1 roots of equations open methods (part 1) fixed point iteration & newton-raphson methods
Post on 20-Dec-2015
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TRANSCRIPT
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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!
4
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:
10
32
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