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Approximate Integration: The Trapezoidal Rule

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Approximate Integration: The Trapezoidal Rule. Claus Schubert. May 25, 2000 ... By refining the partition, we obtain better approximations. ... – PowerPoint PPT presentation

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Title: Approximate Integration: The Trapezoidal Rule


1
Approximate IntegrationThe Trapezoidal Rule
  • Claus Schubert
  • May 25, 2000

2
Why Approximate Integration?
  • Cant always find an antiderivative
  • Example
  • Dont always know the function

3
First Approach Riemann Sums
  • Use left or right Riemann sums to approximate the
    integral.
  • Left Riemann sum
  • Dx length of the n subintervals
  • xi endpoints of the subintervals

4
Left Riemann Sums
By refining the partition, we obtain better
approximations.
Ln is the sum of all the inscribed rectangles
starting at the left endpoints. It is called a
left endpoint approximation.
5
Right Riemann Sums
Rn is the sum of all the inscribed rectangles
starting at the right endpoints. It is called a
right endpoint approximation.
6
Left and Right Endpoint Approximations
  • Observations
  • If Ln underestimates, then Rn overestimates, and
    vice versa
  • Approximations get better if we increase n
  • Take the average of both approximations
  • Idea for improvement

7
Trapezoidal Approximation
8
Trapezoidal Approximation
Ln
Rn
9
Trapezoidal Approximation
10
Trapezoidal Approximation
11
An Example
  • As an example, let us look at .

12
An Example
13
Error bounds
  • Question
  • How accurate is the trapezoidal approximation?

14
Error bounds An Example
  • In our previous example, how large should n be so
    that the error is less than 0.00001 ?

15
Error bounds An Example
16
Lets Wrap Up
  • Approximations are useful if the function cannot
    be integrated or no function is given to begin
    with.
  • Left and right endpoint approximations are too
    inaccurate, so take their average.
  • The trapezoidal approximation is much more
    accurate than the left/right approximations, but
    better approximations exist (midpoint, Simpsons
    etc.)
  • You need a computer to find approximations with
    large n - or you need to get a life!!!
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