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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 Rootfinding –multiplicity Bisection method –Intermediate Value Theorem –convergence measures False position –yet another simple enclosure method –advantage and disadvantage in comparison with bisection method 2

3 In this slide Fixed point iteration scheme –what is a fixed point? –iteration function –convergence Newtons method –tangent line approximation –convergence Secant method 3

4 Rootfinding Simple enclosure –Intermediate Value Theorem –guarantee to converge convergence rate is slow –bisection and false position Fixed point iteration –Mean Value Theorem –rapid convergence loss of guaranteed convergence 4

5 2.3 5 Fixed Point Iteration Schemes

6 6

7 7 There is at least one point on the graph at which the tangent lines is parallel to the secant line

8 Mean Value Theorem 8

9 9

10 Fixed points 10

11 11

12 Number of fixed points According to the previous figure, a trivial question is –how many fixed points of a given function? 12

13 13

14 14

15 Only sufficient conditions Namely, not necessary conditions –it is possible for a function to violate one or more of the hypotheses, yet still have a (possibly unique) fixed point 15

16 Fixed point iteration 16

17 Fixed point iteration 17

18 18 In action

19 19

20 20

21 Any Questions? 21 About fixed point iteration

22 Relation to rootfinding Now we know what fixed point iteration is, but how to apply it on rootfinding? More precisely, given a rootfinding equation, f(x)=x 3 +x 2 -3x-3=0, what is its iteration function g(x) ? 22 hint

23 Iteration function 23

24 24

25 25 In action

26 26

27 Analysis 27

28 Convergence 28

29 29

30 30

31 31

32 32

33 Order of convergence of fixed point iteration schemes 33

34 34

35 35

36 36

37 37

38 38

39 Stopping condition 39

40 40

41 Two steps 41

42 The first step 42

43 The second step 43

44 Any Questions? Fixed Point Iteration Schemes

45 Newtons Method

46 46

47 47

48 Newtons Method Definition 48

49 49 In action

50 50

51 In the previous example 51

52 52

53 Any Questions? 53

54 Initial guess 54 example answer

55 Initial guess 55 answer

56 Initial guess 56

57 57

58 Convergence analysis for Newtons method 58

59 59 The simplest plan is to apply the general fixed point iteration convergence theorem

60 Analysis strategy 60

61 61

62 62

63 63

64 64

65 Any Questions? 65

66 Newtons Method Guaranteed to Converge? 66 hint answer

67 Newtons Method Guaranteed to Converge? 67 answer

68 Newtons Method Guaranteed to Converge? 68

69 69 Oh no! After these annoying analyses, the Newtons method is still not guaranteed to converge!?

70 Dont worry Actually, there is an intuitive method Combine Newtons method and bisection method –Newtons method first –if an approximation falls outside current interval, then apply bisection method to obtain a better guess (Can you write an algorithm for this method?) 70

71 Newtons Method Convergence analysis 71

72 72 Recall that

73 73 Is Newtons method always faster?

74 74

75 75 In action

76 76

77 Any Questions? Newtons Method

78 Secant Method

79 Secant method Because that Newtons method –2 function evaluations per iteration –requires the derivative Secant method is a variation on either false position or Newtons method –1 additional function evaluation per iteration –does not require the derivative Lets see the figure first 79 answer

80 80

81 Secant method 81

82 82

83 83

84 84

85 85

86 Any Questions? Secant Method


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