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```SECTION 10.3
Ax 2 12
5
1
POWER METHOD FOR APPROXIMATING EIGENVALUES
589
6.02
1.00 2.01
2.99
Then, because
Ax x 6.022.99 2.011 20.0
and
x
x 2.992.99 11 9.94,
you can compute the Rayleigh quotient to be
Ax x 20.0
2.01
xx
9.94
which is a good approximation of the dominant eigenvalue 2.
From Example 2 you can see that the power method tends to produce approximations
with large entries. In practice it is best to âscale downâ each approximation before proceeding to the next iteration. One way to accomplish this scaling is to determine the component of Axi that has the largest absolute value and multiply the vector Axi by the
reciprocal of this component. The resulting vector will then have components whose
absolute values are less than or equal to 1. (Other scaling techniques are possible. For
examples, see Exercises 27 and 28.)
EXAMPLE 4
The Power Method with Scaling
Calculate seven iterations of the power method with scaling to approximate a dominant
eigenvector of the matrix
1
A 2
1
2
1
3
0
2 .
1
Use x0 1, 1, 1 as the initial approximation.
Solution
One iteration of the power method produces
1
Ax0 2
1
2
1
3
0
2
1
1
3
1 1 ,
1
5
and by scaling you obtain the approximation
3
0.60
1
x1 5 1 0.20 .
5
1.00
```
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