Unraveling the Mysteries of Power and Inverse Power Methods for Eigenvalues

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SUMMARY

The discussion focuses on the Power and Inverse Power Methods for calculating eigenvalues of a matrix A. The Power Method is defined as x(k+1) = Ax(k) for finding the largest eigenvalue, while the Inverse Power Method, represented as Ax(k+1) = x(k), is used to find the smallest eigenvalue. The convergence rates for both methods are determined by the ratios |Ln-1/Ln| and |L1/L2|, respectively. The discussion also emphasizes the conditions under which these convergence rates hold true, particularly when A is symmetric and x(k) approaches an eigenvector.

PREREQUISITES
  • Understanding of eigenvalues and eigenvectors
  • Familiarity with matrix operations and linear algebra concepts
  • Knowledge of convergence criteria in numerical methods
  • Experience with Rayleigh quotient for eigenvalue approximation
NEXT STEPS
  • Study the derivation and applications of the Power Method for eigenvalue computation
  • Explore the Inverse Power Method and its effectiveness in finding the smallest eigenvalue
  • Learn about the Rayleigh quotient and its role in eigenvalue problems
  • Investigate convergence analysis techniques for iterative methods in linear algebra
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Students and professionals in mathematics, engineering, and computer science who are working with numerical methods for eigenvalue problems, particularly those interested in matrix analysis and iterative algorithms.

pinodk
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I have two excercises which have been causing me to tear my hair off for some time now.
(a) the power method to find largest eigenvalue of A is defined as x(k+1) = Ax(k)
(b) the inverse power method is to solve Ax(k+1) = x(k) to find smallest eigenvalue of A
(c) the smallest/largest eigenvalue is then found by the Rayleigh quotient of A and x : R_A(x) = (Ax,x)/(x,x)

The eigenvalues of A are arranged as lambda_n >= lambda_n-1 >=... lambda_2 >= lambda_1

Henceforth lambda_i will be written as Li, so
Ln >= Ln-1 ... >= L2 >= L1

exercise 1.)
The convergence of x(k) in (a) and (b) is of order 1 with rate of convergence |Ln-1/Ln| and |L1/L2| respectively.
State conditions for this to be true for each coordinate.

that was the first, my question is then, what does that question mean (its translated from danish, hope i did it right :-S)? I read it as:
give the conditions for the eigenvalues that must be fulfilled in order for the above to be true, and show that it applies to each coordinate in x(k)...
If that makes sense to you, please help me on how to get on with this exercise, because I am still stucked...

exercise 2.)
Assume that A is symmetrical, and that x(k) converges towards (a multiple of) the eigenvector, so that each coordinate of x(k) converges with rate of convergence c.
Show that L(k) converges by order 1 with rate of convergence c^2.

Again I am baffled... please get me started :-)
 
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guess I am not the only one finding it difficult, eh? Or have i been unclear in the formulation?
 

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