Finding Maximums and Minimums
Site: | Saylor Academy |
Course: | MA005: Calculus I |
Book: | Finding Maximums and Minimums |
Printed by: | Guest user |
Date: | Saturday, May 4, 2024, 2:32 AM |
Description
Read this section to learn about maximums, minimums, and extreme values for functions. Work through practice problems 1-5.
Introduction
In theory and applications, we often want to maximize or minimize some quantity. An engineer may want to maximize the speed of a new computer or minimize the heat produced by an appliance. A manufacturer may want to maximize profits and market share or minimize waste. A student may want to maximize a grade in calculus or minimize the hours of study needed to earn a particular grade.
Also, many natural objects follow minimum or maximum principles, so if we want to model some natural phenomena we may need to maximize or minimize. A light ray travels along a "minimum time" path. The shape and surface texture of some animals tend to minimize or maximize heat loss. Systems reach equilibrium when their potential energy is minimized. A basic tenet of evolution is that a genetic characteristic which maximizes the reproductive success of an individual will become more common in a species.
Calculus provides tools for analyzing functions and their behavior and for finding maximums and minimums.
Source: Dale Hoffman, https://s3.amazonaws.com/saylordotorg-resources/wwwresources/site/wp-content/uploads/2012/12/MA005-4.1-Finding-Maximums-and-Minimums.pdf
This work is licensed under a Creative Commons Attribution 3.0 License.
Methods for Finding Maximums and Minimums
We can try to find where a function is largest or smallest by evaluating at lots of values of , a method which is not very efficient and may not find the exact place where achieves its extreme value. However, if we try hundreds or thousands of values for , then we can often find a value of which is close to the maximum or minimum. In general, this type of exhaustive search is only practical if you have a computer do the work.
The graph of a function is a visual way of examining lots of values of , and it is a good method, particularly if you have a computer to do the work for you. However, it is inefficient, and we still may not find the exact location of the maximum or minimum.
Calculus provides ways of drastically narrowing the number of points we need to examine to find the exact locations of maximums and minimums. Instead of examining at thousands of values of , calculus can often guarantee that the maximum or minimum must occur at one of 3 or 4 values of , a substantial improvement in efficiency.
A Little Terminology
Before we examine how calculus can help us find maximums and minimums, we need to define the concepts we will develop and use.
Definitions: has a maximum or global maximum at a if for all in the domain of .
The maximum value
of is , and this maximum value of occurs at
The maximum point on the graph of is . (Fig. 1)
Fig. 1
Definition: has a local or relative maximum at a if for all near or in some open interval which contains .
Global and local minimums are defined similarly by replacing the with in the previous definitions.
Definition: has a global extreme at if is a global maximum or minimum.
has a local extreme at if is a local maximum or minimum.
The local and global extremes of the function in Fig. 2 are labeled. You should notice that every global extreme is also a local extreme, but there are local extremes which are not global extremes. If is the height of the earth above sea level at the location , then the global maximum of is (summit of Mt. Everest) =29,028 feet. The local maximum of for the United States is (summit of Mt. McKinley)=20,320 feet. The local minimum of for the United States is (Death Valley)=-282 feet.
Practice 1: The table shows the annual calculus enrollments at a large university. Which years had relative maximum or minimum calculus enrollments? What were the global maximum and minimum enrollments in calculus?
Finding Maximums and Minimums of a Function
One way to narrow our search for a maximum value of a function is to eliminate those values of which, for some reason, cannot possibly make maximum.
Proof: Assume that . By definition, , so and the right and left limits are both positive: and .
Since the right limit, , is positive, there are values of so .
Multiplying each side of this last inequality by the positive , we have and so is not a maximum.
Since the left limit, , is positive, there are values of so .
Multiplying each side of the last inequality by the negative , we have that and so is not a minimum.'
The proof for the case is similar.
When we evaluate the derivative of a function at a point , there are only four possible outcomes: or is undefined. If we are looking for extreme values of , then we can eliminate those points at which is positive or negative, and only two possibilities remain: or is undefined.Example 1: Find the local extremes of for all values of .
Solution: An extreme value of can occur only where or where is not differentiable. so only at and is a polynomial, so is differentiable for all .
The only possible locations of local extremes of are at and . We don't know yet whether or is a local extreme of , but we can be certain that no other point is a local extreme. The graph of (Fig. 4 ) shows that is a local maximum and is a local minimum. This function does not have a global maximum or minimum.
Practice 2: Find the local extremes of and .
It is important to recognize that the conditions " " or " not differentiable at a " do not guarantee that is a local maximum or minimum. They only say that might be a local extreme or that is a candidate for being a local extreme.
Example 2: Find all local extremes of .
Solution: is differentiable for all , and . The only place where
is at , so the only candidate is the point . But if then
, so is not a local maximum. Similarly, if then
so is not a local minimum. The point is the only candidate to be a local extreme of , and this candidate did not turn out to be a local extreme of . The function does not have any local extremes. (Fig. 5 )
Fig. 5
If or is not differentiable at
then the point is a candidate to be a local extreme and may or may not be a local extreme.
Practice 3: Sketch the graph of a differentiable function which satisfies the conditions:
(i) and ,
(ii) and ,
(iii) the only local maximums of are at and , and the only local minimum is at .
Is f(a) a Maximum or Minimum or Neither?
Once we have found the candidates for extreme points of , we still have the problem of determining whether the point is a maximum, a minimum or neither.
One method is to graph (or have your calculator graph) the function near a, and then draw your conclusion from the graph. All of the graphs in Fig. 6 have , and, on each of the graphs, either equals or is undefined. It is clear from the graphs that the point is a local maximum in (a) and (d), is a local minimum in (b) and (e), and is not a local extreme in (c) and (f).
Fig. 6
In sections 3.3 and 3.4, we will investigate how information about the first and second derivatives of can help determine whether the candidate is a maximum, a minimum, or neither.
Endpoint Extremes
So far we have been discussing finding extreme values of functions over the entire real number line or on an open interval, but, in practice, we may need to find the extreme of a function over some closed interval []. If the extreme value of occurs at between and , then the previous reasoning and results still apply: either or is not differentiable at .
On a closed interval, however, there is one more possibility: an extreme can occur at an endpoint of the closed interval (Fig. 7), at or .
Fig. 7
Practice 4: List all of the local extremes of the function in Fig. 8 on the interval and state whether (i) or (ii) is not differentiable
at a or (iii) a is an endpoint.
Fig. 8
Example 3: Find the extreme values of for .
Solution: . We need to find where (i) , (ii) is not differentiable, and (iii) the endpoints.
Altogether we have four points in the interval to examine, and any extreme values of can only occur when is one of those four points: , and . The minimum of on is when , and the maximum of on is when .Sometimes the function we need to maximize or minimize is more complicated, but the same methods work.
Example 4: Find the extreme values of for .
Solution: This function comes from an application we will examine in section 3.5. The only possible locations of extremes are where or is undefined or where is an endpoint of the interval .
To determine where , we need to set the derivative equal to and solve for .
Then so , and the only point in the interval where is at .
Putting into the original equation for gives .
We can evaluate the formula for for any value of , so the derivative is always defined. Finally, the interval has two endpoints, and . and .
The maximum of on must occur at one of the points , and , and the minimum must occur at one of these three points.
The maximum value of is at , and the minimum value of is at . The graph of is shown in Fig. 9.
Fig. 9
Critical Numbers
The points at which a function might have an extreme value are called critical numbers.
Definitions: A critical number for a function is a value in the domain of so
(i)
or (ii) is not differentiable at ,
or (iii)
is an endpoint
If we are trying to find the extreme values of on an open interval or on the entire number line, then there will not be any endpoints so there will not be any endpoint critical numbers to worry
about.
We can now give a very succinct description of where to look for extreme values of a function:
The critical numbers only give the possible locations of extremes, and some critical numbers are not the locations of extremes. The critical numbers are the candidates for the locations of maximums and minimums (Fig. 10). Section 3.5 is devoted entirely to translating and solving maximum and minimum problems.
Fig. 10
Which Functions Have Extremes?
So far we have concentrated on finding the extreme values of functions, but some functions don't have extreme values. Example 2 showed that did not have a maximum or minimum.
Example 5: Find the extreme values of .
Solution: Since for all , the first theorem in this section guarantees that has no extreme values. The function does not have a maximum or
minimum on the real number line.
The difficulty with the previous function was that the domain was so large that we could always make the function larger or smaller than any given value. The next example shows that we can encounter the same difficulty even on a small interval.
Example 6: Show that (Fig. 11) does not have a maximum or minimum on the interval .
Fig. 11
Solution: is continuous for all so is continuous on the interval For , (Fig.11). For any number a strictly between and , we can show
that is neither a maximum nor a minimum of on . Pick to be any number between and , . Then ,
so is not a maximum. Similarly, pick to be any number between and , . Then ,
so is not a minimum. The interval is not large, but still does not have an extreme value in .
The Extreme Value Theorem gives conditions so that a function is guaranteed to have a maximum and a minimum.
Extreme Value Theorem: If is continuous on a closed interval ,
then attains both a maximum and minimum on .
The proof of this theorem is difficult and is omitted. Fig. 12 illustrates some of the possibilities for continuous and discontinuous functions on open and closed intervals. The Extreme Value Theorem guarantees that certain functions (continuous) on certain
intervals (closed) must have maximums and minimums. Other functions on other intervals may or may not have maximums and minimums.
Fig. 12
Practice Answers
Practice 1: The enrollments were relative maximums in , , and .
The global maximum was in . The enrollments were relative minimums in , , and . The global minimum occurred in .
Practice 2: is a polynomial so is differentiable for all , and . when so the only candidate for a local extreme is . Since the graph of is a parabola opening up, the point is a local minimum.
is a polynomial so is differentiable for all , and . when so the only candidates for a local extreme are and . The graph of (Fig. 22) shows that has a local maximum at and a local minimum at .
Fig. 22
Practice 3:
see Fig. 23
Fig. 23
Practice 4: (1, ) is a local minimum. is an endpoint.
(3, ) is a local minimum. is not differentiable at .
(4, ) is a local maximum. is an endpoint.
Critical points: endpoints and .