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Calculus Volume 3

4.7 Maxima/Minima Problems

Calculus Volume 34.7 Maxima/Minima Problems
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  1. Preface
  2. 1 Parametric Equations and Polar Coordinates
    1. Introduction
    2. 1.1 Parametric Equations
    3. 1.2 Calculus of Parametric Curves
    4. 1.3 Polar Coordinates
    5. 1.4 Area and Arc Length in Polar Coordinates
    6. 1.5 Conic Sections
    7. Key Terms
    8. Key Equations
    9. Key Concepts
    10. Chapter Review Exercises
  3. 2 Vectors in Space
    1. Introduction
    2. 2.1 Vectors in the Plane
    3. 2.2 Vectors in Three Dimensions
    4. 2.3 The Dot Product
    5. 2.4 The Cross Product
    6. 2.5 Equations of Lines and Planes in Space
    7. 2.6 Quadric Surfaces
    8. 2.7 Cylindrical and Spherical Coordinates
    9. Key Terms
    10. Key Equations
    11. Key Concepts
    12. Chapter Review Exercises
  4. 3 Vector-Valued Functions
    1. Introduction
    2. 3.1 Vector-Valued Functions and Space Curves
    3. 3.2 Calculus of Vector-Valued Functions
    4. 3.3 Arc Length and Curvature
    5. 3.4 Motion in Space
    6. Key Terms
    7. Key Equations
    8. Key Concepts
    9. Chapter Review Exercises
  5. 4 Differentiation of Functions of Several Variables
    1. Introduction
    2. 4.1 Functions of Several Variables
    3. 4.2 Limits and Continuity
    4. 4.3 Partial Derivatives
    5. 4.4 Tangent Planes and Linear Approximations
    6. 4.5 The Chain Rule
    7. 4.6 Directional Derivatives and the Gradient
    8. 4.7 Maxima/Minima Problems
    9. 4.8 Lagrange Multipliers
    10. Key Terms
    11. Key Equations
    12. Key Concepts
    13. Chapter Review Exercises
  6. 5 Multiple Integration
    1. Introduction
    2. 5.1 Double Integrals over Rectangular Regions
    3. 5.2 Double Integrals over General Regions
    4. 5.3 Double Integrals in Polar Coordinates
    5. 5.4 Triple Integrals
    6. 5.5 Triple Integrals in Cylindrical and Spherical Coordinates
    7. 5.6 Calculating Centers of Mass and Moments of Inertia
    8. 5.7 Change of Variables in Multiple Integrals
    9. Key Terms
    10. Key Equations
    11. Key Concepts
    12. Chapter Review Exercises
  7. 6 Vector Calculus
    1. Introduction
    2. 6.1 Vector Fields
    3. 6.2 Line Integrals
    4. 6.3 Conservative Vector Fields
    5. 6.4 Green’s Theorem
    6. 6.5 Divergence and Curl
    7. 6.6 Surface Integrals
    8. 6.7 Stokes’ Theorem
    9. 6.8 The Divergence Theorem
    10. Key Terms
    11. Key Equations
    12. Key Concepts
    13. Chapter Review Exercises
  8. 7 Second-Order Differential Equations
    1. Introduction
    2. 7.1 Second-Order Linear Equations
    3. 7.2 Nonhomogeneous Linear Equations
    4. 7.3 Applications
    5. 7.4 Series Solutions of Differential Equations
    6. Key Terms
    7. Key Equations
    8. Key Concepts
    9. Chapter Review Exercises
  9. A | Table of Integrals
  10. B | Table of Derivatives
  11. C | Review of Pre-Calculus
  12. Answer Key
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
  13. Index

Learning Objectives

  • 4.7.1. Use partial derivatives to locate critical points for a function of two variables.
  • 4.7.2. Apply a second derivative test to identify a critical point as a local maximum, local minimum, or saddle point for a function of two variables.
  • 4.7.3. Examine critical points and boundary points to find absolute maximum and minimum values for a function of two variables.

One of the most useful applications for derivatives of a function of one variable is the determination of maximum and/or minimum values. This application is also important for functions of two or more variables, but as we have seen in earlier sections of this chapter, the introduction of more independent variables leads to more possible outcomes for the calculations. The main ideas of finding critical points and using derivative tests are still valid, but new wrinkles appear when assessing the results.

Critical Points

For functions of a single variable, we defined critical points as the values of the function when the derivative equals zero or does not exist. For functions of two or more variables, the concept is essentially the same, except for the fact that we are now working with partial derivatives.

Definition

Let z=f(x,y)z=f(x,y) be a function of two variables that is defined on an open set containing the point (x0,y0).(x0,y0). The point (x0,y0)(x0,y0) is called a critical point of a function of two variables ff if one of the two following conditions holds:

  1. fx(x0,y0)=fy(x0,y0)=0fx(x0,y0)=fy(x0,y0)=0
  2. Either fx(x0,y0)orfy(x0,y0)fx(x0,y0)orfy(x0,y0) does not exist.

Example 4.38

Finding Critical Points

Find the critical points of each of the following functions:

  1. f(x,y)=4y29x2+24y+36x+36f(x,y)=4y29x2+24y+36x+36
  2. g(x,y)=x2+2xy4y2+4x6y+4g(x,y)=x2+2xy4y2+4x6y+4
Checkpoint 4.34

Find the critical point of the function f(x,y)=x3+2xy2x4y.f(x,y)=x3+2xy2x4y.

The main purpose for determining critical points is to locate relative maxima and minima, as in single-variable calculus. When working with a function of one variable, the definition of a local extremum involves finding an interval around the critical point such that the function value is either greater than or less than all the other function values in that interval. When working with a function of two or more variables, we work with an open disk around the point.

Definition

Let z=f(x,y)z=f(x,y) be a function of two variables that is defined and continuous on an open set containing the point (x0,y0).(x0,y0). Then f has a local maximum at (x0,y0)(x0,y0) if

f(x0,y0)f(x,y)f(x0,y0)f(x,y)

for all points (x,y)(x,y) within some disk centered at (x0,y0).(x0,y0). The number f(x0,y0)f(x0,y0) is called a local maximum value. If the preceding inequality holds for every point (x,y)(x,y) in the domain of f,f, then ff has a global maximum (also called an absolute maximum) at (x0,y0).(x0,y0).

The function ff has a local minimum at (x0,y0)(x0,y0) if

f(x0,y0)f(x,y)f(x0,y0)f(x,y)

for all points (x,y)(x,y) within some disk centered at (x0,y0).(x0,y0). The number f(x0,y0)f(x0,y0) is called a local minimum value. If the preceding inequality holds for every point (x,y)(x,y) in the domain of f,f, then ff has a global minimum (also called an absolute minimum) at (x0,y0).(x0,y0).

If f(x0,y0)f(x0,y0) is either a local maximum or local minimum value, then it is called a local extremum (see the following figure).

The function z = the square root of (16 – x2 – y2) is shown, which is the upper hemisphere of radius 4 with center at the origin. In the xy plane, the circle with radius 4 and center at the origin is highlighted; it has equation x2 + y2 = 16.
Figure 4.47 The graph of z=16x2y2z=16x2y2 has a maximum value when (x,y)=(0,0).(x,y)=(0,0). It attains its minimum value at the boundary of its domain, which is the circle x2+y2=16.x2+y2=16.

In Maxima and Minima, we showed that extrema of functions of one variable occur at critical points. The same is true for functions of more than one variable, as stated in the following theorem.

Theorem 4.16

Fermat’s Theorem for Functions of Two Variables

Let z=f(x,y)z=f(x,y) be a function of two variables that is defined and continuous on an open set containing the point (x0,y0).(x0,y0). Suppose fxfx and fyfy each exists at (x0,y0).(x0,y0). If ff has a local extremum at (x0,y0),(x0,y0), then (x0,y0)(x0,y0) is a critical point of f.f.

Second Derivative Test

Consider the function f(x)=x3.f(x)=x3. This function has a critical point at x=0,x=0, since f(0)=3(0)2=0.f(0)=3(0)2=0. However, ff does not have an extreme value at x=0.x=0. Therefore, the existence of a critical value at x=x0x=x0 does not guarantee a local extremum at x=x0.x=x0. The same is true for a function of two or more variables. One way this can happen is at a saddle point. An example of a saddle point appears in the following figure.

The function z = x2 – y2 is shown, which is roughly saddle looking, with the function achieving maxima along the x and y axes.
Figure 4.48 Graph of the function z=x2y2.z=x2y2. This graph has a saddle point at the origin.

In this graph, the origin is a saddle point. This is because the first partial derivatives of f(x,y)=x2y2f(x,y)=x2y2 are both equal to zero at this point, but it is neither a maximum nor a minimum for the function. Furthermore the vertical trace corresponding to y=0y=0 is z=x2z=x2 (a parabola opening upward), but the vertical trace corresponding to x=0x=0 is z=y2z=y2 (a parabola opening downward). Therefore, it is both a global maximum for one trace and a global minimum for another.

Definition

Given the function z=f(x,y),z=f(x,y), the point (x0,y0,f(x0,y0))(x0,y0,f(x0,y0)) is a saddle point if both f0(x0,y0)=0f0(x0,y0)=0 and fy(x0,y0)=0,fy(x0,y0)=0, but ff does not have a local extremum at (x0,y0).(x0,y0).

The second derivative test for a function of one variable provides a method for determining whether an extremum occurs at a critical point of a function. When extending this result to a function of two variables, an issue arises related to the fact that there are, in fact, four different second-order partial derivatives, although equality of mixed partials reduces this to three. The second derivative test for a function of two variables, stated in the following theorem, uses a discriminant DD that replaces f(x0)f(x0) in the second derivative test for a function of one variable.

Theorem 4.17

Second Derivative Test

Let z=f(x,y)z=f(x,y) be a function of two variables for which the first- and second-order partial derivatives are continuous on some disk containing the point (x0,y0).(x0,y0). Suppose fx(x0,y0)=0fx(x0,y0)=0 and fy(x0,y0)=0.fy(x0,y0)=0. Define the quantity

D=fxx(x0,y0)fyy(x0,y0)(fxy(x0,y0))2.D=fxx(x0,y0)fyy(x0,y0)(fxy(x0,y0))2.
4.43
  1. If D>0D>0 and fxx(x0,y0)>0,fxx(x0,y0)>0, then ff has a local minimum at (x0,y0).(x0,y0).
  2. If D>0D>0 and fxx(x0,y0)<0,fxx(x0,y0)<0, then ff has a local maximum at (x0,y0).(x0,y0).
  3. If D<0,D<0, then ff has a saddle point at (x0,y0).(x0,y0).
  4. If D=0,D=0, then the test is inconclusive.

See Figure 4.49.

This figure consists of three figures labeled a, b, and c. Figure a has two bulbous mounds pointing down, and the two extrema are listed as the local minima. Figure b has two bulbous mounds pointed up, and the two extrema are listed as the local maxima. Figure c is shaped like a saddle, and in the middle of the saddle, a point is marked as the saddle point.
Figure 4.49 The second derivative test can often determine whether a function of two variables has a local minima (a), a local maxima (b), or a saddle point (c).

To apply the second derivative test, it is necessary that we first find the critical points of the function. There are several steps involved in the entire procedure, which are outlined in a problem-solving strategy.

Problem-Solving Strategy: Using the Second Derivative Test for Functions of Two Variables

Let z=f(x,y)z=f(x,y) be a function of two variables for which the first- and second-order partial derivatives are continuous on some disk containing the point (x0,y0).(x0,y0). To apply the second derivative test to find local extrema, use the following steps:

  1. Determine the critical points (x0,y0)(x0,y0) of the function ff where fx(x0,y0)=fy(x0,y0)=0.fx(x0,y0)=fy(x0,y0)=0. Discard any points where at least one of the partial derivatives does not exist.
  2. Calculate the discriminant D=fxx(x0,y0)fyy(x0,y0)(fxy(x0,y0))2D=fxx(x0,y0)fyy(x0,y0)(fxy(x0,y0))2 for each critical point of f.f.
  3. Apply Second Derivative Test to determine whether each critical point is a local maximum, local minimum, or saddle point, or whether the theorem is inconclusive.

Example 4.39

Using the Second Derivative Test

Find the critical points for each of the following functions, and use the second derivative test to find the local extrema:

  1. f(x,y)=4x2+9y2+8x36y+24f(x,y)=4x2+9y2+8x36y+24
  2. g(x,y)=13x3+y2+2xy6x3y+4g(x,y)=13x3+y2+2xy6x3y+4
Checkpoint 4.35

Use the second derivative to find the local extrema of the function

f(x,y)=x3+2xy6x4y2.f(x,y)=x3+2xy6x4y2.

Absolute Maxima and Minima

When finding global extrema of functions of one variable on a closed interval, we start by checking the critical values over that interval and then evaluate the function at the endpoints of the interval. When working with a function of two variables, the closed interval is replaced by a closed, bounded set. A set is bounded if all the points in that set can be contained within a ball (or disk) of finite radius. First, we need to find the critical points inside the set and calculate the corresponding critical values. Then, it is necessary to find the maximum and minimum value of the function on the boundary of the set. When we have all these values, the largest function value corresponds to the global maximum and the smallest function value corresponds to the absolute minimum. First, however, we need to be assured that such values exist. The following theorem does this.

Theorem 4.18

Extreme Value Theorem

A continuous function f(x,y)f(x,y) on a closed and bounded set DD in the plane attains an absolute maximum value at some point of DD and an absolute minimum value at some point of D.D.

Now that we know any continuous function ff defined on a closed, bounded set attains its extreme values, we need to know how to find them.

Theorem 4.19

Finding Extreme Values of a Function of Two Variables

Assume z=f(x,y)z=f(x,y) is a differentiable function of two variables defined on a closed, bounded set D.D. Then ff will attain the absolute maximum value and the absolute minimum value, which are, respectively, the largest and smallest values found among the following:

  1. The values of ff at the critical points of ff in D.D.
  2. The values of ff on the boundary of D.D.

The proof of this theorem is a direct consequence of the extreme value theorem and Fermat’s theorem. In particular, if either extremum is not located on the boundary of D,D, then it is located at an interior point of D.D. But an interior point (x0,y0)(x0,y0) of DD that’s an absolute extremum is also a local extremum; hence, (x0,y0)(x0,y0) is a critical point of ff by Fermat’s theorem. Therefore the only possible values for the global extrema of ff on DD are the extreme values of ff on the interior or boundary of D.D.

Problem-Solving Strategy: Finding Absolute Maximum and Minimum Values

Let z=f(x,y)z=f(x,y) be a continuous function of two variables defined on a closed, bounded set D,D, and assume ff is differentiable on D.D. To find the absolute maximum and minimum values of ff on D,D, do the following:

  1. Determine the critical points of ff in D.D.
  2. Calculate ff at each of these critical points.
  3. Determine the maximum and minimum values of ff on the boundary of its domain.
  4. The maximum and minimum values of ff will occur at one of the values obtained in steps 2and3.2and3.

Finding the maximum and minimum values of ff on the boundary of DD can be challenging. If the boundary is a rectangle or set of straight lines, then it is possible to parameterize the line segments and determine the maxima on each of these segments, as seen in Example 4.40. The same approach can be used for other shapes such as circles and ellipses.

If the boundary of the set DD is a more complicated curve defined by a function g(x,y)=cg(x,y)=c for some constant c,c, and the first-order partial derivatives of gg exist, then the method of Lagrange multipliers can prove useful for determining the extrema of ff on the boundary. The method of Lagrange multipliers is introduced in Lagrange Multipliers.

Example 4.40

Finding Absolute Extrema

Use the problem-solving strategy for finding absolute extrema of a function to determine the absolute extrema of each of the following functions:

  1. f(x,y)=x22xy+4y24x2y+24f(x,y)=x22xy+4y24x2y+24 on the domain defined by 0x40x4 and 0y20y2
  2. g(x,y)=x2+y2+4x6yg(x,y)=x2+y2+4x6y on the domain defined by x2+y216x2+y216
Checkpoint 4.36

Use the problem-solving strategy for finding absolute extrema of a function to find the absolute extrema of the function

f(x,y)=4x22xy+6y28x+2y+3f(x,y)=4x22xy+6y28x+2y+3

on the domain defined by 0x20x2 and −1y3.−1y3.

Example 4.41

Chapter Opener: Profitable Golf Balls

A basket full of golf balls.
Figure 4.56 (credit: modification of work by oatsy40, Flickr)

Pro-TT company has developed a profit model that depends on the number x of golf balls sold per month (measured in thousands), and the number of hours per month of advertising y, according to the function

z=f(x,y)=48x+96yx22xy9y2,z=f(x,y)=48x+96yx22xy9y2,

where zz is measured in thousands of dollars. The maximum number of golf balls that can be produced and sold is 50,000,50,000, and the maximum number of hours of advertising that can be purchased is 25.25. Find the values of xx and yy that maximize profit, and find the maximum profit.

Section 4.7 Exercises

For the following exercises, find all critical points.

310.

f(x,y)=1+x2+y2f(x,y)=1+x2+y2

311.

f(x,y)=(3x2)2+(y4)2f(x,y)=(3x2)2+(y4)2

312.

f(x,y)=x4+y416xyf(x,y)=x4+y416xy

313.

f(x,y)=15x33xy+15y3f(x,y)=15x33xy+15y3

For the following exercises, find the critical points of the function by using algebraic techniques (completing the square) or by examining the form of the equation. Verify your results using the partial derivatives test.

314.

f(x,y)=x2+y2+1f(x,y)=x2+y2+1

315.

f(x,y)=x25y2+8x10y13f(x,y)=x25y2+8x10y13

316.

f(x,y)=x2+y2+2x6y+6f(x,y)=x2+y2+2x6y+6

317.

f(x,y)=x2+y2+1f(x,y)=x2+y2+1

For the following exercises, use the second derivative test to identify any critical points and determine whether each critical point is a maximum, minimum, saddle point, or none of these.

318.

f(x,y)=x3+4xy2y2+1f(x,y)=x3+4xy2y2+1

319.

f(x,y)=x2y2f(x,y)=x2y2

320.

f(x,y)=x26x+y2+4y8f(x,y)=x26x+y2+4y8

321.

f(x,y)=2xy+3x+4yf(x,y)=2xy+3x+4y

322.

f(x,y)=8xy(x+y)+7f(x,y)=8xy(x+y)+7

323.

f(x,y)=x2+4xy+y2f(x,y)=x2+4xy+y2

324.

f(x,y)=x3+y3300x75y3f(x,y)=x3+y3300x75y3

325.

f(x,y)=9x4y4f(x,y)=9x4y4

326.

f(x,y)=7x2y+9xy2f(x,y)=7x2y+9xy2

327.

f(x,y)=3x22xy+y28yf(x,y)=3x22xy+y28y

328.

f(x,y)=3x2+2xy+y2f(x,y)=3x2+2xy+y2

329.

f(x,y)=y2+xy+3y+2x+3f(x,y)=y2+xy+3y+2x+3

330.

f(x,y)=x2+xy+y23xf(x,y)=x2+xy+y23x

331.

f(x,y)=x2+2y2x2yf(x,y)=x2+2y2x2y

332.

f(x,y)=x2+yeyf(x,y)=x2+yey

333.

f(x,y)=e(x2+y2+2x)f(x,y)=e(x2+y2+2x)

334.

f(x,y)=x2+xy+y2xy+1f(x,y)=x2+xy+y2xy+1

335.

f(x,y)=x2+10xy+y2f(x,y)=x2+10xy+y2

336.

f(x,y)=x25y2+10x30y62f(x,y)=x25y2+10x30y62

337.

f(x,y)=120x+120yxyx2y2f(x,y)=120x+120yxyx2y2

338.

f(x,y)=2x2+2xy+y2+2x3f(x,y)=2x2+2xy+y2+2x3

339.

f(x,y)=x2+x3xy+y35f(x,y)=x2+x3xy+y35

340.

f(x,y)=2xyex2y2f(x,y)=2xyex2y2

For the following exercises, determine the extreme values and the saddle points. Use a CAS to graph the function.

341.

[T] f(x,y)=yexeyf(x,y)=yexey

342.

[T] f(x,y)=xsin(y)f(x,y)=xsin(y)

343.

[T] f(x,y)=sin(x)sin(y),x(0,2π),y(0,2π)f(x,y)=sin(x)sin(y),x(0,2π),y(0,2π)

Find the absolute extrema of the given function on the indicated closed and bounded set R.R.

344.

f(x,y)=xyx3y;f(x,y)=xyx3y; RR is the triangular region with vertices (0,0),(0,4),and(5,0).(0,0),(0,4),and(5,0).

345.

Find the absolute maximum and minimum values of f(x,y)=x2+y22y+1f(x,y)=x2+y22y+1 on the region R={(x,y)|x2+y24}.R={(x,y)|x2+y24}.

346.

f(x,y)=x33xyy3f(x,y)=x33xyy3 on R={(x,y):−2x2,−2y2}R={(x,y):−2x2,−2y2}

347.

f(x,y)=−2yx2+y2+1f(x,y)=−2yx2+y2+1 on R={(x,y):x2+y24}R={(x,y):x2+y24}

348.

Find three positive numbers the sum of which is 27,27, such that the sum of their squares is as small as possible.

349.

Find the points on the surface x2yz=5x2yz=5 that are closest to the origin.

350.

Find the maximum volume of a rectangular box with three faces in the coordinate planes and a vertex in the first octant on the plane x+y+z=1.x+y+z=1.

351.

The sum of the length and the girth (perimeter of a cross-section) of a package carried by a delivery service cannot exceed 108108 in. Find the dimensions of the rectangular package of largest volume that can be sent.

352.

A cardboard box without a lid is to be made with a volume of 44 ft3. Find the dimensions of the box that requires the least amount of cardboard.

353.

Find the point on the surface f(x,y)=x2+y2+10f(x,y)=x2+y2+10 nearest the plane x+2yz=0.x+2yz=0. Identify the point on the plane.

354.

Find the point in the plane 2xy+2z=162xy+2z=16 that is closest to the origin.

355.

A company manufactures two types of athletic shoes: jogging shoes and cross-trainers. The total revenue from xx units of jogging shoes and yy units of cross-trainers is given by R(x,y)=−5x28y22xy+42x+102y,R(x,y)=−5x28y22xy+42x+102y, where xx and yy are in thousands of units. Find the values of x and y to maximize the total revenue.

356.

A shipping company handles rectangular boxes provided the sum of the length, width, and height of the box does not exceed 9696 in. Find the dimensions of the box that meets this condition and has the largest volume.

357.

Find the maximum volume of a cylindrical soda can such that the sum of its height and circumference is 120120 cm.

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