IA ideas library

Numerical methods IA ideas: iteration, Newton–Raphson, integration and error

Calculators and computers don't solve equations the way you do in class: they iterate. A numerical methods IA asks how an approximation works, how fast it gets close to the answer, and how big the error is. That last question is where the mathematics lives.

The trapezoidal rule (SL) and Euler's method (HL) are in the IB courses; Newton–Raphson, Simpson's rule and fixed-point iteration are not, so explain them as new mathematics. These IAs need no data collection, which suits students who would rather calculate than measure.

Graph of y = x squared minus 2 with three Newton–Raphson tangent lines from x0 = 3 converging on the square root of 2, with the iterates listed
Newton–Raphson for √2: each tangent meets the x-axis closer to the root, and the number of correct digits roughly doubles.

Numerical methods & error analysis ideas (8)

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Frequently asked questions

Can a numerical methods IA reach the top of Criterion E?

Yes, if it goes beyond running the method: comparing methods, measuring and explaining the rate at which the error shrinks, and finding cases where a method fails and saying why.

Which numerical methods are in the IB syllabus?

The trapezoidal rule is in both SL courses and Euler's method is in both HL courses. Newton–Raphson, Simpson's rule and the bisection method are not; you may use them if you explain them.

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Related types of IA: Calculus & optimisation · Differential equations & dynamics

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