Lesson 24 - Learning Goals

24.1 Learn why Numerical Integration is useful

24.2 Learn several different Numerical Integration Techniques

24.3 Learn how to compare different Numerical Integration algorithms

Numerical Integration

Introduction

• Given a function y=f(x)

• Want area under curve between x=a and x=b

• Assume we cannot obtain an exact solution, or perhaps f(x) is unknown, and we only have data points (xi,yi).

• Main idea: Break area under curve into n panels of width h: A = Ai , h = (b-a)/n
  

• How to calculate Ai ?

• Many methods of approximation

• All estimates improve with larger n, as h0 .
  

Midpoint Rule

• Use rectangular panels (i.e. assume f(x) is constant and equal to y* between xi-1 and xi )

• Three possibilities:

• Left endpoint: y* = yi-1 = f(xi-1)
• Right endpoint: y* = yi = f(xi) = f(xi-1 + h)
• Midpoint: y* = yi - ½ = f( (xi-1 + xi)/2 ) = f(xi-1 + h/2)
• On average, midpoint looks like best guess
  
  

Midpoint Rule Algorithm

We wish to integrate y=f(x) from x=a to x=b using n rectangular panels of equal width.

h = (b-a) /n
x0 = a
xi = xi-1 + h
Ai = h* f( (xi-1 + xi)/2 )
A = Sum of Ai (i=1 -> i=n)
A = h* [Sum of f( (xi-1 + xi) /2 )] (i=1 -> i=n)

REAL FUNCTION MidPt(x0, x1, N)
! Estimate the area under F between x0 and x1
! using the Midpoint Rule with N panels.

   REAL x0, x1, x, h, Sum
   INTEGER N

   h = (x1 - x0) / N
   Sum = 0.0

   ! Add area of each panel, assuming unit width
   ! and multiplying by the true width h later.

   x = x0+h/2.0 ! Initial start is middle of first panel

   L1: DO k= 1,N
      Sum = Sum + F(x)
      x = x + h
   END DO L1

   MidPt = h*Sum

RETURN

C Version

double midpoint(double x0, double x1, int n)
{
   double h, sum;

   /* Calculate panel width h; initialize Sum. */

   h= (x1 - x0) / n;
   x0= x0 + h/2;
   sum= 0;

/* Add area of each panel, multiplying
by the true width h only once later. */

   do {
      sum= sum + f(x0);
      x0= x0 + h;
   }while(x0 x1);
   return(sum * h);
}

Trapezoidal Rule

Try taking average of the expressions for rectangular Ai, with f evaluated at left and right endpoints

Ai = [ h f(xi-1) + h f(xi) ]

Ai = h/2 [ f(xi-1) + f(xi) ]

Approximate Ai, as the area of trapezoid abcd.

Trapezoidal method appears to take 2 function evaluations per panel, but

Ai + Ai+1 = h/2 [ f(xi-1) + f(xi) ] + h/2 [ f(xi) + f(xi+1) ]

Ai + Ai+1 = h/2 [ f(xi-1) + 2f(xi) + f(xi+1) ]

For n panels, need only n+1 evaluations of f.

Trapezoidal Rule Algorithm

We wish to integrate y=f(x) from x=a to x=b using n trapezoidal panels of equal width.

h= (b-a)/n

x0 = a

xn = b

xi = xi-1 + h

Ai = h/2 [ (f(xi-1) + f(xi) ]

A Ai

n - 1

A h [ ( f(x0) + f(xn) ) + f(xi) ]

i = 1

Simpson's Rule

In Trapezoidal rule, successive points (xi, yi) were joined with straight lines

Now, fit a parabola to 3 points (xi-2, yi-2), (xi-1, yi-1), and (xi, yi) for better accuracy

One panel (width 2h) now includes 3 points

h = (b-a)/n

b-a = nh

n must therefore be even

From geometry or Taylor series expansion, we find

Ai = h/3 [ f(xi-2) + 4f(xi-1) + f(xi) ] , for i = 2,4,6,…,n

Once more, combine expressions for adjacent panels and simplify.

Ai + Ai+2 = h/3 [ f(xi-2) + 4f(xi-1) + 2f(xi) + 4f(xi+1) + f(xi+2) ]

For n panels, again need only n+1 evaluations of f.

Simpson's Rule Algorithm

We wish to integrate y=f(x) from x=a to x=b using n/2 double parabolic panels of equal width.

h = (b-a)/n
x0= a
xn= b
xi= xi-1 + h
Ai = h/3 [ f(xi-2) + 4f(xi-1) + f(xi) ]
A Ai

n - 1 n - 2

A h/3 [ f(x0) + f(xn) + 4 f(xi) + 2 f(xi) ]

i = 1 i = 2

(i odd) (i even)

Adaptive Integration Procedure

- Arbitrary precision

Advanced, recursive adaptive integration technique

/* more sophisticated adaptive integration method

using recursion */

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