TECHNIQUES OF INTEGRATION

1. 7 TECHNIQUES OF INTEGRATION

2. TECHNIQUES OF INTEGRATION • There are two situations in which it is impossible to find the exact value of a definite integral.

3. TECHNIQUES OF INTEGRATION • The first situation arises from the fact that, in order to evaluate using the Fundamental Theorem of Calculus (FTC), we need to know an antiderivative of f.

4. TECHNIQUES OF INTEGRATION • However, sometimes, it is difficult, or even impossible, to find an antiderivative (Section 7.5). • For example, it is impossible to evaluate the following integrals exactly:

5. TECHNIQUES OF INTEGRATION • The second situation arises when the function is determined from a scientific experiment through instrument readings or collected data. • There may be no formula for the function (as we will see in Example 5).

6. TECHNIQUES OF INTEGRATION • In both cases, we need to find approximate values of definite integrals.

7. TECHNIQUES OF INTEGRATION 7.7Approximate Integration In this section, we will learn: How to find approximate values of definite integrals.

8. APPROXIMATE INTEGRATION • We already know one method for approximate integration. • Recall that the definite integral is defined as a limit of Riemann sums. • So, any Riemann sum could be used as an approximation to the integral.

9. APPROXIMATE INTEGRATION • If we divide [a, b] into n subintervals of equal length ∆x = (b – a)/n, we have: • where xi* is any point in the i th subinterval [xi -1, xi].

10. Ln APPROXIMATION Equation 1 • If xi* is chosen to be the left endpoint of the interval, then xi* = xi -1 and we have: • The approximation Ln is called the left endpoint approximation.

11. Ln APPROXIMATION • If f(x) ≥ 0, the integral represents an area and Equation 1 represents an approximation of this area by the rectangles shown here.

12. Rn APPROXIMATION Equation 2 • If we choose xi* to be the right endpoint, xi* = xi and we have: • The approximation Rniscalled right endpoint approximation.

13. APPROXIMATE INTEGRATION • In Section 5.2, we also considered the case where xi* is chosen to be the midpoint of the subinterval [xi -1, xi].

14. Mn APPROXIMATION • The figure shows the midpoint approximation Mn.

15. Mn APPROXIMATION • Mn appears to be better than either Ln or Rn.

16. THE MIDPOINT RULE • where and

17. TRAPEZOIDAL RULE • Another approximation—called the Trapezoidal Rule—results from averaging the approximations in Equations 1 and 2, as follows.

18. TRAPEZOIDAL RULE

19. THE TRAPEZOIDAL RULE • where ∆x = (b – a)/n and xi = a + i ∆x

20. TRAPEZOIDAL RULE • The reason for the name can be seen from the figure, which illustrates the case f(x) ≥ 0.

21. TRAPEZOIDAL RULE • The area of the trapezoid that lies above the i th subinterval is: • If we add the areas of all these trapezoids,we get the right side of the Trapezoidal Rule.

22. APPROXIMATE INTEGRATION Example 1 • Approximate the integral with n = 5, using: a. Trapezoidal Rule • b. Midpoint Rule

23. APPROXIMATE INTEGRATION Example 1 a • With n = 5, a = 1 and b = 2, we have: ∆x = (2 – 1)/5 = 0.2 • So, the Trapezoidal Rule gives:

24. APPROXIMATE INTEGRATION Example 1 a • The approximation is illustrated here.

25. APPROXIMATE INTEGRATION Example 1 b • The midpoints of the five subintervals are: 1.1, 1.3, 1.5, 1.7, 1.9

26. APPROXIMATE INTEGRATION Example 1 b • So, the Midpoint Rule gives:

27. APPROXIMATE INTEGRATION • In Example 1, we deliberately chose an integral whose value can be computed explicitly so that we can see how accurate the Trapezoidal and Midpoint Rules are. • By the FTC,

28. APPROXIMATION ERROR • The error in using an approximation is defined as the amount that needs to be added to the approximation to make it exact.

29. APPROXIMATE INTEGRATION • From the values in Example 1, we see that the errors in the Trapezoidal and Midpoint Rule approximations for n = 5 are:ET≈ – 0.002488 EM≈ 0.001239

30. APPROXIMATE INTEGRATION • In general, we have:

31. APPROXIMATE INTEGRATION • The tables show the results of calculations similar to those in Example 1. • However, these are for n = 5, 10, and 20 and for the left and right endpoint approximations and also the Trapezoidal and Midpoint Rules.

32. APPROXIMATE INTEGRATION • We can make several observations from these tables.

33. OBSERVATION 1 • In all the methods. we get more accurate approximations when we increase n. • However, very large values of n result in so many arithmetic operations that we have to beware of accumulated round-off error.

34. OBSERVATION 2 • The errors in the left and right endpoint approximations are: • Opposite in sign • Appear to decrease by a factor of about 2 when we double the value of n

35. OBSERVATION 3 • The Trapezoidal and Midpoint Rules are much more accurate than the endpoint approximations.

36. OBSERVATION 4 • The errors in the Trapezoidal and Midpoint Rules are: • Opposite in sign • Appear to decrease by a factor of about 4 when we double the value of n

37. OBSERVATION 5 • The size of the error in the Midpoint Rule is about half that in the Trapezoidal Rule.

38. MIDPOINT RULE VS. TRAPEZOIDAL RULE • The figure shows why we can usually expect the Midpoint Rule to be more accurate than the Trapezoidal Rule.

39. MIDPOINT RULE VS. TRAPEZOIDAL RULE • The area of a typical rectangle in the Midpoint Rule is the same as the area of the trapezoid ABCD whose upper side is tangent to the graph at P.

40. MIDPOINT RULE VS. TRAPEZOIDAL RULE • The area of this trapezoid is closer to the area under the graph than is the area of that used in the Trapezoidal Rule.

41. MIDPOINT RULE VS. TRAPEZOIDAL RULE • The midpoint error (shaded red) is smaller than the trapezoidal error (shaded blue).

42. OBSERVATIONS • These observations are corroborated in the following error estimates—which are proved in books on numerical analysis.

43. OBSERVATIONS • Notice that Observation 4 corresponds to the n2 in each denominator because: (2n)2 = 4n2

44. APPROXIMATE INTEGRATION • That the estimates depend on the size of the second derivative is not surprising if you look at the figure. • f’’(x) measures how much the graph is curved. • Recall that f’’(x) measures how fast the slope of y = f(x) changes.

45. ERROR BOUNDS Estimate 3 • Suppose | f’’(x) | ≤ Kfor a≤ x ≤ b. • If ET and EM are the errors in the Trapezoidal and Midpoint Rules, then

46. ERROR BOUNDS • Let’s apply this error estimate to the Trapezoidal Rule approximation in Example 1. • If f(x) = 1/x, then f’(x) = -1/x2 and f’’(x) = 2/x3. • As 1 ≤ x ≤ 2, we have 1/x ≤ 1;so,

47. ERROR BOUNDS • So, taking K = 2, a = 1, b = 2, and n = 5 in the error estimate (3), we see:

48. ERROR BOUNDS • Comparing this estimate with the actual error of about 0.002488, we see that it can happen that the actual error is substantially less than the upper bound for the error given by (3).

49. ERROR ESTIMATES Example 2 • How large should we take n in order to guarantee that the Trapezoidal and Midpoint Rule approximations for are accurate to within 0.0001?

50. ERROR ESTIMATES Example 2 • We saw in the preceding calculation that | f’’(x) | ≤ 2 for 1 ≤ x ≤ 2 • So, we can take K = 2, a = 1, and b = 2 in (3).