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Lesson 4: Numerical Integration

Estimated time: 35-45 minutes

Learning Objectives

By the end of this lesson, you will be able to:

Why Numerical Integration?

Many important integrals cannot be expressed in terms of elementary functions. Examples include ∫ e−x² dx (the Gaussian integral), ∫ sin(x²) dx, and ∫ √(1 + x4) dx. For these, we need numerical methods to approximate the value.

Even when an antiderivative exists, numerical methods can be faster for one-time computations or when the integrand is given as a table of data points.

The Trapezoidal Rule

Instead of approximating the area with rectangles (Riemann sums), the Trapezoidal Rule uses trapezoids, which fit the curve more closely.

Trapezoidal Rule

With n subintervals and Δx = (b − a)/n:

Tn = (Δx/2)[f(x0) + 2f(x1) + 2f(x2) + ... + 2f(xn−1) + f(xn)]

Note the pattern: first and last values get weight 1; all interior values get weight 2.

Example 1: Trapezoidal Rule

Approximate ∫02 e−x² dx with n = 4.

Step 1: Δx = 2/4 = 0.5. Points: x0=0, x1=0.5, x2=1, x3=1.5, x4=2.

Step 2: Evaluate: f(0) = 1, f(0.5) = 0.7788, f(1) = 0.3679, f(1.5) = 0.1054, f(2) = 0.0183.

Step 3: T4 = (0.5/2)[1 + 2(0.7788) + 2(0.3679) + 2(0.1054) + 0.0183]

= 0.25[1 + 1.5576 + 0.7358 + 0.2108 + 0.0183] = 0.25(3.5225) ≈ 0.8806.

Simpson's Rule

Simpson's Rule approximates the integrand with parabolas instead of straight lines, yielding much better accuracy. It requires n to be even.

Simpson's Rule

With n subintervals (n even) and Δx = (b − a)/n:

Sn = (Δx/3)[f(x0) + 4f(x1) + 2f(x2) + 4f(x3) + 2f(x4) + ... + 4f(xn−1) + f(xn)]

Pattern: 1, 4, 2, 4, 2, ..., 4, 1.

Example 2: Simpson's Rule

Approximate ∫02 e−x² dx with n = 4 (same as Example 1).

S4 = (0.5/3)[1 + 4(0.7788) + 2(0.3679) + 4(0.1054) + 0.0183]

= (1/6)[1 + 3.1152 + 0.7358 + 0.4216 + 0.0183] = (1/6)(5.2909) ≈ 0.8818.

(The true value is approximately 0.8821. Simpson's is already very close with just n = 4!)

Error Bounds

Error Bound for Trapezoidal Rule

|ET| ≤ M(b − a)³ / (12n²), where M = max |f''(x)| on [a, b].

Error decreases as O(1/n²): doubling n cuts the error by 4.

Error Bound for Simpson's Rule

|ES| ≤ K(b − a)5 / (180n4), where K = max |f(4)(x)| on [a, b].

Error decreases as O(1/n4): doubling n cuts the error by 16!

Example 3: Comparing Accuracy

For ∫01 x³ dx (exact answer = 1/4), compare T4 and S4.

Δx = 0.25. Points: 0, 0.25, 0.5, 0.75, 1. Values: 0, 0.0156, 0.125, 0.4219, 1.

T4 = (0.25/2)[0 + 2(0.0156) + 2(0.125) + 2(0.4219) + 1] = 0.125(2.1250) = 0.2656. Error: 0.0156.

S4 = (0.25/3)[0 + 4(0.0156) + 2(0.125) + 4(0.4219) + 1] = (1/12)(3.0000) = 0.2500. Error: 0!

Simpson's Rule gives the exact answer for cubics (because f(4) = 0 for polynomials of degree 3 or less).

Working with Data Tables

Numerical methods are essential when you only have data points (no formula).

Example 4: From a Data Table

Estimate ∫06 f(x) dx using Simpson's Rule given:

x0123456
f(x)3546752

n = 6 (even), Δx = 1.

S6 = (1/3)[3 + 4(5) + 2(4) + 4(6) + 2(7) + 4(5) + 2] = (1/3)[3 + 20 + 8 + 24 + 14 + 20 + 2] = 91/3 ≈ 30.33.

Comparison and Guidelines

MethodAccuracyRequirementBest For
TrapezoidalO(1/n²)Any nQuick estimates, odd n
Simpson'sO(1/n4)n must be evenBest accuracy per function evaluation

Rule of thumb: Simpson's Rule with n = 10 often matches the Trapezoidal Rule with n = 100 in terms of accuracy.

Key Takeaways

Check Your Understanding

1. Use the Trapezoidal Rule with n = 4 to approximate ∫13 (1/x) dx.

Answer: Δx = 0.5. Points: 1, 1.5, 2, 2.5, 3. Values: 1, 0.667, 0.5, 0.4, 0.333. T4 = 0.25[1 + 2(0.667) + 2(0.5) + 2(0.4) + 0.333] = 0.25(4.467) ≈ 1.117. (Exact: ln 3 ≈ 1.099.)

2. Use Simpson's Rule with n = 4 for the same integral.

Answer: S4 = (0.5/3)[1 + 4(0.667) + 2(0.5) + 4(0.4) + 0.333] = (1/6)[1 + 2.667 + 1 + 1.6 + 0.333] = (1/6)(6.600) = 1.100. Much closer to ln 3!

3. If you need the Trapezoidal Rule error to be less than 0.001 for ∫01 ex dx, how many subintervals do you need?

Answer: f''(x) = ex, max on [0,1] is e. Error ≤ e(1)³/(12n²) < 0.001. n² > e/0.012 ≈ 226.5. n > 15.05, so n = 16.

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Practice Problems

Test all your integration techniques with 10 problems.

Practice Problems

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