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Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)

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Presentation on theme: "Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)"— Presentation transcript:

1 Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)

2 In the previous slide Special matrices –strictly diagonally dominant matrix –symmetric positive definite matrix Cholesky decomposition –tridiagonal matrix Iterative techniques –Jacobi, Gauss-Seidel and SOR methods –conjugate gradient method Nonlinear systems of equations (Exercise 3) 2

3 In this slide Eigenvalues and eigenvectors The power method –locate the dominant eigenvalue Inverse power method Deflation 3

4 Chapter 4 4 Eigenvalues and eigenvectors

5 5

6 In Chapter 4 Determine the dominant eigenvalue Determine a specific eigenvalue Remove a eigenvalue Determine all eigenvalues 6

7 4.1 7 The power method

8 Different problems have different requirements –a single, several or all of the eigenvalues –the corresponding eigenvectors may or may not also be required To handle each of these situations efficiently, different strategies are required The power method –an iterative technique –locate the dominant eigenvalue –also computes an associated eigenvector –can be extended to compute eigenvalues 8

9 The power method Basics 9

10 10

11 11

12 The power method Approximated eigenvalue 12

13 Any Questions? 13

14 The power method A common practice 14 question

15 The power method A common practice 15

16 16

17 The power method Complete procedure 17

18 Any Questions? 18

19 19 In action

20 20 what is the first estimate?

21 21

22 22

23 23

24 Any Questions? 24 The power method for generic matrices

25 25 The power method for symmetric matrices

26 26

27 The power method variation 27

28 28

29 The power method Approximated eigenvalue 29 Recall that

30 Any Questions? 30 The power method for symmetric matrices

31 Why to 31 Require the matrix to be symmetric?

32 Any Questions? The power method

33 33 An application of eigenvalue

34 34

35 35

36 Undirected graph Relation to eigenvalue Proper coloring –how to color the geographic regions on a map regions that share a common border receive different colors Chromatic number –the minimum number of colors that can be used in a proper coloring of a graph 36

37 Undirected graph The dominant eigenvalue 37

38 Undirected graph The corresponding eigenvector 38

39 Any Questions? 39

40 The inverse power method

41 41 later

42 42

43 43

44 44

45 The inverse power method 45

46 46

47 Any Questions? 47

48 How to 48 Find the eigenvalue smallest in magnitude

49 Any Questions? The inverse power method

50 Deflation

51 So far, we can approximate –the dominant eigenvalue of a matrix –the one smallest in magnitude –the one closest to a specific value What if we need several of the largest/smallest eigenvalues? Deflation –to remove an already determined solution, while leaving the remainder solutions unchanged 51

52 52

53 53

54 54 Recall that

55 Deflation Shift an eigenvalue to zero 55

56 56 While leaving the remaining eigenvalues unchanged

57 57

58 58

59 Deflation Summary 59

60 Any Questions? 60

61 Do we 61 Miss something?

62 62 Recall that

63 63

64 Wielandt deflation 64

65 65

66 Wielandt deflation Bonus 66

67 67 In action

68 68

69 69

70 Hotelling deflation 70

71 71

72 Any Questions? Deflation


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