1 Roots of Equations Open Methods (Part 1) Fixed Point Iteration & Newton-Raphson Methods.

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Roots of EquationsOpen Methods

(Part 1)Fixed Point Iteration &

Newton-Raphson Methods

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The following root finding methods will be introduced:

A. Bracketing MethodsA.1. Bisection MethodA.2. Regula Falsi

B. Open MethodsB.1. Fixed Point IterationB.2. Newton Raphson's MethodB.3. Secant Method

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B. Open Methods

(a) Bisection method

(b) Open method (diverge)

(c) Open method (converge)

To find the root for f(x) = 0, we construct a magic formulae

xi+1 = g(xi)

to predict the root iteratively until x converge to a root. However, x may diverge!

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What you should know about Open Methods

How to construct the magic formulae g(x)?

How can we ensure convergence?

What makes a method converges quickly or diverge?

How fast does a method converge?

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B.1. Fixed Point Iteration• Also known as one-point iteration or

successive substitution

• To find the root for f(x) = 0, we reformulate f(x) = 0 so that there is an x on one side of the equation.

xxgxf )(0)(• If we can solve g(x) = x, we solve f(x) = 0.

– x is known as the fixed point of g(x).

• We solve g(x) = x by computing

until xi+1 converges to x. given with)( 01 xxgx ii

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Fixed Point Iteration – Example032)( 2 xxxf

2

3)(

2

332032

2

1

222

iii

xxgx

xxxxxx

Reason: If x converges, i.e. xi+1 xi

032

2

3

2

3

2

22

1

ii

ii

ii

xx

xx

xx

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ExampleFind root of f(x) = e-x - x = 0.

(Answer: α= 0.56714329)

ixi ex 1putWe

i xi εa (%) εt (%)

0 0 100.0

1 1.000000 100.0 76.3

2 0.367879 171.8 35.1

3 0.692201 46.9 22.1

4 0.500473 38.3 11.8

5 0.606244 17.4 6.89

6 0.545396 11.2 3.83

7 0.579612 5.90 2.20

8 0.560115 3.48 1.24

9 0.571143 1.93 0.705

10 0.564879 1.11 0.399

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Two Curve Graphical Method

Demo

The point, x, where the two curves,

f1(x) = x and

f2(x) = g(x),

intersect is the solution to f(x) = 0.

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Fixed Point Iteration

032)( 2 xxxf

2

3)(

2

3

32

032

2

2

2

2

xxg

xx

xx

xx

2

3)(

2

3

03)2(

0322

xxg

xx

xx

xx

32)(

32

32

0322

2

xxg

xx

xx

xx

• There are infinite ways to construct g(x) from f(x).

For example,

So which one is better?

(ans: x = 3 or -1)

Case a: Case b: Case c:

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aCase

1 ii xx

1. x0 = 4

2. x1 = 3.31662

3. x2 = 3.10375

4. x3 = 3.03439

5. x4 = 3.01144

6. x5 = 3.00381

2

3

bCase

1

ii x

x2

3

cCase2

1

ii

xx

1. x0 = 4

2. x1 = 1.5

3. x2 = -6

4. x3 = -0.375

5. x4 = -1.263158

6. x5 = -0.919355

7. x6 = -1.02762

8. x7 = -0.990876

9. x8 = -1.00305

1. x0 = 4

2. x1 = 6.5

3. x2 = 19.625

4. x3 = 191.070

Converge!

Converge, but slower

Diverge!

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How to choose g(x)?

• Can we know which g(x) would converge to solution before we do the computation?

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Convergence of Fixed Point Iteration

By definition

)2(

)1(

11

ii

ii

x

x

Fixed point iteration

)4()(

and

)3()(

1 ii xgx

g

)6()()()5(in)2(Sub

)5()()()4()3(

1

1

ii

ii

xgg

xggx

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Convergence of Fixed Point Iteration

According to the derivative mean-value theorem, if g(x) and g'(x) are continuous over an interval xi ≤ x ≤ α, there exists a value x = c within the interval such that

)7()()(

)('i

i

x

xggcg

• Therefore, if |g'(c)| < 1, the error decreases with each iteration. If |g'(c)| > 1, the error increase.

• If the derivative is positive, the iterative solution will be monotonic.

• If the derivative is negative, the errors will oscillate.

)()(havewe(6),and(1)From 1 iiii xggandx

iii

i cgcg

)(')(')7(Thus 11

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Demo

(a) |g'(x)| < 1, g'(x) is +ve converge, monotonic

(b) |g'(x)| < 1, g'(x) is -ve converge, oscillate

(c) |g'(x)| > 1, g'(x) is +ve diverge, monotonic

(d) |g'(x)| > 1, g'(x) is -ve diverge, oscillate

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Fixed Point Iteration Impl. (as C function)// x0: Initial guess of the root// es: Acceptable relative percentage error// iter_max: Maximum number of iterations alloweddouble FixedPt(double x0, double es, int iter_max) { double xr = x0; // Estimated root double xr_old; // Keep xr from previous iteration int iter = 0; // Keep track of # of iterations

do { xr_old = xr; xr = g(xr_old); // g(x) has to be supplied if (xr != 0) ea = fabs((xr – xr_old) / xr) * 100;

iter++; } while (ea > es && iter < iter_max);

return xr;}

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The following root finding methods will be introduced:

A. Bracketing MethodsA.1. Bisection MethodA.2. Regula Falsi

B. Open MethodsB.1. Fixed Point IterationB.2. Newton Raphson's MethodB.3. Secant Method

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B.2. Newton-Raphson Method

Use the slope of f(x) to predict the location of the root.

xi+1 is the point where the tangent at xi intersects x-axis.

)('

)(0)()(' 1

1 i

iii

ii

ii xf

xfxx

xx

xfxf

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Newton-Raphson Method

What would happen when f '(α) = 0?

For example, f(x) = (x –1)2 = 0

)('

)(1

i

iii xf

xfxx

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Error Analysis of Newton-Raphson Method

By definition

)2(

)1(

11

ii

ii

x

x

Newton-Raphson method

)3())(('))((')(

))(('))((')(

))((')()('

)(

1

1

1

1

iiiii

iiiii

iiii

i

iii

xxfxxfxf

xxfxxfxf

xxxfxfxf

xfxx

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Suppose α is the true value (i.e., f(α) = 0).Using Taylor's series

Error Analysis of Newton-Raphson Method

221

21

21

2

2

)('2

)("

)('2

)("

))2(and)1(from()(2

)("))(('0

))3(from()(2

)("))(('0

)(2

)("))((')(0

)(2

)("))((')()(

iii

i

iii

iii

iiii

iiii

f

f

xf

cf

cfxf

xcf

xxf

xcf

xxfxf

xcf

xxfxff

When xi and α are very close to each other, c is between xi and α.

The iterative process is said to be of second order.

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The Order of Iterative Process (Definition)

Using an iterative process we get xk+1 from xk and other info.

We have x0, x1, x2, …, xk+1 as the estimation for the root α.

Let δk = α – xk

Then we may observe

The process in such a case is said to be of p-th order.• It is called Superlinear if p > 1.

– It is call quadratic if p = 2

• It is called Linear if p = 1.• It is called Sublinear if p < 1.

)(1

p

kk O

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Error of the Newton-Raphson Method

Each error is approximately proportional to the square of the previous error. This means that the number of correct decimal places roughly doubles with each approximation.

Example: Find the root of f(x) = e-x - x = 0

(Ans: α= 0.56714329)

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i

i

xi

x

ii e

xexx

Error Analysis

56714329.0)("

56714329.11)('

ef

ef

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Error Analysis

2

2

21

18095.0)56714329.1(2

56714329.0)('2

)("

i

i

ii f

f

i xi εt (%) |δi| estimated |δi+1|

0 0 100 0.56714329 0.0582

1 0.500000000 11.8 0.06714329 0.008158

2 0.566311003 0.147 0.0008323 0.000000125

3 0.567143165 0.0000220 0.000000125 2.83x10-15

4 0.567143290 < 10-8

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Newton-Raphson vs. Fixed Point Iteration

Find root of f(x) = e-x - x = 0.

(Answer: α= 0.56714329) ixi ex 1

i xi εa (%) εt (%)

0 0 100.0

1 1.000000 100.0 76.3

2 0.367879 171.8 35.1

3 0.692201 46.9 22.1

4 0.500473 38.3 11.8

5 0.606244 17.4 6.89

6 0.545396 11.2 3.83

7 0.579612 5.90 2.20

8 0.560115 3.48 1.24

9 0.571143 1.93 0.705

10 0.564879 1.11 0.399

i xi εt (%) |δi|

0 0 100 0.56714329

1 0.500000000 11.8 0.06714329

2 0.566311003 0.147 0.0008323

3 0.567143165 0.0000220 0.000000125

4 0.567143290 < 10-8

Newton-Raphson

Fixed Point Iteration with

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Pitfalls of the Newton-Raphson Method

• Sometimes slowiteration xi

0 0.5

1 51.65

2 46.485

3 41.8365

4 37.65285

5 33.8877565

… …

40

41

42

43

1.002316024

1.000023934

1.000000003

1.000000000

1)( 10 xxf

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Pitfalls of the Newton-Raphson Method

Figure (a)

An inflection point (f"(x)=0) at the vicinity of a root causes divergence.

Figure (b)

A local maximum or minimum causes oscillations.

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Pitfalls of the Newton-Raphson Method

Figure (c)

It may jump from one location close to one root to a location that is several roots away.

Figure (d)

A zero slope causes division by zero.

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Overcoming the Pitfalls?• No general convergence criteria for Newton-

Raphson method.

• Convergence depends on function nature and accuracy of initial guess.– A guess that's close to true root is always a better

choice– Good knowledge of the functions or graphical analysis

can help you make good guesses

• Good software should recognize slow convergence or divergence.– At the end of computation, the final root estimate

should always be substituted into the original function to verify the solution.

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Other Facts• Newton-Rahpson method converges quadratically

(when it converges).– Except when the root is a multiple roots

• When the initial guess is close to the root, Newton-Rahpson method usually converges.

– To improve the chance of convergence, we could use a bracketing method to locate the initial value for the Newton-Raphson method.

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Summary• Differences between bracketing methods and open

methods for locating roots– Guarantee of convergence?– Performance?

• Convergence criteria for fixed-point iteration method

• Rate of convergence– Linear, quadratic, super-linear, sublinear

• Understand what conditions make Newton-Raphson method converges quickly or diverges