Applications of Derivatives
Apply derivatives to solve optimization and rate-related problems.
Rates of Change
The derivative of a function measures its instantaneous rate of change. This is one of its most fundamental and useful applications.
- If a function represents position over time, its derivative is velocity.
- If a function represents velocity over time, its derivative is acceleration.
- In economics, the derivative of a cost function is the marginal cost.
Increasing and Decreasing Functions
The sign of the first derivative tells us whether a function is increasing or decreasing on an interval.
- If
f'(x) > 0on an interval, then f(x) is increasing on that interval. - If
f'(x) < 0on an interval, then f(x) is decreasing on that interval.
Tangents and Normals
The derivative is a powerful tool for analyzing the geometry of curves.
- A tangent line to a curve at a given point is a straight line that "just touches" the curve at that point and has the same instantaneous slope as the curve. The slope of the tangent at point `(x₀, y₀)` is given by `f'(x₀)`.
- A normal line is a line that is perpendicular to the tangent line at the point of tangency. Its slope is the negative reciprocal of the tangent's slope, which is `-1 / f'(x₀)`.
Use the slider in the visualizer below to move the point of tangency and observe how the tangent and normal lines change.
Maxima and Minima
Derivatives are crucial for finding the maximum and minimum values of a function (optimization). These occur at critical points, where the derivative is either zero or undefined.
First Derivative Test
This test helps identify local maxima and minima by looking at the sign change of the derivative at a critical point `c`:
- If f'(x) changes from positive to negative at `c`, then there is a local maximum at `c`.
- If f'(x) changes from negative to positive at `c`, then there is a local minimum at `c`.
Second Derivative Test
This test uses the second derivative to classify critical points where f'(c) = 0:
- If
f''(c) > 0, then there is a local minimum at `c` (concave up). - If
f''(c) < 0, then there is a local maximum at `c` (concave down).
Rolle's Theorem
Rolle's Theorem is a special case of the Mean Value Theorem. It states that if a function `f` is:
- Continuous on the closed interval [a, b],
- Differentiable on the open interval (a, b), and
- f(a) = f(b)
Then there exists at least one number `c` in (a, b) such that f'(c) = 0.
Geometrically, this means that if a smooth curve has the same height at two different points, there must be at least one point between them where the tangent line is horizontal (i.e., its slope is zero).
Lagrange's Mean Value Theorem
The Mean Value Theorem (MVT) is a more general version of Rolle's Theorem. It states that if a function `f` is:
- Continuous on the closed interval [a, b], and
- Differentiable on the open interval (a, b)
Then there exists at least one number `c` in (a, b) such that:
f'(c) = [f(b) - f(a)] / [b - a]This means that there is at least one point on the curve where the slope of the tangent line (`f'(c)`) is equal to the slope of the secant line connecting the endpoints (a, f(a)) and (b, f(b)).