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On estimating the largest eigenvalue with the Lanczos algorithm

Authors: B. N. Parlett, H. Simon and L. M. Stringer
Journal: Math. Comp. 38 (1982), 153-165
MSC: Primary 65F15
MathSciNet review: 637293
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Abstract: The Lanczos algorithm applied to a positive definite matrix produces good approximations to the eigenvalues at the extreme ends of the spectrum after a few iterations. In this note we utilize this behavior and develop a simple algorithm which computes the largest eigenvalue. The algorithm is especially economical if the order of the matrix is large and the accuracy requirements are low. The phenomenon of misconvergence is discussed. Some simple extensions of the algorithm are also indicated. Finally, some numerical examples and a comparison with the power method are given.

References [Enhancements On Off] (What's this?)

  • [1] D. O'Leary, G. W. Stewart & J. S. Vandergraft, ``Estimating the largest eigenvalue of a positive definite matrix,'' Math. Comp., v. 33, 1979, pp. 1289-1292. MR 537973 (80d:65048)
  • [2] B. N. Parlett, The Symmetric Eigenvalue Problem, Prentice-Hall, Englewood Cliffs, N. J., 1980. MR 570116 (81j:65063)
  • [3] B. N. Parlett & J. K. Reid, Tracking the Process of the Lanczos Algorithm for Large Symmetric Eigenproblems, IMA J. Numer. Anal., v. 1, 1981, pp. 135-155. MR 616327 (82e:65039)
  • [4] L. M. Stringer, Efficient and Optimal Methods for Finding the Largest Eigenvalue of a Real Symmetric Matrix, M. A. Thesis, Univ. of California, Berkeley, 1980.

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Article copyright: © Copyright 1982 American Mathematical Society

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