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Development of powerful algorithm for maximal eigenpair

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Abstract

Based on a series of recent papers, a powerful algorithm is reformulated for computing the maximal eigenpair of self-adjoint complex tridiagonal matrices. In parallel, the same problem in a particular case for computing the sub-maximal eigenpair is also introduced. The key ideas for each critical improvement are explained. To illustrate the present algorithm and compare it with the related algorithms, more than 10 examples are included.

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References

  1. Chen M F. Eigenvalues, Inequalities, and Ergodic Theory. London: Springer, 2005

    MATH  Google Scholar 

  2. Chen M F. Speed of stability for birth-death processes. Front Math China, 2010, 5(3): 379–515

    Article  MathSciNet  MATH  Google Scholar 

  3. Chen M F. Criteria for discrete spectrum of 1D operators. Commun Math Stat, 2014, 2: 279–309

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen M F. Efficient initials for computing the maximal eigenpair. Front Math China, 2016, 11(6): 1379–1418 See also Vol 4 in the middle of author’s homepage: http://math0.bnu.edu.cn/~chenmf A package based on the paper is available on CRAN now (by X. J. Mao). One may check it through the link: https://github.com/mxjki/PowerfulMaxEigenpair A Matlab package is also available, see the author’s homepage above The authors’ papers cited in this article can be found from Vols 1–4 in the middle of the homepage above

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen M F. The charming leading eigenpair. Adv Math (China), 2017, 46(4): 281–297

    MATH  Google Scholar 

  6. Chen M F. Global algorithms for maximal eigenpair. Front Math China, 2017, 12(5): 1023–1043

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen M F. Trilogy on computing maximal eigenpair. In: Yue W, Li Q L, Jin S, Ma Z, eds. Queueing Theory and Network Applications (QTNA 2017). Lecture Notes in Comput Sci, Vol 10591. Cham: Springer, 2017, 312–329

    Google Scholar 

  8. Chen M F. Hermitizable, isospectral complex matrices or differential operators. Front Math China, 2018, 13(6): 1267–1311

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen M F, Zhang X. Isospectral operators. Commun Math Stat, 2014, 2: 17–32

    Article  MathSciNet  MATH  Google Scholar 

  10. Cipra B A. The best of the 20th century: Editors name top 10 algorithms. SIAM News, 2000, 33(4): 1–2

    Google Scholar 

  11. Frolov A V, Voevodin V V, Teplov A. Thomas algorithm, pointwise version. https://algowiki-project.org/en/Thomas_algorithm,_pointwise_version

  12. From Wikipedia. Tridiagonal matrix algorithm. https://en.wikipedia.org/wiki/Tri-diagonal_matrix_algorithm

  13. Golub G H, van der Vorst H A. Eigenvalue computation in the 20th century. J Comput Appl Math, 2000, 123(1–2): 35–65

    Article  MathSciNet  MATH  Google Scholar 

  14. Householder A S. Unitary triangularization of a nonsymmetric matrix. J Assoc Comput Mach, 1958, 5: 339–342

    Article  MathSciNet  MATH  Google Scholar 

  15. Moler C. Llewellyn Thomas. https://en.wikipedia.org/wiki/Llewellyn+Thomas 1996

  16. Shukuzawa O, Suzuki T, Yokota I. Real tridiagonalization of Hermitian matrices by modified Householder transformation. Proc Japan Acad Ser A, 1996, 72: 102–103

    Article  MathSciNet  MATH  Google Scholar 

  17. Stewart G W. The decompositional approach to matrix computation. IEEE Comput Sci Eng, 2000, 2(1): 50–59

    Article  Google Scholar 

  18. Tang T, Yang J. Computing the maximal eigenpairs of large size tridiagonal matrices with O(1) number of iterations. Numer Math Theory Methods Appl, 2018, 11(4): 877–894

    Article  MathSciNet  Google Scholar 

  19. van der Vorst H A, Golub G H. 150 Years old and still alive: eigenproblems. In: Duff I S, Watson G A, eds. The State of the Art in Numerical Analysis. Oxford: Oxford Univ Press, 1997, 93–119

    Google Scholar 

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Acknowledgements

The authors thank MS Zhou-Jing Wang for providing a program in MatLab on the Householder transformation for Hermitian matrix. This work was supported in part by the National Natural Science Foundation of China (Grant No. 11771046), the Project from the Ministry of Education in China, and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Correspondence to Yue-Shuang Li.

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Chen, MF., Li, YS. Development of powerful algorithm for maximal eigenpair. Front. Math. China 14, 493–519 (2019). https://doi.org/10.1007/s11464-019-0769-5

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