Derivatives are essential for measuring rates of change, optimizing functions, and analyzing motion. In physics, they determine velocity and acceleration, while in economics, they help maximize profit and minimize costs. Derivatives also play a key role in curve sketching, identifying points of inflection, and analyzing concavity. Engineers use derivatives in system modeling, while in biology, they model population growth and disease spread. In financial mathematics, derivatives help in pricing and risk management. Additionally, derivatives are used in signal processing to filter and analyze data, and in geometry to find tangent and normal lines to curves.
The use of the derivative to determine instantaneous rates of change is a very important application of calculus. One of the first areas of applied mathematics to be studied in the seventeenth century was motion in a straight line. The problems of kinematics were the motivation for Newton’s work on calculus.
The derivative of a function is a new function that gives the measure of the gradient of the tangent at each point on the curve. With the gradient, we can find the equation of the tangent line at a given point on the curve.
Suppose that \((x_1, y_1)\) is a point on the curve \(y = f(x)\). Then, if \(f\) is differentiable at \(x = x_1\), the equation of the tangent at \((x_1, y_1)\) is given by:
\[ y - y_1 = f'(x_1)(x - x_1) \]
The normal to a curve at a point on the curve is the line that passes through the point and is perpendicular to the tangent at that point.
Two lines with gradients \(m_1\) and \(m_2\) are perpendicular if and only if \(m_1 m_2 = -1\).
Thus, if a tangent has gradient \(m\), the normal has gradient \(-\frac{1}{m}\).
The process of differentiation can be used to address many problems involving rates of change.
For a function with rule \( f(x) \):
The average rate of change for \( x \in [a, b] \) is given by:
\[ \frac{f(b) - f(a)}{b - a} \]
The instantaneous rate of change of \( f \) with respect to \( x \) when \( x = a \) is given by \( f'(a) \).
The derivative \( \frac{dy}{dx} \) gives the instantaneous rate of change of \( y \) with respect to \( x \).
If \( \frac{dy}{dx} > 0 \), then \( y \) is increasing as \( x \) increases.
If \( \frac{dy}{dx} < 0 \), then \( y \) is decreasing as \( x \) increases.
In the previous chapter, we have seen that the gradient of the tangent at a point \( (a, f(a)) \) on the curve with rule \( y = f(x) \) is given by \( f'(a) \).
A point \( (a, f(a)) \) on a curve \( y = f(x) \) is said to be a stationary point if \( f'(a) = 0 \).
Equivalently, a point \( (a, f(a)) \) on \( y = f(x) \) is a stationary point if \( \frac{dy}{dx} = 0 \) when \( x = a \).
The graph of \(y = f(x)\) shown has three stationary points: A, B, and C.
Point A is called a local maximum point. Notice that immediately to the left of A, the gradient is positive, and immediately to the right, the gradient is negative.
Gradient: + 0 −
Shape of \(f\): ↗ — ↘
Point B is called a local minimum point. Notice that immediately to the left of B, the gradient is negative, and immediately to the right, the gradient is positive.
Gradient: − 0 +
Shape of \(f\): ↘ — ↗
Point C is called a stationary point of inflection. The gradient is positive immediately to the left and right of C.
Gradient: + 0 +
Shape of \(f\): ↗ — ↗
Clearly, it is also possible to have stationary points of inflection where the gradient is negative immediately to the left and right.
Gradient: − 0 −
Shape of \(f\): ↘ — ↘
Stationary points of types A and B are referred to as turning points.
Local maximum and minimum values were discussed in the previous section. These are often not the actual maximum and minimum values of the function.
For a function defined on an interval:
The corresponding points on the graph of the function are not necessarily stationary points.
More precisely, for a continuous function \( f \) defined on an interval \([a, b]\):
We know that when we take the derivative of a function, we obtain a new function, the derivative, which gives the instantaneous rate of change. We can apply the same technique to the new function to find the maximum rate of increase or decrease.
Remember: