Chap. 11 Numerical Differentiation and Integration Computer Theory and Formal Methods LAB HWANG...

Chap. 11Numerical Differentiation and Integration

Computer Theory and Formal Methods LAB HWANG Dae-Yon ([email protected])

SIM Jae-Hwan ([email protected])YANG Jin-Seok ([email protected])

May. 18, 2005

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Computer Theory and Formal Methods Lab.

Contents

11.1 DIFFERENTIATION 11.1.1 First Derivatives 11.1.2 Higher Derivatives 11.1.3 Richardson Extrapolation

11.2 BASIC NUMERICAL INTEGRATION 11.2.1 Trapezoid Rule 11.2.2 Simpson Rule 11.2.3 Midpoint Rule 11.2.4 Other Newton-Cotes Open Formulas

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11.1 DIFFERENTIATION

11.1.1 First Derivatives

Forward difference formula

Backward difference formula

Central difference formula

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Example 11.1

Forward, Backward and Central DifferencesData Points(x0, y0) = (1,2) (x1, y1) = (2,4) (x2, y2) = (3,8) (x3, y3) = (4,16) (x4, y4) = (5,32)

Forward

Backward

Central

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Three-point difference formula

Three-point forward difference formula

Three-point backward difference formula

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Discussion

Taylor polynomial

Forward : h = xi+1 – xi

Backward : h = xi-1 – xi

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Discussion (cont’)

Central : h = xi+1 – xi = xi – xi-1

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General Three-Point Formula

Based on Lagrange interpolation polynomial(x1 , y1) , (x2 , y2) , (x3 , y3)

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General Three-Point Formula(2)

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If h = xi+1 – xi = xi – xi-1

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If h = xi+1 – xi = xi – xi-1

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11.1.2 Higher Derivative

Formula for higher derivative can be founded by Differentiating the interpolating polynomial repeatedly. Using Taylor expansions.

For example, Three equally spaced abscissas xi-1,xi, xi+1 Formula for the second derivative is

1 12

1( ) [ ( ) 2 ( ) ( )]i i i if x f x f x f x

h with truncation error O(h2)

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11.1.2 Higher Derivative- Derivation of Second-Derivative Formula

If we assume that fourth derivative is continuous on [x-h, x+h], we can

write the error term as for some point

From the Taylor polynomial

2 3 4(4)

2( ) ( ) ( ) ( ) ( ) ( )2! 3! 4!

h h hf x h f x hf x f x f x f

2 3 4(4)

1( ) ( ) ( ) ( ) ( ) ( )2! 3! 4!

h h hf x h f x hf x f x f x f

where

1x x h and

2x h x . adding gives4

2 (4) (4)1 2( ) ( ) 2 ( ) ( ) [ ( ) ( )]

4!

hf x h f x h f x h f x f f

or 2

1( ) [ ( ) 2 ( ) ( )]f x f x h f x f x h

h with truncation error O(h4)

2(4) ( )

12

hf x h x h

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11.1.2 Higher Derivative- Derivation of Second-Derivative Formula

Table 11.1 Centered difference formulas, all O(h2)

1 1

1( ) [ ( ) ( )]

2i i if x f x f xh

1 12

1( ) [ ( ) 2 ( ) ( )]i i i if x f x f x f x

h

2 1 1 23

1( ) [ ( ) 2 ( ) 2 ( ) ( )]

2i i i i if x f x f x f x f xh

Example 11.3 Second DerivativeEstimate the second derivative at x2 = 3, using point (x1, y1) = (2, 4),

(x2, y2) = (3, 8), and (x3, y3) = (4, 6); for this example, h=1

(4)2 1 1 24

1( ) [ ( ) 4 ( ) 6 ( ) 4 ( ) ( )]i i i i i if x f x f x f x f x f x

h

(3) [ (4) 2 (3) (2)] [16 2(8) 4] 4f f f f

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11.1.2 Higher Derivative- Partial Derivatives

Partial derivative of a function of two variables General point as (xi, yj ) The value of the function u(x, y) at that point as u i, j

The spacing in the x and y directions is the same, h Using subscripts to indicate partial differentiation

1, 1,

1 1[ ]

2 2x i j i ju u uh h -1 0 1

i-1 i i+1

j

1, , 1,2 2

1 1[ 2 ]xx i j i j i ju u u u

h h 1 -2 1

i-1 i i+1

j

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11.1.2 Higher Derivative- Partial Derivatives

For the mixed second partial derivative and higher derivatives, the schematic form is especially convenient.The Laplacian operatorThe bi-harmonic operator

2xx yyu u u

4 2xxxx xxyy yyyyu u u u

2

1

4xyu h 2

2

1u

h

44

1u

h

-1 0 1

0

-1

00

1 0

1

1-41

1i-1 i i+1

j-1

j

j+1

i-1 i i+1j-1

j

j+1

1 -8 20 -8 1

2 -8 2

1

2 -8 2

1

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11.1.3 Richardson Extrapolation

Method of improving the accuracy of a low order approximation formula A(h) whose error can be expressed as

To apply Richardson extrapolation, we form approximations to A separately using the step size h and h/2

2 42 4( ) ...A A h a h a h

4 ( / 2) ( )

3

A h A hA

To continue the extrapolation process, consider4 6 8

4 6 8( ) ...A B h b h b h b h Where B(h) is simply the extrapolated approximation to A, using step sizes h/2 and h/4, this would correspond to B(h/2). we get

16 ( / 2) ( )( )

15

B h B hC h

which has error O(h6)

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11.1.3 Richardson Extrapolation

The central difference formula can be written as2

41( ) ( ) [ ( ) ( )] ( ) ( )

2 6

hD h f x f x h f x h f x O h

h

We can also find f’(x) using one-half the previous value of h2

41( / 2) ( ) [ ( / 2) ( / 2)] ( ) ( )

24

hD h f x f x h f x h f x O h

h

Since the coefficient of the h2 term does not change , the two estimates can be combined to give

4 ( / 2) ( )

3

D h D hD

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11.1.3 Richardson Extrapolation- Example 11.4

Improved Estimate of the Derivative From Example 11.1 h=2 The approximation to f’(x2) is based on D(h)=7.5 and D(h/2)

4(6) 7.516.5 5.5

3D

The data in the example are points on the curve f(x)=2x. The actual value of f’(x) is (ln2)2x, which gives

(3) 5.54f

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11.1.3 Richardson Extrapolation- Discussion

Richardson extrapolation Forms a linear combination of approximation A(h) and A(h/2) Dominant error term, which depends on h2, cancels

2 42 4( ) ...A A h a h a h

2 4

2 4( / 2) ...4 16

h hA A h a a or

42

2 44 4 ( / 2) ...4

hA A h a h a

(11.2)

(11.3)

Subtracting eq. (11.2) from eq. (11.3) gives

43 4 ( / 2) ( ) ( )A A h A h O h or 414 ( / 2) ( ) ( )

3A A h A h O h

O(h2) O(h4)

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11.1.3 Richardson Extrapolation- Discussion

To continue the extrapolation, we write4 6 8

4 6 8( ) ...A B h b h b h b h where B(h) is simply the extrapolated approximation to A, using step size h, h/2.using step size h/2 and h/4, this would corresponding to B(h/2).

4 6 8 84 6 8( / 2) ( /16) ( / 64) ( / 2 ) ...A B h b h b h b h

Therefore,6 8

6 815 16 ( / 2) ( )A B h B h c h c h Define the second level extrapolated approximation to A as

16 ( / 2) ( )( )

15

B h B hC h

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Contents

Trapezoid Rule Example 11.5 Discussion about Trapezoid Rule

Simpson Rule Example 11.6 Example 11.7 Discussion about Simpson Rule

Midpoint Rule Example 11.8

Other Newton-Cotes Open Formulas

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Overview (1/2)

Numerical integration rules are very important. Functions may not have exact formulas for their antiderivatives (inde

finite integrals). An exact formula for the antiderivative dose exist, it may be difficult

to find.

A numerical integration rule has the form

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Overview (2/2)

Two basic types of Newton-Cotes formulas. “Closed” formulas : the endpoints values are used.

Trapezoid Rule Simpson Rule

“Open” formulas : the endpoints are not used. Midpoint Rule

Each of these formulas can be derived by approximating the function to be integrated by its Lagrange interpolating polynomial.

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Trapezoid Rule

This rule approximates the curve by the straight line that passes through the points (a,f(a)) and (b, f(b)).

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Trapezoid Rule – Example 11.5

Integral of Using the Trapezoid Rule

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Trapezoid Rule – Discussion (1/2)

It derived from the Lagrange form of linear interpolation of f(x) using the endpoints of the interval of integration.

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Trapezoid Rule – Discussion (2/2)

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Simpson Rule

Approximating the function to be integrated by a quadratic polynomial leads to the basic Simpon Rule:

The approximate integral is given by

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Simpson Rule – Example 11.6

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Simpson Rule – Example 11.7

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Simpson Rule – Discussion (1/2)

Simpson’s rule is found by integrating the Lagrange interpolating polynomial for f(x).

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Simpson Rule – Discussion (2/2)

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Midpoint Rule

If we use only function evaluations at points within the interval, the simplest formula is the midpoint rule.

This formula uses only one function evaluation (so n = 1),at the midpoint of the interval, xm=(a+b)/2.

Interpolating the function by the constant value f(xm) :

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Midpoint Rule – Example 11.8

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Other Newton-Cotes Open Formulas (1/2)

Using two function evaluations

Trapezoid Rule

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Other Newton-Cotes Open Formulas (2/2)

Using three function evaluations

Simpson Rule

Chap. 11 Numerical Differentiation and Integration Computer Theory and Formal Methods LAB HWANG...

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