Monotonicity and local extrema: Survey of methods

Let f be a "reasonable" function, which here means that its domain consists of at most countably many intervals, on each of which the function is differentiable. We want to determine intervals of monotonicity and local extrema.

Algorithm:
Step 1. Identify intervals of the domain of f.
Step 2. Find the derivative f ′. Find critical points, that is, points where f ′(x) = 0 or f ′ does not exist. These points will split intervals from Step 1 into intervals of monotonicity.
By the way, it is not necessary to exactly identify points where the derivative does not exist. It is enough to include all points where the existence is suspect; if we put in some extra points, they will be discarded in subsequent steps (see connecting adjacent intervals of identical monotonicity).
Step 3. For each of the intervals from Step 2, determine the sign of the derivative. General method: substitute a point from the interior of such an interval into f ′ to find the sign.
Special method for a derivative that is a product and/or ratio of factors: Determine the signs for each factor separately and then multiply the signs using the standard sign algebra. This is best done using a table (see Example below).
Step 4. Determine monotonicity from the signs of f ′. The function f is increasing on intervals where f ′ is positive. It is decreasing on intervals where f ′ is negative.
When there are two adjacent intervals of the same monotonicity, check whether these can be connected (for instance, if the function is continuous at the meeting point of the two intervals, then we can always connect). When writing the intervals in the answer, include endpoints where f is continuous from the appropriate side.
Step 5. Determine local extrema. Local minima are all points from the domain where f changes from decreasing to increasing and is continuous. Local maxima are all points from the domain where f changes from increasing to decreasing and is continuous. In case of critical points where f is not continuous further analysis is necessary.

For background for this algorithm see Monotonicity and local extrema in Theory - Graphing.

Example: Investigate monotonicity and local extrema of

Solution: Df ) = (−∞,−1) ∪ (−1,∞). We start with two intervals.

Critical points: f ′(x) = 0 yields x = −3 and x = 0; there are no points in the domain where the derivative would not exist, hence no other critical point. We obtain four intervals. We put them in a table, using closed ends where f is continuous from the appropriate side. In the rows we put individual factors of the derivative and determine their signs in all intervals, then do conclusions.

We see two adjacent intervals of the same monotonicity, so we should ask whether they can be connected. Since f is continuous at 0, they can.

Conclusion: f is increasing on (−∞,−3] and on (−1,∞), it is decreasing on [−3,−1). It has a local maximum f (−3) = −11/8.

Note: f also changes monotonicity at −1, but this point is not in the domain, so it is not a local extreme. By the way, if you want to see how this function looks like, check out the Example in Methods Survey - Graphing - Overview).

Remark: The table method has one particular advantage, it is not necessary to substitute points from intervals into the derivative (or its factors). If a factor is linear, then it changes sign only once, namely at the point where this factor is zero. Thus it is enough to mark this dividing point in the table and then put one kind of a sign to the right and opposite signs to the left. Should it go − + or + −? That's easy to see, it is enough to substitute some number other than the dividing point, once can even ask what happens when x grows to infinity, does the factor become positive or negative? For more insight see Sign inequalities in the section Solving equations and inequalities in Extra. This possibility of avoiding substituting numbers from intervals can be quite useful in case of a small interval, for instance there is no nice integer to substitute from the interval (2,3).

For other examples see Monotonicity and local extrema in Theory - Graphing and appropriate problems in Solved Problems.


Concavity and inflection points
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