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3.4: Applied Optimization

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    Learning Objectives

    In this section, we strive to understand the ideas generated by the following important questions:

    • In a setting where a situation is described for which optimal parameters are sought, how do we develop a function that models the situation and use calculus to find the desired maximum or minimum?

    Near the conclusion of Section 3.3, we considered two examples of optimization problems where determining the function to be optimized was part of a broader question. In Example 3.4, we sought to use a single piece of wire to build two geometric figures (an equilateral triangle and square) and to understand how various choices for how to cut the wire led to different values of the area enclosed. One of our conclusions was that in order to maximize the total combined area enclosed by the triangle and square, all of the wire must be used to make a square. In the subsequent Activity 3.9, we investigated how the volume of a box constructed from a piece of cardboard by removing squares from each corner and folding up the sides depends on the size of the squares removed.

    Both of these problems exemplify situations where there is not a function explicitly provided to optimize. Rather, we first worked to understand the given information in the problem, drawing a figure and introducing variables, and then sought to develop a formula for a function that models the quantity (area or volume, in the two examples, respectively) to be optimized. Once the function was established, we then considered what domain was appropriate on which to pursue the desired absolute minimum or maximum (or both). At this point in the problem, we are finally ready to apply the ideas of calculus to determine and justify the absolute minimum or maximum. Thus, what is primarily different about problems of this type is that the problem-solver must do considerable work to introduce variables and develop the correct function and domain to represent the described situation.

    Throughout what follows in the current section, the primary emphasis is on the reader solving problems. Initially, some substantial guidance is provided, with the problems progressing to require greater independence as we move along.

    Preview Activity \(\PageIndex{1}\)

    According to U.S. postal regulations, the girth plus the length of a parcel sent by mail may not exceed 108 inches, where by “girth” we mean the perimeter of the smallest end. What is the largest possible volume of a rectangular parcel with a square 192 end that can be sent by mail? What are the dimensions of the package of largest volume?


    Figure \(\PageIndex{1}\): A rectangular parcel with a square end.

    1. Let \(x\) represent the length of one side of the square end and y the length of the longer side. Label these quantities appropriately on the image shown in Figure \(\PageIndex{1}\).
    2. What is the quantity to be optimized in this problem? Find a formula for this quantity in terms of \(x\) and \(y\).
    3. The problem statement tells us that the parcel’s girth plus length may not exceed 108 inches. In order to maximize volume, we assume that we will actually need the girth plus length to equal 108 inches. What equation does this produce involving \(x\) and \(y\)?
    4. Solve the equation you found in (c) for one of \(x\) or \(y\) (whichever is easier).
    5. Now use your work in (b) and (d) to determine a formula for the volume of the parcel so that this formula is a function of a single variable.
    6. Over what domain should we consider this function? Note that both \(x\) and \(y\) must be positive; how does the constraint that girth plus length is 108 inches produce intervals of possible values for \(x\) and \(y\)?
    7. Find the absolute maximum of the volume of the parcel on the domain you established in (f) and hence also determine the dimensions of the box of greatest volume. Justify that you’ve found the maximum using calculus.

    More Applied Optimization Problems

    Many of the steps in Preview Activity \(\PageIndex{1}\) are ones that we will execute in any applied optimization problem. We briefly summarize those here to provide an overview of our approach in subsequent questions.

    • Draw a picture and introduce variables. It is essential to first understand what quantities are allowed to vary in the problem and then to represent those values with variables. Constructing a figure with the variables labeled is almost always an essential first step. Sometimes drawing several diagrams can be especially helpful to get a sense of the situation. A nice example of this can be seen at, where the choice of where to bend a piece of wire into the shape of a rectangle determines both the rectangle’s shape and area.
    • Identify the quantity to be optimized as well as any key relationships among the variable quantities. Essentially this step involves writing equations that involve the variables that have been introduced: one to represent the quantity whose minimum or maximum is sought, and possibly others that show how multiple variables in the problem may be interrelated.
    • Determine a function of a single variable that models the quantity to be optimized; this may involve using other relationships among variables to eliminate one or more variables in the function formula. For example, in Preview Activity \(\PageIndex{1}\), we initially found that \(V = x^2 y\), but then the additional relationship that \(4x + y = 108\) (girth plus length equals 108 inches) allows us to relate \(x\) and \(y\) and thus observe equivalently that \(y = 108 − 4x\). Substituting for \(y\) in the volume equation yields \(V(x) = x^2 (108−4x)\), and thus we have written the volume as a function of the single variable \(x\).
    • Decide the domain on which to consider the function being optimized. Often the physical constraints of the problem will limit the possible values that the independent variable can take on. Thinking back to the diagram describing the overall situation and any relationships among variables in the problem often helps identify the smallest and largest values of the input variable.
    • Use calculus to identify the absolute maximum and/or minimum of the quantity being optimized. This always involves finding the critical numbers of the function first. Then, depending on the domain, we either construct a first derivative sign chart (for an open or unbounded interval) or evaluate the function at the endpoints and critical numbers (for a closed, bounded interval), using ideas we’ve studied so far in Chapter 3.
    • Finally, we make certain we have answered the question: does the question seek the absolute maximum of a quantity, or the values of the variables that produce the maximum? That is, finding the absolute maximum volume of a parcel is different from finding the dimensions of the parcel that produce the maximum.

    Activity \(\PageIndex{1}\): Soup Prices

    A soup can in the shape of a right circular cylinder is to be made from two materials. The material for the side of the can costs $0.015 per square inch and the material for the lids costs $0.027 per square inch. Suppose that we desire to construct a can that has a volume of 16 cubic inches. What dimensions minimize the cost of the can?

    1. Draw a picture of the can and label its dimensions with appropriate variables.
    2. Use your variables to determine expressions for the volume, surface area, and cost of the can.
    3. Determine the total cost function as a function of a single variable. What is the domain on which you should consider this function?
    4. Find the absolute minimum cost and the dimensions that produce this value.

    Familiarity with common geometric formulas is particularly helpful in problems like the one in Activity \(\PageIndex{1}\). Sometimes those involve perimeter, area, volume, or surface area. At other times, the constraints of a problem introduce right triangles (where the Pythagorean Theorem applies) or other functions whose formulas provide relationships among variables present.

    Activity \(\PageIndex{2}\): Optimized Hikes I

    A hiker starting at a point P on a straight road walks east towards point Q, which is on the road and 3 kilometers from point P. Two kilometers due north of point Q is a cabin. The hiker will walk down the road for a while, at a pace of 8 kilometers per hour. At some point Z between P and Q, the hiker leaves the road and makes a straight line towards the cabin through the woods, hiking at a pace of 3 kph, as pictured in Figure \(\PageIndex{2}\). In order to minimize the time to go from P to Z to the cabin, where should the hiker turn into the forest?


    Figure \(\PageIndex{2}\): A hiker walks from P to Z to the cabin, as pictured.

    In more geometric problems, we often use curves or functions to provide natural constraints. For instance, we could investigate which isosceles triangle that circumscribes a unit circle has the smallest area, which you can explore for yourself at Or similarly, for a region bounded by a parabola, we might seek the rectangle of largest area that fits beneath the curve, as shown at The next activity is similar to the latter problem.

    Activity \(\PageIndex{2}\): Optimized Hikes II

    Consider the region in the \(x-y\) plane that is bounded by the x-axis and the function \(f (x) = 25 − x^2\). Construct a rectangle whose base lies on the \(x\)-axis and is centered at the origin, and whose sides extend vertically until they intersect the curve \(y = 25 − x^2\). Which such rectangle has the maximum possible area? Which such rectangle has the greatest perimeter? Which has the greatest combined perimeter and area? (Challenge: answer the same questions in terms of positive parameters \(a\) and \(b\) for the function \(f (x) = b − ax^2\).)

    Activity \(\PageIndex{3}\): Maximized Volume

    A trough is being constructed by bending a 4 × 24 (measured in feet) rectangular piece of sheet metal. Two symmetric folds 2 feet apart will be made parallel to the longest side of the rectangle so that the trough has cross-sections in the shape of a trapezoid, as pictured in Figure \(\PageIndex{3}\). At what angle should the folds be made to produce the trough of maximum volume?


    Figure \(\PageIndex{3}\): A cross-section of the trough formed by folding to an angle of θ.


    In this section, we encountered the following important ideas:

    • While there is no single algorithm that works in every situation where optimization is used, in most of the problems we consider, the following steps are helpful: draw a picture and introduce variables; identify the quantity to be optimized and find relationships among the variables; determine a function of a single variable that models the quantity to be optimized; decide the domain on which to consider the function being optimized; use calculus to identify the absolute maximum and/or minimum of the quantity being optimized.

    Contributors and Attributions

    This page titled 3.4: Applied Optimization is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Matthew Boelkins, David Austin & Steven Schlicker (ScholarWorks @Grand Valley State University) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.

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