Unit 5: Analytical Applications of Differentiation
- Syllabus
- 2020
- Section
- —
- Level
- —

The Mean Value Theorem connects the average rate across an interval to an instantaneous rate inside it. If f is continuous on [a,b] and differentiable on (a,b), then at least one c in (a,b) has tangent slope equal to the secant slope from a to b.
f'(c)=\frac{f(b)-f(a)}{b-a}\qquad\text{for some }c\in(a,b)
For f(x)=x2 on [1,3], the polynomial is continuous on [1,3] and differentiable on (1,3). The average rate is 3−1f(3)−f(1)=29−1=4. Since f′(x)=2x, solve 2c=4 to get c=2, which lies in (1,3).
MVT guarantees at least one point, not exactly one. If continuity or differentiability fails, the theorem gives no guarantee; that does not by itself prove that no matching point exists.
The Extreme Value Theorem (EVT) is an existence guarantee. If f is continuous on the closed interval [a,b], then f attains at least one absolute (global) minimum value and at least one absolute maximum value somewhere on [a,b]. The theorem guarantees that these values exist; it does not locate them.
| Idea | Meaning | Key point |
|---|---|---|
| Global extremum | Greatest or least value on the entire stated interval | May occur at an endpoint or an interior point |
| Local extremum | Greatest or least value compared with nearby values | Must occur at a critical point |
| Critical point | A point on the function where f′(x)=0 or f′(x) does not exist | It is only a candidate; it need not be an extremum |
To find global extrema on [a,b]: first verify continuity on the whole closed interval. Next find every interior point where f′(x)=0 or f′(x) does not exist. Evaluate f at those critical points and at both endpoints. The largest output is the global maximum value, and the smallest is the global minimum value.
For f(x)=x2−2x on [0,3], continuity guarantees both global extrema. Since f′(x)=2x−2, the only interior critical point is x=1. Compare f(0)=0, f(1)=−1, and f(3)=3. Thus the global minimum is −1 at x=1, while the global maximum is 3 at the endpoint x=3.
Do not reverse the critical-point statement. Every local extremum is a critical point, but a critical point may be neither a maximum nor a minimum: for f(x)=x3, f′(0)=0, yet the function keeps increasing through x=0.
The sign of f′(x) tells the direction of change of f. On an interval where f′(x)>0, the function f is increasing; where f′(x)<0, f is decreasing. This conclusion concerns the original function, not the graph of its derivative.
For f(x)=x3−3x, f′(x)=3x2−3=3(x−1)(x+1). The derivative is zero at x=−1 and x=1, so these values split the real line into three intervals.
| Interval | Sign of f′(x) | Behavior of f |
|---|---|---|
| (−∞,−1) | + | increasing |
| (−1,1) | − | decreasing |
| (1,∞) | + | increasing |
Write increasing and decreasing sets as open intervals separated by critical values or domain breaks. A single point where f′(x)=0 does not itself form an interval, and the sign must be established on each side rather than inferred from the zero alone.
The First Derivative Test classifies a critical point by tracking how f moves on either side. Because f′(x)>0 means f is increasing and f′(x)<0 means f is decreasing, a local extremum occurs only when the sign of f′ changes.
| Sign of f′ through x=c | Behavior of f | Conclusion at c |
|---|---|---|
| +→− | increasing, then decreasing | local maximum |
| −→+ | decreasing, then increasing | local minimum |
| +→+ or −→− | same direction on both sides | neither |
Find the critical points in the domain, use them to split the domain into intervals, and determine the sign of f′ on each interval. Then state the sign change and the corresponding conclusion; the sign chart is the justification.
For f(x)=x3−3x, f′(x)=3(x−1)(x+1), so the critical points are x=−1 and x=1. The derivative signs are positive on (−∞,−1), negative on (−1,1), and positive on (1,∞). Therefore f′ changes +→− at x=−1, giving a local maximum, and −→+ at x=1, giving a local minimum.
A critical point is only a candidate. The equation f′(c)=0 alone does not prove a local extremum; the First Derivative Test requires signs on both sides of c.
For a function on a closed interval [a,b], an absolute extremum can occur only at an interior critical point or at an endpoint. If the function is continuous on [a,b], the Extreme Value Theorem also guarantees that an absolute minimum and maximum exist. The Candidates Test locates them by comparing all possible outputs.
For f(x)=x2+2x−3 on [−3,2], f′(x)=2x+2=0 gives the interior critical point x=−1. The candidates are therefore −3, −1, and 2.
| Candidate x | f(x) | Conclusion |
|---|---|---|
| −3 | 0 | neither global extreme |
| −1 | −4 | absolute minimum |
| 2 | 5 | absolute maximum |
Compare values of f, not values of f′. Endpoints must be checked even though the usual interior derivative test does not classify them as local critical points. If the largest or smallest output occurs at several candidates, report every location.
Concavity describes how the slopes of f change. If f′ is increasing, successive tangent slopes become larger and f is concave up. If f′ is decreasing, the slopes become smaller and f is concave down. Because f′′ measures the rate of change of f′, its sign gives the same information directly.
| Behavior of f′ | Sign of f′′ | Concavity of f |
|---|---|---|
| increasing | f′′(x)>0 | concave up |
| decreasing | f′′(x)<0 | concave down |
Find domain values where f′′(x)=0 or f′′(x) does not exist. Use them, together with any domain breaks, to divide the domain into open intervals. Determine the sign of f′′ on each interval. A point on the graph is an inflection point only when the concavity changes across it.
For f(x)=x3−3x2, f′′(x)=6x−6=6(x−1). This is negative for x<1 and positive for x>1, so f is concave down on (−∞,1) and concave up on (1,∞). The concavity changes at x=1, and f(1)=−2, so (1,−2) is an inflection point.
The equation f′′(c)=0 or an undefined second derivative identifies only a candidate. Without a change from concave up to concave down or vice versa, there is no inflection point.
The Second Derivative Test uses concavity to classify a stationary critical point. First verify that f′(c)=0 and that f′′(c) exists. Then the sign of f′′(c) tells whether the graph bends upward or downward at x=c.
| Value of f′′(c) | Concavity near c | Conclusion |
|---|---|---|
| f′′(c)>0 | concave up | local minimum at x=c |
| f′′(c)<0 | concave down | local maximum at x=c |
| f′′(c)=0 | not determined | test is inconclusive |
Solve f′(x)=0 for the stationary critical points, calculate f′′(c) at each one, and state the sign-based conclusion. If f′′(c)=0 or does not exist, use another method such as the First Derivative Test; do not force a classification.
For f(x)=x2+4x+1, f′(x)=2x+4, so the only critical point is c=−2. Since f′′(x)=2>0, f has a local minimum at x=−2. The polynomial is continuous on (−∞,∞) and has only this one critical point, so the local minimum is also the absolute minimum; its value is f(−2)=−3.
The global conclusion needs all of its own conditions: continuity on the interval, exactly one critical point there, and proof that this point is a local extremum. Also, f′′(c)=0 does not mean “no extremum”; it means this test gives no answer.
Whether derivative information is given as a graph, table, or formula, read the same mathematical features: sign, zeros, sign changes, and increasing or decreasing behavior. Translate each feature into a statement about f before attempting a sketch.
| Derivative information | Behavior of f |
|---|---|
| f′>0 / f′<0 | increasing / decreasing |
| f′ changes +→− / −→+ | local maximum / local minimum |
| f′ increasing, equivalently f′′>0 | concave up |
| f′ decreasing, equivalently f′′<0 | concave down |
| f′′ changes sign at a point on f | inflection point |
Mark domain breaks and important x-values first. Next record intervals of increase/decrease and classify any sign-changing zeros of f′. Then add concavity and verified inflection points from f′′ or from the trend of f′. Finally connect the features without contradicting any interval statement.
Suppose f′ is positive on (−∞,−2), negative on (−2,1), and positive on (1,∞). Then f has a local maximum at x=−2 and a local minimum at x=1. If f′′<0 for x<0 and f′′>0 for x>0, the sketch is concave down before 0, concave up after 0, and has an inflection point at x=0 if that point lies on the graph.
Derivative information determines shape, not absolute vertical position: functions that differ by a constant have the same derivatives. A value such as f(a) is needed to anchor the sketch vertically. Also, a zero of f′ or f′′ matters only when the required sign change occurs.
At the same input x, the height f′(x) is the slope of the graph of f, and the height f′′(x) is the slope of the graph of f′. To match related graphs, compare features at aligned x-values; the graphs do not need to look alike.
| Feature on one graph | Aligned feature on the next graph |
|---|---|
| f increasing / decreasing | f′ above / below the x-axis |
| horizontal tangent on f | zero of f′ |
| f concave up / down | f′ increasing / decreasing |
| horizontal tangent on f′ | zero of f′′ |
| f′ increasing / decreasing | f′′ above / below the x-axis |
Start with unmistakable events such as zeros and horizontal tangents. Check the sign of the proposed derivative against increasing or decreasing intervals. Then check whether its own rise and fall agrees with the sign of the proposed second derivative. Require several consistent relationships before assigning labels.
For f(x)=x3−3x, f′(x)=3x2−3,f′′(x)=6x. The cubic has horizontal tangents at x=−1 and x=1, exactly where the derivative parabola crosses the x-axis. The parabola decreases for x<0 and increases for x>0, matching the negative and positive sides of the line f′′=6x. The cubic therefore changes concavity at x=0.
Do not match graphs by overall shape, height, or a single zero. A derivative records slope, not the original function's output. A proposed match is justified only when signs, zeros, turning behavior, and concavity agree at the same inputs.
An optimization problem asks for the greatest or least value of an objective quantity subject to constraints. Calculus begins only after the objective has been written as a function of one variable and its feasible interval has been identified.
For a rectangle with fixed perimeter P>0, let its sides be x and y. The constraint 2x+2y=P gives y=P/2−x. Therefore A(x)=x(2P−x),0≤x≤2P. Differentiate: A′(x)=P/2−2x, so the interior candidate is x=P/4. Then y=P/4. The endpoint areas are 0, while A(P/4)=P2/16, so the maximum area is P2/16 square units and occurs for a square.
Solving A′(x)=0 produces a candidate input, not automatically the requested optimum. Check that it is feasible, compare it with every required endpoint or other critical point, and distinguish the optimizing dimensions from the maximum or minimum value itself.
A complete optimization conclusion interprets both coordinates of the result. If x=x∗ produces an extremum of Q(x), then x∗ describes the input, design, time, or condition that achieves the optimum, while Q(x∗) is the minimum or maximum value of the quantity being optimized.
| Mathematical result | Contextual meaning |\n|---|---|\n| x∗ | the feasible choice or condition that produces the optimum |\n| Q(x∗) | the greatest or least achievable objective value |\n| domain of x | the choices allowed by the context |\n| units of x and Q | what each numerical value measures |
Name the quantity, state whether the result is a minimum or maximum, report where it occurs, give the optimized value with units, and connect it to the feasible interval. Use “absolute” only when the comparison covered every candidate required on that interval.
For the fixed-perimeter rectangle from the previous method, the calculation gives side lengths x=y=P/4 and area A=P2/16. The interpretation is: among all rectangles with perimeter P, the square with side length P/4 has the greatest possible area, P2/16. If P is measured in meters, the side lengths are in meters and the maximum area is in square meters.
Do not report only the critical input or only the function value. A correct derivative calculation can still produce an unusable conclusion if the input violates the feasible domain, the units are missing, or a local extremum is described as globally optimal without a complete comparison.
For a branch of an implicit relation, a critical point is a point (x,y) on the relation where dy/dx=0 or where dy/dx does not exist. The answer is a coordinate pair, so a derivative condition alone is not enough.
Differentiate implicitly and solve two cases: set the numerator of dy/dx equal to zero while the denominator is nonzero; then set the denominator equal to zero and examine where the derivative is undefined. In both cases, solve together with the original relation and verify that the relevant branch exists near the point.
For x2+4y2=4, implicit differentiation gives dy/dx=−x/(4y). The derivative is zero when x=0; the original relation then gives (0,1) and (0,−1). It is undefined when y=0; the relation gives (2,0) and (−2,0). Thus all four are critical points of the relation; the first pair has horizontal tangents and the second pair has vertical tangents.
Do not list only x-values or cancel a factor before checking where it is zero. If both numerator and denominator vanish, the simplified derivative may hide a singular point; inspect the original relation and its local branches before classifying it.
An implicitly defined branch can be analyzed without solving explicitly for y. Use the sign of dy/dx to justify where the branch increases or decreases, and the sign of d2y/dx2 to justify where it is concave up or concave down.
First find dy/dx from the relation. Differentiate that equation again with respect to x, treating y as a function of x; the result may contain x, y, and dy/dx. Substitute the first-derivative relation when useful, then determine signs using the coordinates and branch conditions supplied by the original relation.
x^2+y^2=25 \qquad \frac{dy}{dx}=-\frac{x}{y} \qquad \frac{d^2y}{dx^2}=-\frac{25}{y^3}
On the upper semicircle, y>0, so d2y/dx2<0: the entire upper branch is concave down. There, dy/dx=−x/y is positive when x<0 and negative when x>0, so the branch increases to (0,5) and then decreases. On the lower branch, y<0, so the concavity conclusion reverses.
A sign conclusion belongs only to the branch and interval on which its conditions hold. Do not assign one increasing, decreasing, or concavity statement to an entire relation when different branches give different signs, and do not cross points where the required derivative is undefined.