Elementary Linear Algebra 6e - Larson, Edwards, Falvo - Chapter 10.3

# Elementary Linear Algebra 6e - Larson, Edwards, Falvo - Chapter 10.3

This preview shows pages 1–3. Sign up to view the full content.

10.3 POWER METHOD FOR APPROXIMATING EIGENVALUES In Chapter 7 you saw that the eigenvalues of an matrix A are obtained by solving its characteristic equation For large values of n , polynomial equations like this one are difficult and time-consuming to solve. Moreover, numerical techniques for approximating roots of polynomial equations of high degree are sensitive to rounding errors. In this section you will look at an alterna- tive method for approximating eigenvalues. As presented here, the method can be used only to find the eigenvalue of A that is largest in absolute value—this eigenvalue is called the dominant eigenvalue of A . Although this restriction may seem severe, dominant eigenval- ues are of primary interest in many physical applications. Not every matrix has a dominant eigenvalue. For instance, the matrix with eigenvalues of and has no dominant eigenvalue. Similarly, the matrix with eigenvalues of and has no dominant eigenvalue. EXAMPLE 1 Finding a Dominant Eigenvalue Find the dominant eigenvalue and corresponding eigenvectors of the matrix Solution From Example 4 of Section 7.1 you know that the characteristic polynomial of A is So the eigenvalues of A are and of which the dominant one is From the same example you know that the dominant eigenvectors of A those corresponding to are of the form t Þ 0. x 5 t 3 3 1 4 , l 2 52 2 d s 2 2. 2 2, 1 1 2 1 3 1 2 5 s 1 1 ds 1 2 d . A 5 3 2 1 2 12 2 5 4 . 3 5 1 d 1 5 2, 2 5 2, s A 5 3 2 0 0 0 2 0 0 0 1 4 2 1 d 1 5 1 s A 5 3 1 0 0 2 1 4 n 1 c n 2 1 n 2 1 1 c n 2 2 n 2 2 1 . . . 1 c 0 5 0. n 3 n 586 CHAPTER 10 NUMERICAL METHODS Definition of Dominant Eigenvalue and Dominant Eigenvector Let and be the eigenvalues of an matrix A . is called the dominant eigenvalue of A if The eigenvectors corresponding to are called dominant eigenvectors of A . 1 i 5 2, . . . , n . | 1 | > | i | , 1 n 3 n n 1 , 2 , . . . ,

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
The Power Method Like the Jacobi and Gauss-Seidel methods, the power method for approximating eigenval- ues is iterative. First assume that the matrix A has a dominant eigenvalue with correspond- ing dominant eigenvectors. Then choose an initial approximation of one of the dominant eigen vectors of A . This initial approximation must be a nonzero vector in Finally, form the sequence given by For large powers of k , and by properly scaling this sequence, you will see that you obtain a good approximation of the dominant eigenvector of A . This procedure is illustrated in Example 2. EXAMPLE 2 Approximating a Dominant Eigenvector by the Power Method Complete six iterations of the power method to approximate a dominant eigenvector of Solution Begin with an initial nonzero approximation of Then obtain the following approximations. Iteration “Scaled” Approximation 190 3 2.99 1.00 4 x 6 5 A x 5 5 3 2 1 2 12 2 5 43 2 280 2 94 4 5 3 568 190 4 2 94 3 2.98 1.00 4 x 5 5 A x 4 5 3 2 1 2 12 2 5 43 136 46 4 5 3 2 280 2 94 4 46 3 2.96 1.00 4 x 4 5 A x 3 5 3 2 1 2 12 2 5 43 2 64 2 22 4 5 3 136 46 4 2 22 3 2.91 1.00 4 x 3 5 A x 2 5 3 2 1 2 12 2 5 43 28 10 4 5 3 2 64 2 22 4 10 3 2.80 1.00 4 x 2 5 A x 1 5 3 2 1 2 12 2 5 43 2 10 2 4 4 5 3 28 10 4 2 4 3 2.50 1.00 4 x 1 5 A x 0 5 3 2 1 2 12 2 5 43 1 1 4 5 3
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 02/24/2012 for the course MATH 310 taught by Professor Staff during the Spring '08 term at VCU.

### Page1 / 9

Elementary Linear Algebra 6e - Larson, Edwards, Falvo - Chapter 10.3

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online