Issue 4 |
ISSN: | 2073-8994 |
Popis: | Anderson(m0) extrapolation, an accelerator to a fixed-point iteration, stores m0+1 prior evaluations of the fixed-point iteration and computes a linear combination of those evaluations as a new iteration. The computational cost of the Anderson(m0) acceleration becomes expensive with the parameter m0 increasing, thus m0 is a common choice in most practice. In this paper, with the aim of improving the computations of PageRank problems, a new method was developed by applying Anderson(1) extrapolation at periodic intervals within the Arnoldi-Inout method. The new method is called the AIOA method. Convergence analysis of the AIOA method is discussed in detail. Numerical results on several PageRank problems are presented to illustrate the effectiveness of our proposed method. |
Databáze: | OpenAIRE |
Externí odkaz: |