1. Chapter 5 Eigenvalues and Eigenvectors 大葉大學 資訊工程系 黃鈴玲 Linear Algebra

2. Ch5_2 5.1 Eigenvalues and Eigenvectors Definition Let A be an n  n matrix. A scalar is called an eigenvalue ( 特徵 值, 固有值 ) of A if there exists a nonzero vector x in R n such that Ax = x. The vector x is called an eigenvector corresponding to. Figure 6.1

3. Ch5_3 Computation of Eigenvalues and Eigenvectors Let A be an n  n matrix with eigenvalue and corresponding eigenvector x. Thus Ax = x. This equation may be written Ax – x = 0 given (A – I n )x = 0 Solving the equation |A – I n | = 0 for leads to all the eigenvalues of A. On expending the determinant |A – I n |, we get a polynomial in. This polynomial is called the characteristic polynomial of A. The equation |A – I n | = 0 is called the characteristic equation of A.

4. Ch5_4 Example 1 Find the eigenvalues and eigenvectors of the matrix Let us first derive the characteristic polynomial of A. We get Solution We now solve the characteristic equation of A. The eigenvalues of A are 2 and –1. The corresponding eigenvectors are found by using these values of in the equation(A – I 2 )x = 0. There are many eigenvectors corresponding to each eigenvalue.

5. Ch5_5 = 2 We solve the equation (A – 2I 2 )x = 0 for x. The matrix (A – 2I 2 ) is obtained by subtracting 2 from the diagonal elements of A. We get This leads to the system of equations giving x 1 = –x 2. The solutions to this system of equations are x 1 = –r, x 2 = r, where r is a scalar. Thus the eigenvectors of A corresponding to = 2 are nonzero vectors of the form

6. Ch5_6 = –1 We solve the equation (A + 1I 2 )x = 0 for x. The matrix (A + 1I 2 ) is obtained by adding 1 to the diagonal elements of A. We get This leads to the system of equations Thus x 1 = –2x 2. The solutions to this system of equations are x 1 = –2s and x 2 = s, where s is a scalar. Thus the eigenvectors of A corresponding to = –1 are nonzero vectors of the form 隨堂作業： 9(a) 先不求 eigenspaces

7. Ch5_7 Theorem 5.1 Let A be an n  n matrix and an eigenvalue of A. The set of all eigenvectors corresponding to, together with the zero vector, is a subspace of R n. This subspace is called the eigenspace of. Proof Let x 1 and x 2 be two vectors in the eigenspace of and let c be a scalar. Then Ax 1 = x 1 and Ax 2 = x 2. Hence, Thus is a vector in the eigenspace of. The set is closed under addition.

8. Ch5_8 Further, since Ax 1 = x 1, Therefore cx 1 is a vector in the eigenspace of. The set is closed scalar multiplication. Thus the set is a subspace of R n.

9. Ch5_9 Example 2 Find the eigenvalues and eigenvectors of the matrix The matrix A – I 3 is obtained by subtracting from the diagonal elements of A.Thus Solution The characteristic polynomial of A is |A – I 3 |. Using row and column operations to simplify determinants, we get

10. Ch5_10 We now solving the characteristic equation of A: The eigenvalues of A are 10 and 1. The corresponding eigenvectors are found by using three values of in the equation (A – I 3 )x = 0.

11. Ch5_11 = 10 We get The solution to this system of equations are x 1 = 2r, x 2 = 2r, and x 3 = r, where r is a scalar. Thus the eigenspace of = 10 is the one-dimensional space of vectors of the form.

12. Ch5_12 = 1 Let = 1 in (A – I 3 )x = 0. We get The solution to this system of equations can be shown to be x 1 = – s – t, x 2 = s, and x 3 = 2t, where s and t are scalars. Thus the eigenspace of = 1 is the space of vectors of the form.

13. Ch5_13 Separating the parameters s and t, we can write Thus the eigenspace of = 1 is a two-dimensional subspace of R 2 with basis If an eigenvalue occurs as a k times repeated root of the characteristic equation, we say that it is of multiplicity k. Thus =10 has multiplicity 1, while =1 has multiplicity 2 in this example. 隨堂作業： 10

14. Ch5_14 Example 3 Let A be an n  n matrix A with eigenvalues 1, …, n, and corresponding eigenvectors X 1, …, X n. Prove that if c  0, then the eigenvalues of cA are c 1, …, c n with corresponding eigenvectors X 1, …, X n. Solution Let i be one of eigenvalues of A with corresponding eigenvectors X i. Then AX i = i X i. Multiply both sides of this equation by c to get cAX i = c i X i Thus c i is an eigenvalues of cA with corresponding eigenvector X i. Further, since cA is n  n matrix, the characteristic polynomial of A is of degree n. The characteristic equation has n roots, implying that cA has n eigenvalues. The eigenvalues of cA are therefore c 1, …, c n with corresponding eigenvectors X 1, …, X n. 隨堂作業： 28

15. Ch5_15 Homework Exercise 5.1: 9, 10, 13, 15, 24, 26, 32 Ex24: Prove that if A is a diagonal matrix, then its eigenvalues are the diagonal elements. Ex26: Prove that if A and A t have the same eigenvalues. Ex32: Prove that the constant term of the characteristic polynomial of a matrix A is |A|.

16. Ch5_16 5.3 Diagonalization of Matrices Definition Let A and B be square matrices of the same size. B is said to be similar to A if there exists an invertible matrix C such that B = C –1 AC. The transformation of the matrix A into the matrix B in this manner is called a similarity transformation.

17. Ch5_17 Example 1 Consider the following matrices A and C. C is invertible. Use the similarity transformation C –1 AC to transform A into a matrix B. Solution 隨堂作業： 1(b)

18. Ch5_18 Theorem 5.3 Similar matrices have the same eigenvalues. Proof Let A and B be similar matrices. Hence there exists a matrix C such that B = C –1 AC. The characteristic polynomial of B is |B – I n |. Substituting for B and using the multiplicative properties of determinants, we get The characteristic polynomials of A and B are identical. This means that their eigenvalues are the same.

19. Ch5_19 Definition A square matrix A is said to be diagonalizable if there exists a matrix C such that D = C –1 AC is a diagonal matrix. Theorem 5.4 Let A be an n  n matrix. (a)If A has n linearly independent eigenvectors, it is diagonalizable. The matrix C whose columns consist of n linearly independent eigenvectors can be used in a similarity transformation C –1 AC to give a diagonal matrix D. The diagonal elements of D will be the eigenvalues of A. (b)If A is diagonalizable, then it has n linearly independent eigenvectors

20. Ch5_20 Proof (a) Let A have eigenvalues 1, …, n, with corresponding linearly independent eigenvectors v 1, …, v n. Let C be the matrix having v 1, …, v n as column vectors. C = [v 1 … v n ] Since Av 1 = 1 v 1, …, Av n = 1 v n, matrix multiplication in terms of columns gives

21. Ch5_21 Since the columns of C are linearly independent, C is nonsingular. Thus Therefore, if an n  n matrix A has n linearly independent eigenvectors, these eigenvectors can be used as the columns of a matrix A that diagonalizes A. The diagonal matrix has the eigenvaules of A as diagonal elements.

22. Ch5_22 (b) The converse is proved by retracting the above steps. Commence with the assumption that C is a matrix [v 1 … v n ] that diagonalizes A. Thus, there exist scalars  1, …,  n, such that Retracting the above steps, we arrive at the conclusion that Av 1 =  1 v 1, …, Av n =  n v n The v 1, …, v n are eigenvectors of A. Since C is nonsingular, these vectors (column vectors of C) are linearly independent. Thus if an n  n matrix A is diagonalizable, it has n linearly independent eigenvectors.

23. Ch5_23 Example 2 (a)Show that the following matrix A is diagonalizable. (b)Find a diagonal matrix D that is similar to A. (c)Determine the similarity transformation that diagonalizes A. Solution (a)The eigenvalues and corresponding eigenvector of this matrix were found in Example 1 of Section 5.1. They are Since A, a 2  2 matrix, has two linearly independent eigenvectors, it is diagonalizable.

24. Ch5_24 (b) A is similar to the diagonal matrix D, which has diagonal elements 1 = 2 and 2 = –1. Thus (c) Select two convenient linearly independent eigenvectors, say Let these vectors be the column vectors of the diagonalizing matrix C. We get 隨堂作業： 3(a)

25. Ch5_25 If A is similar to a diagonal matrix D under the transformation C –1 AC, then it can be shown that A k = CD k C –1. This result can be used to compute A k. Let us derive this result and then apply it. This leads to Note

26. Ch5_26 Example 3 Compute A 9 for the following matrix A. Solution A is the matrix of the previous example. Use the values of C and D from that example. We get 隨堂作業： 9(a)

27. Ch5_27 Example 4 Show that the following matrix A is not diagonalizable. Solution The characteristic equation is There is a single eigenvalue, = 2. We find the corresponding eigenvectors. (A – 2I 2 )x = 0 gives Thus x 1 = r, x 2 = r. The eigenvectors are nonzero vectors of the form The eigenspace is a one-dimensional space. A is a 2  2 matrix, but it does not have two linearly independent eigenvectors. Thus A is not diagonalizable. 隨堂作業： 3(c)

28. Ch5_28 Theorem 5.5 Let A be an n  n symmetric matrix. (a)All the eigenvalues of A are real numbers. (b)The dimensional of an eigenspace of A is the multiplicity of the eigenvalues as a root of the characteristic equation. (c)The eigenspaces of A are orthogonal. (d) A has n linearly independent eigenvectors. Definition A square matrix A is said to be orthogonally diagonalizable if there exists an orthogonal matrix C such that D = C  1 AC is a diagonal matrix. Orthogonal Diagonalization

29. Ch5_29 Theorem 5.6 Let A be a square matrix. A is orthogonally diagonalizable if and only if it is a symmetric matrix. Example 5 Orthogonally diagonalize the following symmetric matrix A. Solution The eigenvalues and corresponding eigenspaces of this matrix are

30. Ch5_30 Let us determine the transformation. The eigenspaces V 1 and V 2 are orthogonal. Use a unit vector in each eigenspace as columns of an orthogonal matrix C. We get 隨堂作業： 6(a) The orthogonal transformation that leads to D is Since A is symmetric, it can be diagonalized to give

31. Ch5_31 Homework Exercise 5.3: 1, 2, 6, 9