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Locally optimal and heavy ball GMRES methods

  • Akira ImakuraEmail author
  • Ren-Cang Li
  • Shao-Liang Zhang
Original Paper Area 2

Abstract

The restarted GMRES (REGMRES) is one of the well used Krylov subspace methods for solving linear systems. However, the price to pay for the restart usually is slower speed of convergence. In this paper, we draw inspirations from the locally optimal CG and the heavy ball methods in optimization to propose two variants of the restarted GMRES that can overcome the slow convergence. Compared to various existing hybrid GMRES which are also designed to speed up REGMRES and which usually require eigen-region estimations, our variants preserve the appealing feature of GMRES and REGMRES—their simplicity. Numerical tests on real data are presented to demonstrate the superiority of the new methods over REGMRES and its variants.

Keywords

GMRES Restarted GMRES Locally optimal GMRES Heavy ball GMRES Linear system 

Mathematics Subject Classification

65F10 

Notes

Acknowledgments

The authors are grateful to an anonymous referee for useful comments.

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Copyright information

© The JJIAM Publishing Committee and Springer Japan 2016

Authors and Affiliations

  1. 1.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan
  2. 2.School of Mathematical ScienceXiamen UniversityXiamenPeople’s Republic of China
  3. 3.Department of MathematicsUniversity of Texas at ArlingtonArlingtonUSA
  4. 4.Department of Computational Science and Engineering, Graduate School of EngineeringNagoya UniversityNagoyaJapan
  5. 5.CREST, JSTTokyoJapan

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