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Self-adaptive Non-stationary Parallel Multisplitting Two-Stage Iterative Methods for Linear Systems

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Data and Knowledge Engineering (ICDKE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7696))

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Abstract

In this paper,the new non-stationary parallel multisplitting two-stage iterative methods with self-adaptive weighting matrices are presented for the linear system of equations when the coefficient matrix is nonsingular. Self-adaptive weighting matrices are given, especially, the nonnegativity is eliminated. The convergence theories are established for the self-adaptive non-stationary parallel multisplitting two-stage iterative methods. Finally, the numerical comparisons of several self-adaptive non-stationary parallel multisplitting two-stage iterative methods are shown.

This work is supported by NSF of China (11071184) and NSF of Shanxi Province (201001006, 2012011015-6).

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Author information

Authors and Affiliations

  1. Department of Compute Science, Xinzhou teacher University, Xinzhou, 034000, Shanxi Province, P.R. China

    Guo-Yan Meng

  2. Department of Mathematics, Taiyuan Normal University, Taiyuan, 030012, Shanxi Province, P.R. China

    Chuan-Long Wang & Xi-Hong Yan

Authors
  1. Guo-Yan Meng
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  2. Chuan-Long Wang
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  3. Xi-Hong Yan
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Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne Burwood Campus, 221 Burwood Highway, 3125, Burwood, VIC, Australia

    Yang Xiang

  2. Media Distribution, Telstra Corporation Limited, 21/35 Collins St, 3000, Melbourne, VIC, Australia

    Mukaddim Pathan

  3. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, QLD, Australia

    Xiaohui Tao

  4. Department of Mathematics and Computing, The University of Southern Queensland, Toowoomba, QLD, Australia

    Hua Wang

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© 2012 Springer-Verlag Berlin Heidelberg

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Meng, GY., Wang, CL., Yan, XH. (2012). Self-adaptive Non-stationary Parallel Multisplitting Two-Stage Iterative Methods for Linear Systems. In: Xiang, Y., Pathan, M., Tao, X., Wang, H. (eds) Data and Knowledge Engineering. ICDKE 2012. Lecture Notes in Computer Science, vol 7696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34679-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-34679-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34678-1

  • Online ISBN: 978-3-642-34679-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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