Numerical Methods

Apply numerical methods at A-Level: sign change, Newton-Raphson, and the trapezium rule.

Numerical methods find approximate solutions when exact methods fail. A-Level covers change of sign, iteration, Newton-Raphson, and the trapezium rule.

Change of Sign

If f(a)f(a) and f(b)f(b) have opposite signs and ff is continuous, there's a root between aa and bb.

Newton-Raphson Method

xn+1=xnf(xn)f(xn)x_{n+1} = x_n - \frac{f(x_n)}{f'(x_n)}

Converges quickly but can fail with poor starting point or f(xn)0f'(x_n) \approx 0.

Example

f(x)=x32f(x) = x^3 - 2. f(x)=3x2f'(x) = 3x^2. x0=1x_0 = 1.

x1=113=1.333...x_1 = 1 - \frac{-1}{3} = 1.333... x2=1.3330.3705.3331.264x_2 = 1.333 - \frac{0.370}{5.333} \approx 1.264

Converges to 231.2599\sqrt[3]{2} \approx 1.2599.

The Trapezium Rule

abf(x)dxh2[y0+2(y1+y2+...+yn1)+yn]\int_a^b f(x)\,dx \approx \frac{h}{2}[y_0 + 2(y_1 + y_2 + ... + y_{n-1}) + y_n]

where h=banh = \frac{b-a}{n}.

When It Works Well

Better with more strips. Overestimates for concave-up curves, underestimates for concave-down.

Practice Problems

    1. Show x3+x5=0x^3 + x - 5 = 0 has a root between 1 and 2.
    1. Use Newton-Raphson on x23=0x^2 - 3 = 0 with x0=2x_0 = 2.
    1. Use the trapezium rule with 4 strips to estimate 01ex2dx\int_0^1 e^{x^2}\,dx.

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Key Takeaways

  • Sign change: proves existence of a root.

  • Newton-Raphson: fast convergence, needs f(x)f'(x).

  • Trapezium rule: approximates definite integrals.

  • More strips/iterations → better accuracy.

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