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Chapter 6: Application of Derivatives

A derivative is more than a slope , it is a measure of instantaneous rate of change. In this chapter the derivative becomes a tool for solving practical problems: finding the steepest slope of a hill, the fastest growth rate of an investment, the dimensions of a box that uses the least material. We will translate each problem into a function f(x)f(x), differentiate, and use the sign or vanishing of ff' to extract the desired information.

The chapter has three branches. The rate-of-change branch uses derivatives directly: if yy depends on xx and xx depends on time, then dydt=dydxdxdt\dfrac{dy}{dt} = \dfrac{dy}{dx}\dfrac{dx}{dt}. The tangents/normals branch reads geometric information from ff'. The optimisation branch , the heaviest in board examinations , finds extrema by setting f(x)=0f'(x) = 0 and classifying the critical points using ff''.

For JEE, the same techniques become subtler. Problems often involve a parameter, asking for ranges where a function is increasing, or for the largest possible value of a complicated expression. The first-derivative test and the second-derivative test become indispensable. So does sign analysis: drawing a number line, marking the critical points, and checking the sign of ff' in each interval.

Approximation by differentials , using f(a+h)f(a)+hf(a)f(a + h) \approx f(a) + h f'(a) , is a small but useful section. It allows quick estimates of square roots and trigonometric values without a calculator, and previews the deeper Taylor's theorem from higher mathematics.

The chapter has practical importance beyond examinations. Physics, economics, biology , every quantitative science uses optimisation. The derivative is the universal tool.

What's inside

  1. Rate of change , derivatives as rates.
  2. Increasing and decreasing functions , monotonicity test.
  3. Tangents and normals , line equations from the derivative.
  4. Approximation , using differentials.
  5. Maxima and minima , first and second derivative tests.
  6. Applied optimisation , modelling real problems.

Key results / Formula card

ConceptFormula
Rate of changedydt=dydxdxdt\dfrac{dy}{dt} = \dfrac{dy}{dx} \cdot \dfrac{dx}{dt}
Increasing on IIf(x)0f'(x) \ge 0 on II (strict if >0> 0)
Decreasing on IIf(x)0f'(x) \le 0 on II
Tangent slope at (a,f(a))(a, f(a))f(a)f'(a)
Normal slope1/f(a)-1/f'(a)
Tangent equationyf(a)=f(a)(xa)y - f(a) = f'(a)(x - a)
Approximationf(a+h)f(a)+hf(a)f(a + h) \approx f(a) + h f'(a)
Critical pointf(c)=0f'(c) = 0 or f(c)f'(c) undefined
1st derivative testsign change of ff' at cc
2nd derivative testf(c)>0f''(c) > 0 ⇒ min, f(c)<0f''(c) < 0 ⇒ max

How to read this chapter

Master the first-derivative test, then the second. Practise translating word problems into functions of a single variable. The optimisation section repays repeated practice , most board questions on this chapter are optimisation problems in disguise.

Sub-topics

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