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Convergence of algorithms for finding eigenvectors


In this paper we give a rigorous analysis of convergence of algorithms for finding eigenvectors of a real symmetric matrix. The algorithms are deterministics and our methods are very intuitive.

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  1. 1

    J.H. Wilkinson. The Algebraic Eigenvalue Problem. Oxford University Press, London, 1965

    MATH  Google Scholar 

  2. 2

    A.R. Gourlay, G.A. Watson. Computational Methods for Matrix Eigenproblems. John Wiley & Sons, Inc., New York, 1973.

    Google Scholar 

  3. 3

    Z.H. Cao, Matrix Eigenproblems. Shanghai Science and Technology Press, Shaughai, 1980 (in Chinese)

    Google Scholar 

  4. 4

    B.N. Parlett. The Symmetric Eigenvalue Problem. Printice Hall, New York, 1980.

    MATH  Google Scholar 

  5. 5

    E. Oja. Subspace Methods of Pattern Recognition, Research Studies Press Ltd., Letchworth., Hertfordshire, 1983.

    Google Scholar 

  6. 6

    J.H. Zhang, H.F. Chen. Convergence of Algorithms Used for Principal Component Analysis.Science in China (Series E), 1997, 40(6): 597–604

    MATH  Article  MathSciNet  Google Scholar 

  7. 7

    W. Rudin. Real and Complex Analysis. 2nd edition, McGraw-Hill, New York, 1974.

    MATH  Google Scholar 

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Junhua, Z. Convergence of algorithms for finding eigenvectors. Acta Mathematicae Applicatae Sinica 16, 355–361 (2000).

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Key words

  • Eigenvectors
  • algorithms
  • convergence