Thoughts on Numerical Integration (Part 16): Midpoint rule and local rate of convergence

Numerical integration is a standard topic in first-semester calculus. From time to time, I have received questions from students on various aspects of this topic, including:
  • Why is numerical integration necessary in the first place?
  • Where do these formulas come from (especially Simpson’s Rule)?
  • How can I do all of these formulas quickly?
  • Is there a reason why the Midpoint Rule is better than the Trapezoid Rule?
  • Is there a reason why both the Midpoint Rule and the Trapezoid Rule converge quadratically?
  • Is there a reason why Simpson’s Rule converges like the fourth power of the number of subintervals?
In this series, I hope to answer these questions. While these are standard questions in a introductory college course in numerical analysis, and full and rigorous proofs can be found on Wikipedia and Mathworld, I will approach these questions from the point of view of a bright student who is currently enrolled in calculus and hasn’t yet taken real analysis or numerical analysis.
In this post, we will perform an error analysis for the Midpoint Rule

\int_a^b f(x) \, dx \approx h \left[f(c_1) + f(c_2) + \dots + f(c_n) \right] \equiv M_n

where n is the number of subintervals and h = (b-a)/n is the width of each subinterval, so that x_k = x_0 + kh. Also, c_i = (x_{i-1} + x_i)/2 is the midpoint of the ith subinterval.
As noted above, a true exploration of error analysis requires the generalized mean-value theorem, which perhaps a bit much for a talented high school student learning about this technique for the first time. That said, the ideas behind the proof are accessible to high school students, using only ideas from the secondary curriculum (especially the Binomial Theorem), if we restrict our attention to the special case f(x) = x^k, where k \ge 5 is a positive integer.

For this special case, the true area under the curve f(x) = x^k on the subinterval [x_i, x_i +h] will be

\displaystyle \int_{x_i}^{x_i+h} x^k \, dx = \frac{1}{k+1} \left[ (x_i+h)^{k+1} - x_i^{k+1} \right]

= \displaystyle \frac{1}{k+1} \left[x_i^{k+1} + {k+1 \choose 1} x_i^k h + {k+1 \choose 2} x_i^{k-1} h^2 + {k+1 \choose 3} x_i^{k-2} h^3 + {k+1 \choose 4} x_i^{k-3} h^4+ {k+1 \choose 5} x_i^{k-4} h^5+ O(h^6) - x_i^{k+1} \right]

= \displaystyle \frac{1}{k+1} \bigg[ (k+1) x_i^k h + \frac{(k+1)k}{2} x_i^{k-1} h^2 + \frac{(k+1)k(k-1)}{6} x_i^{k-2} h^3+ \frac{(k+1)k(k-1)(k-2)}{24} x_i^{k-3} h^4

+ \displaystyle \frac{(k+1)k(k-1)(k-2)(k-3)}{120} x_i^{k-4} h^5 \bigg] + O(h^6)

= x_i^k h + \displaystyle \frac{k}{2} x_i^{k-1} h^2 + \frac{k(k-1)}{6} x_i^{k-2} h^3 + \frac{k(k-1)(k-2)}{24} x_i^{k-3} h^4 + \frac{k(k-1)(k-2)(k-3)}{120} x_i^{k-4} h^5 + O(h^6)

In the above, the shorthand O(h^6) can be formally defined, but here we’ll just take it to mean “terms that have a factor of h^6 or higher that we’re too lazy to write out.” Since h is supposed to be a small number, these terms will small in magnitude and thus can be safely ignored. I wrote the above formula to include terms up to and including h^5 because I’ll need this later in this series of posts. For now, looking only at the Midpoint Rule, it will suffice to write this integral as

\displaystyle \int_{x_i}^{x_i+h} x^k \, dx =x_i^k h + \displaystyle \frac{k}{2} x_i^{k-1} h^2 + \frac{k(k-1)}{6} x_i^{k-2} h^3 + O(h^4).

Using the midpoint of the subinterval, the left-endpoint approximation of \displaystyle \int_{x_i}^{x_i+h} x^k \, dx is \displaystyle \left(x_i+ \frac{h}{2} \right)^k h. Using the Binomial Theorem, this expands as

 x_i^k h + \displaystyle {k \choose 1} x_i^{k-1} \frac{h^2}{2}  + {k \choose 2} x_i^{k-2} \frac{h^3}{4} + {k \choose 3} x_i^{k-3} \frac{h^4}{8}  + {k \choose 4} x_i^{k-4} \frac{h^5}{16} + O(h^6)

 = x_i^k h + \displaystyle \frac{k}{2} x_i^{k-1} h^2  + \frac{k(k-1)}{8} x_i^{k-2} h^3 + \frac{k(k-1)(k-2)}{48} x_i^{k-3} h^4

\displaystyle + \frac{k(k-1)(k-2)(k-3)}{384} x_i^{k-4} h^5 + O(h^6)

Once again, this is a little bit overkill for the present purposes, but we’ll need this formula later in this series of posts. Truncating somewhat earlier, we find that the Midpoint Rule for this subinterval gives

x_i^k h + \displaystyle \frac{k}{2} x_i^{k-1} h^2  + \displaystyle \frac{k(k-1)}{8} x_i^{k-2} h^3 + O(h^4)

Subtracting from the actual integral, the error in this approximation will be equal to

\displaystyle x_i^k h + \frac{k}{2} x_i^{k-1} h^2 + \frac{k(k-1)}{6} x_i^{k-2} h^3 - x_i^k h - \frac{k}{2} x_i^{k-1} h^2  - \frac{k(k-1)}{8} x_i^{k-2} h^3 + O(h^4)

= \displaystyle \frac{k(k-1)}{24} x_i^{k-2} h^3 + O(h^4)

In other words, unlike the left-endpoint and right-endpoint approximations, both of the first two terms x_i^k h and \displaystyle \frac{k}{2} x_i^{k-1} h^2 cancel perfectly, leaving us with a local error on the order of h^3.
The logic for determining the global error is much the same as what we used earlier for the left-endpoint rule. The total error when approximating \displaystyle \int_a^b x^k \, dx = \int_{x_0}^{x_n} x^k \, dx will be the sum of the errors for the integrals over [x_0,x_1], [x_1,x_2], through [x_{n-1},x_n]. Therefore, the total error will be

E \approx \displaystyle \frac{k(k-1)}{24} \left(x_0^{k-2} + x_1^{k-2} + \dots + x_{n-1}^{k-2} \right) h^3.

So that this formula doesn’t appear completely mystical, this actually matches the numerical observations that we made earlier. The figure below shows the left-endpoint approximations to \displaystyle \int_1^2 x^9 \, dx for different numbers of subintervals. If we take n = 100 and h = 0.01, then the error should be approximately equal to

\displaystyle \frac{9 \times 8}{24} \left(1^7 + 1.01^7 + \dots + 1.99^7 \right) (0.01)^3 \approx 0.0093731,

which, as expected, is close to the actual error of 102.3 - 102.2904379 \approx 0.00956211.
Let y_i = x_i^{k-2}, so that the error becomes

E \approx \displaystyle \frac{k(k-1)}{24} \left(y_0 + y_1 + \dots + y_{n-1} \right) h^3 + O(h^4) = \displaystyle \frac{k(k-1)}{24} \overline{y} n h^3,

where \overline{y} = (y_0 + y_1 + \dots + y_{n-1})/n is the average of the y_i. Clearly, this average is somewhere between the smallest and the largest of the y_i. Since y = x^{k-2} is a continuous function, that means that there must be some value of x_* between x_0 and x_{k-1} — and therefore between a and b — so that x_*^{k-2} = \overline{y} by the Intermediate Value Theorem. We conclude that the error can be written as

E \approx \displaystyle \frac{k(k-1)}{24} x_*^{k-2} nh^3,

Finally, since h is the length of one subinterval, we see that nh = b-a is the total length of the interval [a,b]. Therefore,

E \approx \displaystyle \frac{k(k-1)}{24} x_*^{k-2} (b-a)h^2 \equiv ch^2,

where the constant c is determined by a, b, and k. In other words, for the special case f(x) = x^k, we have established that the error from the Midpoint Rule is approximately quadratic in h — without resorting to the generalized mean-value theorem and confirming the numerical observations we made earlier.

One thought on “Thoughts on Numerical Integration (Part 16): Midpoint rule and local rate of convergence

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.