1. Open Methods Chapter 6 The Islamic University of Gaza
Faculty of Engineering
Civil Engineering Department
Numerical Analysis
ECIV 3306
Chapter 6
Open Methods

2. Open Methods
Bracketing methods are based on assuming an interval of the function which brackets the root.
The bracketing methods always converge to the root.
Open methods are based on formulas that require only a single starting value of x or two starting values that do not necessarily bracket the root.
These method sometimes diverge from the true root.

3. 1. Simple Fixed-Point Iteration
Rearrange the function so that x is on the left side of the equation:
Bracketing methods are “convergent”.
Fixed-point methods may sometime “diverge”, depending on the stating point (initial guess) and how the function behaves.

4. Simple Fixed-Point Iteration
Examples: 1.
f(x) = x 2-2x+3  x = g(x)=(x2+3)/2
f(x) = sin x  x = g(x)= sin x + x
f(x) = e-x- x  x = g(x)= e-x

5. Simple Fixed-Point Iteration Convergence
x = g(x) can be expressed as a pair of equations:
y1= x
y2= g(x)…. (component equations)
Plot them separately.

6. Simple Fixed-Point Iteration Convergence
Fixed-point iteration converges if :
When the method converges, the error is roughly proportional to or less than the error of the previous step, therefore it is called “linearly convergent.”

7. Simple Fixed-Point Iteration-Convergence

8. Steps of Simple Fixed Pint Iteration
1. Rearrange the equation f(x) = 0 so that x is on the left hand side and g(x) is on the right hand side.
e.g f(x) = x2-2x-1 = 0  x= (x2-1)/2
g(x) = (x2-1)/2
2. Set xi at an initial guess xo.
3. Evaluate g(xi)
4. Let xi+1 = g(xi)
5. Find a=(Xi+1 – xi)/Xi+1, and set xi at xi+1
6. Repeat steps 3 through 5 until |a|<= a

9. Example: Simple Fixed-Point Iteration
f(x) = e-x - x
f(x)
f(x)=e-x - x
1. f(x) is manipulated so that we get x=g(x) g(x) = e-x 2. Thus, the formula predicting the new value of x is: xi+1 = e-xi 3. Guess xo = 0 4. The iterations continues till the approx. error reaches a certain limiting value
Root x
f(x)
f1(x) = x
g(x) = e-x x

10. Example: Simple Fixed-Point Iteration
i xi g(xi) ea% et%

11. Example: Simple Fixed-Point Iteration
i xi g(xi) ea% et%

12. Ex 5.1

13. Flow Chart – Fixed Point
Start
Input: xo , s, maxi
i=0
a=1.1s 1

14. 1 False Stop True while a< s & i >maxi
xn=0
x0=xn
Print: xo, f(xo) ,a , i
False
True

15. 2. The Newton-Raphson Method
Most widely used method.
Based on Taylor series expansion:
Solve for
Newton-Raphson formula

16. The Newton-Raphson Method
A tangent to f(x) at the initial point xi is extended till it meets the x-axis at the improved estimate of the root xi+1.
The iterations continues till the approx. error reaches a certain limiting value.
f(x)
Root x xi
xi+1
Slope f /(xi)
f(xi)

17. Example: The Newton Raphson Method
Use the Newton-Raphson method to find the root of e-x-x= 0  f(x) = e-x-x and f`(x)= -e-x-1; thus
Iter. xi et%
<10-8

18. Flow Chart – Newton Raphson
Start
Input: xo , s, maxi
i=0
a=1.1s 1

19. 1 False Stop True while a >s & i <maxi
xn=0
x0=xn
Print: xo, f(xo) ,a , i
False
True

20. Pitfalls of The Newton Raphson Method
Cases where Newton Raphson method diverges or exhibit poor convergence. a) Reflection point b) oscillating around a local optimum
c) Near zero slop , and d) zero slop

21. Thus, the formula predicting the xi+1 is:
3. The Secant Method
The derivative is sometimes difficult to evaluate by the computer program. It may be replaced by a backward finite divided difference
Thus, the formula predicting the xi+1 is:

22. The Secant Method
Requires two initial estimates of x , e.g, xo, x1. However, because f(x) is not required to change signs between estimates, it is not classified as a “bracketing” method.
The scant method has the same properties as Newton’s method. Convergence is not guaranteed for all xo, x1, f(x).

23. Secant Method: Example
Use the Secant method to find the root of e-x-x=0; f(x) = e-x-x and xi-1=0, x0=1 to get x1 of the first iteration using:
Iter xi-1 f(xi-1) xi f(xi) xi et%

24. Comparison of convergence of False Position and Secant Methods
Use two estimate xl and xu
Use two estimate xi and xi-1
f(x) must changes signs between xl and xu
f(x) is not required to change signs between xi and xi-1
Xr replaces whichever of the original values yielded a function value with the same sign as f(xr)
Xi+1 replace xi
Xi replace xi-1
Always converge
May be diverge
Slower convergence than Secant in case the secant converges.
If converges, It does faster then False Position

25. Comparison of convergence of False Position and Secant Methods
Use the false-position and secant method to find the root of f(x)=lnx. Start computation with xl= xi-1=0.5, xu=xi = 5.
False position method
Secant method
Iter xi xi xi+1
Iter xl xu xr

26. False Position and Secant Methods
Although the secant method may be divergent, when it converges it usually does so at a quicker rate than the false position method
See the next figure xl
xi-1 xu xi

27. Comparison of the true percent relative Errors Et for the methods to the determine the root of
f(x)=e-x-x

28. Flow Chart – Secant Method
Start
Input: x-1 , x0,s, maxi
i=0
a=1.1s 1

29. 1 False Stop True while a >s & i < maxi
Xi-1=xi
Xi=xi+1
Print: xi , f(xi) ,a , i
False
True

30. Modified Secant Method
Rather than using two initial values, an alternative approach is using a fractional perturbation of the independent variable to estimate
 is a small perturbation fraction

31. Modified Secant Method: Example
Use the modified secant method to find the root of f(x) = e-x-x and, x0=1 and =0.01

32. Multiple Roots f(x)= (x-3)(x-1)(x-1)(x-1) = x4- 6x3+ 125 x2- 10x+3
Double roots
f(x) 1 3
triple roots x

33. Multiple Roots
“Multiple root” corresponds to a point where a function is tangent to the x axis.
Difficulties
Function does not change sign with double (or even number of multiple root), therefore, cannot use bracketing methods.
Both f(x) and f′(x)=0, division by zero with Newton’s and Secant methods which may diverge around this root.

34. 4. The Modified Newton Raphson Method
Another u(x) is introduced such that u(x)=f(x)/f /(x);
Getting the roots of u(x) using Newton Raphson technique:
This function has roots at all the same locations as the original function

35. Modified Newton Raphson Method: Example
Using the Newton Raphson and Modified Newton Raphson evaluate the multiple roots of f(x)= x3-5x2+7x-3 with an initial guess of x0=0
Newton Raphson formula:
Modified Newton Raphson formula:

36. Modified Newton Raphson Method: Example
Newton Raphson Modified Newton-Raphson Iter xi et% iter xi et%
Newton Raphson technique is linearly converging towards the true value of 1.0 while the Modified Newton Raphson is quadratically converging.
For simple roots, modified Newton Raphson is less efficient and requires more computational effort than the standard Newton Raphson method

37. Systems of Nonlinear Equations
Roots of a set of simultaneous equations:
f1(x1,x2,…….,xn)=0
f2 (x1,x2,…….,xn)=0
fn (x1,x2,…….,xn)=0
The solution is a set of x values that simultaneously get the equations to zero.

38. Systems of Nonlinear Equations
Example: x2 + xy = 10 & y + 3xy2 = 57
u(x,y) = x2+ xy -10 = 0
v(x,y) = y+ 3xy2 -57 = 0
The solution will be the value of x and y which makes u(x,y)=0 and v(x,y)=0
These are x=2 and y=3
Numerical methods used are extension of the open methods for solving single equation; Fixed point iteration and Newton-Raphson. (we will only discuss the Newton Raphson)

39. Systems of Nonlinear Equations: 2. Newton Raphson Method
Recall the standard Newton Raphson formula:
which can be written as the following formula

40. Systems of Nonlinear Equations: 2. Newton Raphson Method
By multi-equation version (in this section we deal only with two equation) the formula can be derived in an identical fashion:
u(x,y)=0 and v(x,y)=0

41. Systems of Nonlinear Equations: 2. Newton Raphson Method
And thus

42. Systems of Nonlinear Equations: 2. Newton Raphson Method
x 2+ xy =10 and y + 3xy 2 = 57
are two nonlinear simultaneous equations with two unknown x and y they can be expressed in the form: use the point (1.5,3.5) as initial guess.