8.2 Elements of numerical integration
To approximate \(\int _a^bf(x)\,dx\), numerical quadrature uses a weighted sum \(\sum a_if(x_i)\). Integrating the Lagrange polynomial through equally spaced nodes gives the Newton–Cotes formulas. The two simplest are the following.
Theorem 8.2.1 (Trapezoidal rule). With \(h = b-a\), \[\int _a^bf(x)\,dx = \frac h2\bigl [f(a)+f(b)\bigr ]-\frac {h^3}{12}f''(\xi ),\qquad \xi \in (a,b).\]
Proof. The linear interpolant at \(x_0 = a\), \(x_1 = b\) has error \(\frac {f''(\xi (x))}{2}(x-a)(x-b)\). Its integral is \(\frac h2[f(a)+f(b)]\), the area of a trapezoid. The error integral is \(\frac 12\int _a^bf''(\xi (x))(x-a)(x-b)\,dx\); since \((x-a)(x-b)\) does not change sign on \([a,b]\), the Weighted Mean Value Theorem (Theorem 1.1.5) gives \[\frac {f''(\xi )}{2}\int _a^b(x-a)(x-b)\,dx = \frac {f''(\xi )}{2}\left (-\frac {h^3}{6}\right ) = -\frac {h^3}{12}f''(\xi ) .\] □
Theorem 8.2.2 (Simpson’s rule). With \(h = \frac {b-a}{2}\) and \(x_1 = a+h\), \[\int _a^bf(x)\,dx = \frac h3\bigl [f(a)+4f(x_1)+f(b)\bigr ]-\frac {h^5}{90}f^{(4)}(\xi ),\qquad \xi \in (a,b).\]
Proof. Integrating the quadratic interpolant gives the weights \(\frac h3,\frac {4h}3,\frac h3\). For the error, the direct approach gives only \(O(h^4)\), because the cubic \((x-a)(x-x_1)(x-b)\) changes sign. Instead expand \(f\) about \(x_1\): \(f(x) = f(x_1)+f'(x_1)(x-x_1)+\frac {f''(x_1)}{2}(x-x_1)^2+\frac {f'''(x_1)}{6}(x-x_1)^3+\frac {f^{(4)}(\xi (x))}{24}(x-x_1)^4\). Integrating over \([x_1-h,x_1+h]\), the odd powers vanish by symmetry, and the Weighted Mean Value Theorem applied to \((x-x_1)^4\geq 0\) gives \[\int _a^bf = 2hf(x_1)+\frac {h^3}{3}f''(x_1)+\frac {f^{(4)}(\xi _1)}{60}h^5 .\] Replacing \(f''(x_1)\) by the central difference \(\frac {f(a)-2f(x_1)+f(b)}{h^2}-\frac {h^2}{12}f^{(4)}(\xi _2)\) (derived by adding the Taylor expansions of \(f(x_1\pm h)\)) gives \[\int _a^bf = \frac h3[f(a)+4f(x_1)+f(b)]-\frac {h^5}{12}\left [\frac 13f^{(4)}(\xi _2)-\frac 15f^{(4)}(\xi _1)\right ],\] and a more careful argument shows that the bracket can be replaced by \(\frac {2}{15}f^{(4)}(\xi )\), giving \(-\frac {h^5}{90}f^{(4)}(\xi )\). □
Definition 8.2.3. The degree of precision of a quadrature formula is the largest \(n\) such that the formula is exact for \(x^k\), \(k = 0,1,\dots ,n\).
The error terms show that the trapezoidal rule has degree of precision \(1\) and Simpson’s rule degree \(3\): one more than its number of intervals suggests, because the error involves \(f^{(4)}\), which vanishes for cubics. The same happens for every Newton–Cotes formula with an odd number of points. With four points, \(h = \frac {b-a}3\), integration of the cubic interpolant gives Simpson’s three-eighths rule \[\int _a^bf(x)\,dx = \frac {3h}{8}\bigl [f(x_0)+3f(x_1)+3f(x_2)+f(x_3)\bigr ]-\frac {3h^5}{80}f^{(4)}(\xi ),\] which also has degree of precision \(3\), with a slightly smaller error constant than Simpson’s rule over the same interval.
Example 8.2.4. Compare the trapezoidal rule, Simpson’s rule and Simpson’s three-eighths rule for \(\int _0^2f(x)\,dx\) for several functions.
Solution.
| \(f(x)\) | \(x^2\) | \(x^4\) | \(\frac 1{x+1}\) | \(\sqrt {1+x^2}\) | \(\sin x\) | \(e^x\) |
| exact | \(2.667\) | \(6.400\) | \(1.099\) | \(2.958\) | \(1.416\) | \(6.389\) |
| trapezoidal | \(4.000\) | \(16.000\) | \(1.333\) | \(3.236\) | \(0.909\) | \(8.389\) |
| Simpson | \(2.667\) | \(6.667\) | \(1.111\) | \(2.964\) | \(1.425\) | \(6.421\) |
| Simpson \(\frac 38\) | \(2.667\) | \(6.519\) | \(1.105\) | \(2.960\) | \(1.420\) | \(6.403\) |
Both Simpson rules are exact for \(x^2\) (degree of precision \(3\)); neither is exact for \(x^4\). For the trapezoidal rule, \(h = 2\) is far too large; the next subsection shows how to use these rules on small subintervals.
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