IB Maths AI HL Further Differentiation Paper 1 & 2 ~6 min read

Second Order Derivatives

Differentiate once and you get the gradient. Differentiate the gradient and you get the second derivative β€” the rate of change of the rate of change. It tells you how the gradient itself is shifting, which is exactly what you need to classify stationary points and read a curve’s concavity in the topics ahead.

πŸ“˜ What you need to know

What the notation means

The first derivative is the gradient; the second is the gradient’s gradient. Watch where the superscript 2’s go β€” they sit differently in the two parts of the fraction.

First and second derivative notation dydx = fβ€²(x)  (first)  Β·  dΒ²ydxΒ² = fβ€²β€²(x)  (second) βœ— notation to recognise, not a booklet formula

🧠 “d-two-y over d-x-squared”

You differentiate twice (so dΒ²) with respect to x twice (so xΒ²). The 2 sits up top with the d, but down below with the x β€” that asymmetry is the thing to memorise.

DerivativeWhat it measures
f(x)the value of the function
fβ€²(x) (first)the gradient β€” the rate of change of f
fβ€²β€²(x) (second)the rate of change of the gradient
Why it’s useful: the second derivative lets you test for local minimum and maximum points, decide the nature of stationary points, determine the concavity of a curve, and help sketch the graph of the derivative β€” all coming up in the next two topics.

Finding a second derivative

🧭 Recipe β€” second derivative

  1. Rewrite roots and fractions as negative/fractional powers of x.
  2. Differentiate once to get fβ€²(x) β€” applying chain/product/quotient rules as needed.
  3. Differentiate again to get fβ€²β€²(x), working carefully term by term.
  4. Evaluate or simplify if asked β€” e.g. tidy surds by rationalising the denominator.

πŸ€” Why are negative powers so error-prone here?

Each differentiation drops the power by 1, so a term like xβˆ’1/2 becomes xβˆ’3/2, then xβˆ’5/2 β€” the exponents get more negative and the coefficients pick up extra factors each time. Two passes means twice the chance to slip a sign or mishandle the fraction, so the safest approach is one clean term at a time.

Worked examples

Throughout, f(x) = 4 βˆ’ √x + 3√x.

WE 1

Rewrite f(x) as powers of x

Convert the roots before differentiating.

√x = x^(1/2), 3√x = 3x^(βˆ’1/2) f(x) = 4 βˆ’ x^(1/2) + 3x^(βˆ’1/2)
WE 2

Find fβ€²(x)

Differentiate once, term by term.

4 β†’ 0 βˆ’x^(1/2) β†’ βˆ’Β½x^(βˆ’1/2) 3x^(βˆ’1/2) β†’ 3Β·(βˆ’Β½)x^(βˆ’3/2) = βˆ’32x^(βˆ’3/2) fβ€²(x) = βˆ’Β½x^(βˆ’1/2) βˆ’ 32x^(βˆ’3/2)
WE 3

Find fβ€²β€²(x)

Differentiate fβ€²(x) again. Watch the negatives.

βˆ’Β½x^(βˆ’1/2) β†’ βˆ’Β½Β·(βˆ’Β½)x^(βˆ’3/2) = ΒΌx^(βˆ’3/2) βˆ’32x^(βˆ’3/2) β†’ βˆ’32Β·(βˆ’32)x^(βˆ’5/2) = 94x^(βˆ’5/2) fβ€³(x) = ΒΌx^(βˆ’3/2) + 94x^(βˆ’5/2)
WE 4

Evaluate fβ€²β€²(3) in the form a√b

Write as surds, substitute x = 3, then rationalise.

fβ€³(x) = 14x√x + 94x²√x fβ€³(3) = 112√3 + 936√3 = 1236√3 = 13√3 rationalise: 13√3 Γ— √3√3 = √39 fβ€³(3) = 19√3
WE 5

Find fβ€²β€²(x) for f(x) = 2x3 βˆ’ 5x2 + 4x βˆ’ 1

A polynomial β€” straightforward double differentiation.

fβ€²(x) = 6xΒ² βˆ’ 10x + 4 fβ€³(x) = 12x βˆ’ 10 fβ€³(x) = 12x βˆ’ 10

πŸ’‘ Top tips

⚠ Common mistakes

Next up β€” Stationary Points. With both first and second derivatives in hand, you can now do more than just find stationary points (where fβ€²(x) = 0) β€” you can classify them. The next topic uses the sign of fβ€²β€²(x) to decide instantly whether a turning point is a local minimum, a local maximum, or something subtler.

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