University:
Whitman College
Course:
MATH-126 | Calculus II
Academic year:

2024
Views:

30

Pages:

310
Author:

pleonofficemate

Calculus Contents 1 Analytic Geometry 1.1 1.2 1.3 1.4 1 Lines . . . . . . . . . . . . . . . . Distance Between Two Points; Circles Functions . . . . . . . . . . . . . . Shifts and Dilations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 .7 .8 14 2 Instantaneous Rate Of Change: The Derivative 2.1 2.2 2.3 2.4 2.5 The slope of a function . An example . . . . . . . Limits . . . . . . . . . The Derivative Function Adjectives For Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 24 26 35 40 v vi Contents 3 Rules For Finding Derivatives 3.1 3.2 3.3 3.4 3.5 The Power Rule . . . . . Linearity of the Derivative The Product Rule . . . . The Quotient Rule . . . . The Chain Rule . . . . . . 45 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 48 50 53 56 4 Transcendental Functions 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 63 Trigonometric Functions . . . . . . . . . . . . . . . . The Derivative of sin x . . . . . . . . . . . . . . . . . A hard limit . . . . . . . . . . . . . . . . . . . . . . The Derivative of sin x, continued . . . . . . . . . . . . Derivatives of the Trigonometric Functions . . . . . . . Exponential and Logarithmic functions . . . . . . . . . Derivatives of the exponential and logarithmic functions Limits revisited . . . . . . . . . . . . . . . . . . . . . Implicit Differentiation . . . . . . . . . . . . . . . . . Inverse Trigonometric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 66 67 70 71 72 75 80 84 89 5 Curve Sketching 5.1 5.2 5.3 5.4 5.5 Maxima and Minima . . . . . . . . . . . The first derivative test . . . . . . . . . . The second derivative test . . . . . . . . Concavity and inflection points . . . . . . Asymptotes and Other Things to Look For 93 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 . . 97 . . 99 . 100 . 102 Contents vii 6 Applications of the Derivative 6.1 6.2 6.3 6.4 6.5 Optimization . . . . . . . Related Rates . . . . . . Newton’s Method . . . . . Linear Approximations . . The Mean Value Theorem 105 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 118 127 131 133 7 Integration 7.1 7.2 7.3 139 Two examples . . . . . . . . . . . . . . . . . . . . . . . . . The Fundamental Theorem of Calculus . . . . . . . . . . . . . Some Properties of Integrals . . . . . . . . . . . . . . . . . . 139 143 150 8 Techniques of Integration 8.1 8.2 8.3 8.4 8.5 8.6 Substitution . . . . . . . Powers of sine and cosine . Trigonometric Substitutions Integration by Parts . . . Rational Functions . . . . Additional exercises . . . . 155 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 160 162 166 170 176 viii Contents 9 Applications of Integration 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 9.11 177 Area between curves . . . . . . . Distance, Velocity, Acceleration . . Volume . . . . . . . . . . . . . . Average value of a function . . . . Work . . . . . . . . . . . . . . . Center of Mass . . . . . . . . . . Kinetic energy; improper integrals Probability . . . . . . . . . . . . Arc Length . . . . . . . . . . . . Surface Area . . . . . . . . . . . Differential equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 182 185 192 195 200 205 210 220 222 227 10 Sequences and Series 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 Sequences . . . . . . . . . Series . . . . . . . . . . . The Integral Test . . . . . Alternating Series . . . . . Comparison Tests . . . . . Absolute Convergence . . The Ratio and Root Tests Power Series . . . . . . . Calculus with Power Series Taylor Series . . . . . . . Taylor’s Theorem . . . . . Additional exercises . . . . 233 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 240 244 249 251 254 256 259 261 263 267 271 Contents ix A Introduction to Maple A.1 A.2 Getting Started . . . . . . . . . Algebra . . . . . . . . . . . . . A.2.1 Numbers . . . . . . . . . . A.2.2 Variables and Expressions . . A.2.3 Evaluation and Substitution A.2.4 Solving Equations . . . . . A.3 Plotting . . . . . . . . . . . . A.4 Calculus . . . . . . . . . . . . A.4.1 Limits . . . . . . . . . . . A.4.2 Differentiation . . . . . . . A.4.3 Implicit Differentiation . . . A.4.4 Integration . . . . . . . . . A.5 Adding text to a Maple session . A.6 Printing . . . . . . . . . . . . A.7 Saving your work . . . . . . . . A.8 Getting Help . . . . . . . . . . 275 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 . 277 . 279 . 280 . . . . . 284 . 284 . 285 . 285 . . . . . . . . 275 276 282 284 286 286 286 287 B Selected Answers 289 Index 303 Introduction The emphasis in this course is on problems—doing calculations and story problems. To master problem solving one needs a tremendous amount of practice doing problems. The more problems you do the better you will be at doing them, as patterns will start to emerge in both the problems and in successful approaches to them. You will learn fastest and best if you devote some time to doing problems every day. Typically the most difficult problems are story problems, since they require some effort before you can begin calculating. Here are some pointers for doing story problems: 1. Carefully read each problem twice before writing anything. 2. Assign letters to quantities that are described only in words; draw a diagram if appropriate. 3. Decide which letters are constants and which are variables. A letter stands for a constant if its value remains the same throughout the problem. 4. Using mathematical notation, write down what you know and then write down what you want to find. 5. Decide what category of problem it is (this might be obvious if the problem comes at the end of a particular chapter, but will not necessarily be so obvious if it comes on an exam covering several chapters). 6. Double check each step as you go along; don’t wait until the end to check your work. xi xii Introduction 7. Use common sense; if an answer is out of the range of practical possibilities, then check your work to see where you went wrong. Suggestions for Using This Text 1. Read the example problems carefully, filling in any steps that are left out (ask someone if you can’t follow the solution to a worked example). 2. Later use the worked examples to study by covering the solutions, and seeing if you can solve the problems on your own. 3. Most exercises have answers in Appendix B; the availability of an answer is marked by “⇒” at the end of the exercise. In the pdf version of the full text, clicking on the arrow will take you to the answer. The answers should be used only as a final check on your work, not as a crutch. Keep in mind that sometimes an answer could be expressed in various ways that are algebraically equivalent, so don’t assume that your answer is wrong just because it doesn’t have exactly the same form as the answer in the back. 4. A few figures in the book are marked with “(JA)” at the end of the caption. Clicking on this should open a related Java applet in your web browser. Some Useful Formulas Algebra Remember that the common algebraic operations have precedences relative to each other: for example, mulitplication and division take precedence over addition and subtraction, but are “tied” with each other. In the case of ties, work left to right. This means, for example, that 1/2x means (1/2)x: do the division, then the multiplication in left to right order. It sometimes is a good idea to use more parentheses than strictly necessary, for clarity, but it is also a bad idea to use too many parentheses. Completing the square: x2 + bx + c = (x + 2b )2 − b2 4 + c. √ −b ± b2 − 4ac Quadratic formula: the roots of ax2 + bx + c are . 2a Exponent rules: ab · ac = ab+c ab = ab−c c a b c (a ) = abc √ a1/b = b a Introduction xiii Geometry Circle: circumference = 2πr, area = πr2 . Sphere: vol = 4πr3 /3, surface area = 4πr2 . Cylinder: vol = πr2 h, lateral area = 2πrh, total surface area = 2πrh + 2πr2 . √ √ Cone: vol = πr2 h/3, lateral area = πr r2 + h2 , total surface area = πr r2 + h2 + πr2 . Analytic geometry Point-slope formula for straight line through the point (x0 , y0 ) with slope m: y = y0 + m(x − x0 ). Circle with radius r centered at (h, k): (x − h)2 + (y − k)2 = r2 . x2 y2 Ellipse with axes on the x-axis and y-axis: 2 + 2 = 1. a b Trigonometry sin(θ) = opposite/hypotenuse cos(θ) = adjacent/hypotenuse tan(θ) = opposite/adjacent sec(θ) = 1/ cos(θ) csc(θ) = 1/ sin(θ) cot(θ) = 1/ tan(θ) tan(θ) = sin(θ)/ cos(θ) cot(θ) = cos(θ)/ sin(θ) ¢ ¡ sin(θ) = cos π2 − θ ¡ ¢ cos(θ) = sin π2 − θ sin(θ + π) = − sin(θ) cos(θ + π) = − cos(θ) Law of cosines: a2 = b2 + c2 − 2bc cos A Law of sines: b c a = = sin A sin B sin C xiv Introduction Sine of sum of angles: sin(x + y) = sin x cos y + cos x sin y sin2 (θ) and cos2 (θ) formulas: sin2 (θ) + cos2 (θ) = 1 1 − cos(2θ) 2 1 + cos(2θ) cos2 (θ) = 2 sin2 (θ) = Cosine of sum of angles: cos(x + y) = cos x cos y − sin x sin y Tangent of sum of angles: tan(x + y) = tan x + tan y 1 − tan x tan y 1 Analytic Geometry Much of the mathematics in this chapter will be review for you. However, the examples will be oriented toward applications and so will take some thought. In the (x, y) coordinate system we normally write the x-axis horizontally, with positive numbers to the right of the origin, and the y-axis vertically, with positive numbers above the origin. That is, unless stated otherwise, we take “rightward” to be the positive xdirection and “upward” to be the positive y-direction. In a purely mathematical situation, we normally choose the same scale for the x- and y-axes. For example, the line joining the origin to the point (a, a) makes an angle of 45◦ with the x-axis (and also with the y-axis). But in applications, often letters other than x and y are used, and often different scales are chosen in the x and y directions. For example, suppose you drop something from a window, and you want to study how its height above the ground changes from second to second. It is natural to let the letter t denote the time (the number of seconds since the object was released) and to let the letter h denote the height. For each t (say, at one-second intervals) you have a corresponding height h. This information can be tabulated, and then plotted on the (t, h) coordinate plane, as shown in figure 1.1. We use the word “quadrant” for each of the four regions the plane is divided into: the first quadrant is where points have both coordinates positive, or the “northeast” portion of the plot, and the second, third, and fourth quadrants are counted off counterclockwise, so the second quadrant is the northwest, the third is the southwest, and the fourth is the southeast. 1 2 Chapter 1 Analytic Geometry seconds 0 1 2 3 4 meters 80 75.1 60.4 35.9 1.6 h 80 • 60 40 20 ....................................... ................. ............. ........... .......... ......... ......... ........ ........ ....... ....... ....... ....... ....... ...... ...... ...... ...... ...... ...... ...... ...... ...... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ... • • • • 0 1 Figure 1.1 2 3 t 4 A data plot. Suppose we have two points A(2, 1) and B(3, 3) in the (x, y)-plane. We often want to know the change in x-coordinate (also called the “horizontal distance”) in going from A to B. This is often written ∆x, where the meaning of ∆ (a capital delta in the Greek alphabet) is “change in”. (Thus, ∆x can be read as “change in x” although it usually is read as “delta x”. The point is that ∆x denotes a single number, and should not be interpreted as “delta times x”.) In our example, ∆x = 3 − 2 = 1. Similarly, the “change in y” is written ∆y. In our example, ∆y = 3−1 = 2, the difference between the y-coordinates of the two points. It is the vertical distance you have to move in going from A to B. The general formulas for the change in x and the change in y between a point (x1 , y1 ) and a point (x2 , y2 ) is: ∆x = x2 − x1 , ∆y = y2 − y1 . Note that either or both of these might be negative. 1.1 Lines If we have two points A(x1 , y1 ) and B(x2 , y2 ), then we can draw one and only one line through both points. By the slope of this line we mean the ratio of ∆y to ∆x. The slope is often denoted m: m = ∆y/∆x = (y2 − y1 )/(x2 − x1 ). For example, the line joining the points A and B in the last paragraph has slope 2. 1.1 Lines 3 EXAMPLE 1.1 According to the 1990 U.S. federal income tax schedules, a head of household paid 15% on taxable income up to $26050. If taxable income was between $26050 and $134930, then, in addition, 28% was to be paid on the amount between $26050 and $67200, and 33% paid on the amount over $67200 (if any). Interpret the tax bracket information (15%, 28%, or 33%) using mathematical terminology, and graph the tax on the y-axis against the taxable income on the x-axis. The percentages, when converted to decimal values 0.15, 0.28, and 0.33, are the slopes of the straight lines which form the graph of the tax for the corresponding tax brackets. The tax graph is what’s called a polygonal line, i.e., it’s made up of several straight line segments of different slopes. The first line starts at the point (0,0) and heads upward with slope 0.15 (i.e., it goes upward 15 for every increase of 100 in the x-direction), until it reaches the point above x = 26050. Then the graph “bends upward,” i.e., the slope changes to 0.28. As the horizontal coordinate goes from x = 26050 to x = 67200, the line goes upward 28 for each 100 in the x-direction. At x = 67200 the line turns upward again and continues with slope 0.33. See figure 1.2. 30000 20000 ....... ....... ........ . . . . . . . ....... ....... ....... ........ . . . . . . .. ....... ....... ....... ....... . . . . . . . ........ ........ ......... ......... . . . . . . . ......... ......... ........ ......... . . . . . . . ......... ......... ........ ......... . . . . . . . . .......... ............... ............... ............... . . . . . . . . . . . . . . . ............... • 10000 • 50000 Figure 1.2 100000 Tax vs. income. The most familiar form of the equation of a straight line is: y = mx + b. Here m is the slope of the line: if you increase x by 1, the equation tells you that you have to increase y by m. If you increase x by ∆x, then y increases by ∆y = m∆x. The number b is called the y-intercept, because it is where the line crosses the y-axis. If you know two points on a line, the formula m = (y2 − y1 )/(x2 − x1 ) gives you the slope. Once you know a point and the slope, then the y-intercept can be found by substituting the coordinates of either point in the equation: y1 = mx1 + b, i.e., b = y1 − mx1 . Alternatively, one can use the “point-slope” form of the equation of a straight line, which is: (y − y1 )/(x − x1 ) = m. This 4 Chapter 1 Analytic Geometry relation says that “between the point (x1 , y1 ) and any other point (x, y) on the line, the change in y divided by the change in x is the slope m of the line.” It is possible to find the equation of a line between two points directly from the relation (y − y1 )/(x − x1 ) = (y2 − y1 )/(x2 − x1 ), which says “the slope measured between the point (x1 , y1 ) and the point (x2 , y2 ) is the same as the slope measured between the point (x1 , y1 ) and any other point (x, y) on the line.” For example, if we want to find the equation of the line joining our earlier points A(2, 1) and B(3, 3), we can use this formula: y−1 3−1 = = 2, x−2 3−2 so that y − 1 = 2(x − 2), i.e., y = 2x − 3. The slope m of a line in the form y = mx + b tells us the direction in which the line is pointing. If m is positive, the line goes into the 1st quadrant as you go from left to right. If m is large and positive, it has a steep incline, while if m is small and positive, then the line has a small angle of inclination. If m is negative, the line goes into the 4th quadrant as you go from left to right. If m is a large negative number (large in absolute value), then the line points steeply downward; while if m is negative but near zero, then it points only a little downward. These four possibilities are illustrated in figure 1.3. ... ... ... . . ... ... ... . . ... ... ... . . .. ... ... ... . ... ... ... . . . ... 4 2 0 −2 −4 −4 −2 0 2 4 2 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 4 .......................... ................................ ............................... 2 0 0 −2 −2 −4 −4 −4 −2 Figure 1.3 0 2 4 −4 −2 0 2 4 2 ................................ ................................ .......................... 0 −2 −4 4 −4 −2 0 2 4 Lines with slopes 3, 0.1, −4, and −0.1. If m = 0, then the line is horizontal: its equation is simply y = b. There is one type of line that cannot be written in the form y = mx + b, namely, vertical lines. A vertical line has an equation of the form x = a. Sometimes one says that a vertical line has an “infinite” slope. Sometimes it is useful to find the x-intercept of a line y = mx + b. This is the x-value when y = 0. Setting mx + b equal to 0 and solving for x gives: x = −b/m. For example, the line y = 2x − 3 through the points A(2, 1) and B(3, 3) has x-intercept 1.5. EXAMPLE 1.2 Suppose that you are driving to Seattle at constant speed, and notice that after you have been traveling for 1 hour (i.e., t = 1), you pass a sign saying it is 110 1.1 Lines 5 miles to Seattle, and after driving another half-hour you pass a sign saying it is 85 miles to Seattle. Using the horizontal axis for the time t and the vertical axis for the distance y from Seattle, graph and find the equation y = mt + b for your distance from Seattle. Find the slope, y-intercept, and t-intercept, and describe the practical meaning of each. The graph of y versus t is a straight line because you are traveling at constant speed. The line passes through the two points (1, 110) and (1.5, 85). So its slope is m = (85 − 110)/(1.5 − 1) = −50. The meaning of the slope is that you are traveling at 50 mph; m is negative because you are traveling toward Seattle, i.e., your distance y is decreasing. The word “velocity” is often used for m = −50, when we want to indicate direction, while the word “speed” refers to the magnitude (absolute value) of velocity, which is 50 mph. To find the equation of the line, we use the point-slope formula: y − 110 = −50, t−1 so that y = −50(t − 1) + 110 = −50t + 160. The meaning of the y-intercept 160 is that when t = 0 (when you started the trip) you were 160 miles from Seattle. To find the t-intercept, set 0 = −50t+160, so that t = 160/50 = 3.2. The meaning of the t-intercept is the duration of your trip, from the start until you arrive in Seattle. After traveling 3 hours and 12 minutes, your distance y from Seattle will be 0. Exercises 1. Find the equation of the line through (1, 1) and (−5, −3) in the form y = mx + b. ⇒ 2. Find the equation of the line through (−1, 2) with slope −2 in the form y = mx + b. ⇒ 3. Find the equation of the line through (−1, 1) and (5, −3) in the form y = mx + b. ⇒ 4. Change the equation y − 2x = 2 to the form y = mx + b, graph the line, and find the y-intercept and x-intercept. ⇒ 5. Change the equation x+y = 6 to the form y = mx+b, graph the line, and find the y-intercept and x-intercept. ⇒ 6. Change the equation x = 2y − 1 to the form y = mx + b, graph the line, and find the y-intercept and x-intercept. ⇒ 7. Change the equation 3 = 2y to the form y = mx + b, graph the line, and find the y-intercept and x-intercept. ⇒ 8. Change the equation 2x + 3y + 6 = 0 to the form y = mx + b, graph the line, and find the y-intercept and x-intercept. ⇒ 9. Determine whether the lines 3x + 6y = 7 and 2x + 4y = 5 are parallel. ⇒ 10. Suppose a triangle in the x, y–plane has vertices (−1, 0), (1, 0) and (0, 2). Find the equations of the three lines that lie along the sides of the triangle in y = mx + b form. ⇒ 11. Suppose that you are driving to Seattle at constant speed. After you have been traveling for an hour you pass a sign saying it is 130 miles to Seattle, and after driving another 20 6 Chapter 1 Analytic Geometry minutes you pass a sign saying it is 105 miles to Seattle. Using the horizontal axis for the time t and the vertical axis for the distance y from your starting point, graph and find the equation y = mt + b for your distance from your starting point. How long does the trip to Seattle take? ⇒ 12. Let x stand for temperature in degrees Celsius (centigrade), and let y stand for temperature in degrees Fahrenheit. A temperature of 0◦ C corresponds to 32◦ F, and a temperature of 100◦ C corresponds to 212◦ F. Find the equation of the line that relates temperature Fahrenheit y to temperature Celsius x in the form y = mx + b. Graph the line, and find the point at which this line intersects y = x. What is the practical meaning of this point? ⇒ 13. A car rental firm has the following charges for a certain type of car: $25 per day with 100 free miles included, $0.15 per mile for more than 100 miles. Suppose you want to rent a car for one day, and you know you’ll use it for more than 100 miles. What is the equation relating the cost y to the number of miles x that you drive the car? ⇒ 14. A photocopy store advertises the following prices: 5/ c per copy for the first 20 copies, 4/ c per copy for the 21st through 100th copy, and 3/ c per copy after the 100th copy. Let x be the number of copies, and let y be the total cost of photocopying. (a) Graph the cost as x goes from 0 to 200 copies. (b) Find the equation in the form y = mx + b that tells you the cost of making x copies when x is more than 100. ⇒ 15. In the Kingdom of Xyg the tax system works as follows. Someone who earns less than 100 gold coins per month pays no tax. Someone who earns between 100 and 1000 golds coins pays tax equal to 10% of the amount over 100 gold coins that he or she earns. Someone who earns over 1000 gold coins must hand over to the King all of the money earned over 1000 in addition to the tax on the first 1000. (a) Draw a graph of the tax paid y versus the money earned x, and give formulas for y in terms of x in each of the regions 0 ≤ x ≤ 100, 100 ≤ x ≤ 1000, and x ≥ 1000. (b) Suppose that the King of Xyg decides to use the second of these line segments (for 100 ≤ x ≤ 1000) for x ≤ 100 as well. Explain in practical terms what the King is doing, and what the meaning is of the y-intercept. ⇒ 16. The tax for a single taxpayer is described in the figure below. Use this information to graph tax versus taxable income (i.e., x is the amount on Form 1040, line 37, and y is the amount on Form 1040, line 38). Find the slope and y-intercept of each line that makes up the polygonal graph, up to x = 97620. ⇒ 1990 Tax Rate Schedules Schedule X—Use if your filing status is Schedule Z—Use if your filing status is Single Head of household If the amount on Form 1040 line 37 is over: But not over: Enter on Form 1040 line 38 of the amount over: If the amount on Form 1040 line 37 is over: But not over: Enter on Form 1040 line 38 of the amount over: $0 19,450 47,050 $19,450 47,050 97,620 15% $2,917.50+28% $10,645.50+33% $0 19,450 47,050 $0 26,050 67,200 $26,050 67,200 134,930 15% $3,907.50+28% $15,429.50+33% $0 26,050 67,200 97,620 ............ Use Worksheet below to figure your tax 134,930 ............ Use Worksheet below to figure your tax 1.2 7 Distance Between Two Points; Circles 17. Market research tells you that if you set the price of an item at $1.50, you will be able to sell 5000 items; and for every 10 cents you lower the price below $1.50 you will be able to sell another 1000 items. Let x be the number of items you can sell, and let P be the price of an item. (a) Express P linearly in terms of x, in other words, express P in the form P = mx + b. (b) Express x linearly in terms of P . ⇒ 18. An instructor gives a 100-point final exam, and decides that a score 90 or above will be a grade of 4.0, a score of 40 or below will be a grade of 0.0, and between 40 and 90 the grading will be linear. Let x be the exam score, and let y be the corresponding grade. Find a formula of the form y = mx + b which applies to scores x between 40 and 90. ⇒ 1.2 Distance Between Two Points; Circles Given two points (x1 , y1 ) and (x2 , y2 ), recall that their horizontal distance from one another is ∆x = x2 −x1 and their vertical distance from one another is ∆y = y2 −y1 . (Actually, the word “distance” normally denotes “positive distance”. ∆x and ∆y are signed distances, but this is clear from context.) The actual (positive) distance from one point to the other is the length of the hypotenuse of a right triangle with legs ∆x and ∆y, as shown in figure 1.4. The Pythagorean theorem then says that the distance between the two points is the square root of the sum of the squares of the horizontal and vertical sides: distance = p p (∆x)2 + (∆y)2 = (x2 − x1 )2 + (y2 − y1 )2 . For example, the distance between points A(2, 1) and B(3, 3) is (x1 , y1 ) ...... ...... ...... . . . . . ..... ...... ...... ...... . . . . . ..... ...... ...... ...... . . . . . ...... ...... ...... ...... . . . . . .. ...... ...... ...... Figure 1.4 p (3 − 2)2 + (3 − 1)2 = √ 5. (x2 , y2 ) ∆y ∆x Distance between two points. As a special case of the distance formula, suppose we want to know the distance of a p point (x, y) to the origin. According to the distance formula, this is (x − 0)2 + (y − 0)2 = p x2 + y 2 . p A point (x, y) is at a distance r from the origin if and only if x2 + y 2 = r, or, if we square both sides: x2 + y 2 = r2 . This is the equation of the circle of radius r centered at the origin. The special case r = 1 is called the unit circle; its equation is x2 + y 2 = 1. 8 Chapter 1 Analytic Geometry Similarly, if C(h, k) is any fixed point, then a point (x, y) is at a distance r from the p point C if and only if (x − h)2 + (y − k)2 = r, i.e., if and only if (x − h)2 + (y − k)2 = r2 . This is the equation of the circle of radius r centered at the point (h, k). For example, the circle of radius 5 centered at the point (0, 5) on the y-axis has equation (x−0)2 +(y −5)2 = 25. If we expand (y − 5)2 = y 2 − 10y + 25 and cancel the 25 on both sides, we can rewrite this as: x2 + y 2 − 10y = 0. Exercises 1. Find the equation of the circle of radius 3 centered at: a) (0, 0) d) (0, 3) b) (5, 6) e) (0, −3) c) (−5, −6) f ) (3, 0) ⇒ 2. For each pair of points A(x1 , y1 ) and B(x2 , y2 ) find (i) ∆x and ∆y in going from A to B, (ii) the slope of the line joining A and B, (iii) the equation of the line joining A and B in the form y = mx + b, (iv) the distance from A to B, and (v) an equation of the circle with center at A that goes through B. a) A(2, 0), B(4, 3) d) A(−2, 3), B(4, 3) b) A(1, −1), B(0, 2) e) A(−3, −2), B(0, 0) c) A(0, 0), B(−2, −2) f ) A(0.01, −0.01), B(−0.01, 0.05) ⇒ 3. Graph the circle x2 + y 2 + 10y = 0. 4. Graph the circle x2 − 10x + y 2 = 24. 5. Graph the circle x2 − 6x + y 2 − 8y = 0. 6. Find the standard equation of the circle passing through (−2, 1) and tangent to the line 3x − 2y = 6 at the point (4, 3). Sketch. (Hint: The line through the center of the circle and the point of tangency is perpendicular to the tangent line.) ⇒ 1.3 Functions A function y = f (x) is a rule for determining y when you’re given a value of x. For example, the rule y = f (x) = 2x + 1 is a function. Any line y = mx + b is called a linear function. The graph of a function looks like a curve above (or below) the x-axis, where for any value of x the rule y = f (x) tells you how far to go above (or below) the x-axis to reach the curve. 1.3 Functions 9 Functions can be defined in various ways: by an algebraic formula or several algebraic formulas, by a graph, or by an experimentally determined table of values. (In the latter case, the table gives a bunch of points in the plane, which we then interpolate with a smooth curve.) Given a value of x, a function must give you at most one value of y. Thus, vertical lines are not functions. For example, the line x = 1 has infinitely many values of y if x = 1. It is also true that if x is any number not 1 there is no y which corresponds to x, but that is not a problem—only multiple y values is a problem. In addition to lines, another familiar example of a function is the parabola y = f (x) = 2 x . You can draw the graph of this function by taking various values of x (say, at regular intervals) and plotting the points (x, f (x)) = (x, x2 ). Then connect the points with a smooth curve. (See figure 1.5.) The two examples y = f (x) = 2x + 1 and y = f (x) = x2 are both functions which can be evaluated at any value of x from negative infinity to positive infinity. For many functions, however, it only makes sense to take x in some interval or outside of some “forbidden” region. The interval of x-values at which we’re allowed to evaluate the function is called the domain of the function. ... ... ... ... ... ... ... . .. ... .. .. .. .. .. . . ... .. ... .. ... ... ... .. . ... . .... ... ..... .... .... ...... ........................... y = f (x) = x2 ... ... .. .. .. .. ... ... ... ... .... ..... ....... .......... .................. ..... .............. .......... ......... ....... . . . . . .. ...... ..... .... ... . .. y = f (x) = Figure 1.5 √ .................... ........... ....... ...... ..... ... ... ... ... .. .. .. .. ... ... x y = f (x) = 1/x Some graphs. √ For example, the square-root function y = f (x) = x is the rule which says, given an x-value, take the nonnegative number whose square is x. This rule only makes sense if x is positive or zero. We say that the domain of this function is x ≥ 0, or more formally {x ∈ R | x ≥ 0}. Alternately, we can use interval notation, and write that the domain is [0, ∞). (In interval notation, square brackets mean that the endpoint is included, and a √ parenthesis means that the endpoint is not included.) The fact that the domain of y = x 10 Chapter 1 Analytic Geometry is x ≥ 0 means that in the graph of this function ((see figure 1.5) we have points (x, y) only above x-values on the right side of the x-axis. Another example of a function whose domain is not the entire x-axis is: y = f (x) = 1/x, the reciprocal function. We cannot substitute x = 0 in this formula. The function makes sense, however, for any nonzero x, so we take the domain to be: x 6= 0. The graph of this function does not have any point (x, y) with x = 0. As x gets close to 0 from either side, the graph goes off toward infinity. We call the vertical line x = 0 an asymptote. To summarize, two reasons why certain x-values are excluded from the domain of a function are that (i) we cannot divide by zero, and (ii) we cannot take the square root of a negative number. We will encounter some other ways in which functions might be undefined later. Another reason why the domain of a function might be restricted is that in a given situation the x-values outside of some range might have no practical meaning. For example, if y is the area of a square of side x, then we can write y = f (x) = x2 . In a purely mathematical context the domain of the function y = x2 is all x. But in the story-problem context of finding areas of squares, we restrict the domain to x > 0, because a square with negative or zero side makes no sense. In a problem in pure mathematics, we usually take the domain to be all values of x at which the formulas can be evaluated. But in a story problem there might be further restrictions on the domain because only certain values of x are of interest or make practical sense. In a story problem, often letters different from x and y are used. For example, the volume V of a sphere is a function of the radius r, given by the formula V = f (r) = 4/3πr3 . Also, letters different from f may be used. For example, if y is the velocity of something at time t, we write y = v(t) with the letter v (instead of f ) standing for the velocity function (and t playing the role of x). The letter playing the role of x is called the independent variable, and the letter playing the role of y is called the dependent variable (because its value “depends on” the value of the independent variable). In story problems, when one has to translate from English into mathematics, a crucial step is to determine what letters stand for variables. If only words and no letters are given, then you have to decide which letters to use. Some letters are traditional. For example, almost always, t stands for time. EXAMPLE 1.3 An open-top box is made from an a × b rectangular piece of cardboard by cutting out a square of side x from each of the four corners, and then folding the sides up and sealing them with duct tape. Find a formula for the volume V of the box as a function of x, and find the domain of this function. 1.3 Functions 11 Here the box we get will have height x and rectangular base of dimensions a − 2x by b − 2x. Thus, V = f (x) = x(a − 2x)(b − 2x). Here a and b are constants, and V is the variable that depends on x, i.e., V is playing the role of y. This formula makes mathematical sense for any x, but in the story problem the domain is much less. In the first place, x must be positive. In the second place, it must be less than half the length of either of the sides of the cardboard. Thus, the domain is 0 0. First method. Factor 4x − x2 as x(4 − x). The product of two numbers is positive when either both are positive or both are negative, i.e., if either x > 0 and 4 − x > 0, or else x < 0 and 4 − x < 0. The latter alternative is impossible, since if x is negative, then 4 − x is greater than 4, and so cannot be negative. As for the first alternative, the condition 4 − x > 0 can be rewritten (adding x to both sides) as 4 > x, so we need: x > 0 and 4 > x (this is sometimes combined in the form 4 > x > 0, or, equivalently, 0 < x < 4). In interval notation, this says that the domain is the interval (0, 4). Second method. Write 4x − x2 as −(x2 − 4x), and then complete the square, obtaining ³ ´ − (x − 2)2 − 4 = 4 − (x − 2)2 . For this to be positive we need (x − 2)2 < 4, which means that x − 2 must be less than 2 and greater than −2: −2 < x − 2 < 2. Adding 2 to everything gives 0 < x < 4. Both of these methods are equally correct; you may use either in a problem of this type. A function does not always have to be given by a single formula, as we have already seen (in the income tax problem, for example). For example, suppose that y = v(t) is the velocity function for a car which starts out from rest (zero velocity) at time t = 0; then increases its speed steadily to 20 m/sec, taking 10 seconds to do this; then travels at constant speed 20 m/sec for 15 seconds; and finally applies the brakes to decrease speed steadily to 0, taking 5 seconds to do this. The formula for y = v(t) is different in each of the three time intervals: first y = 2x, then y = 20, then y = −4x + 120. The graph of this function is shown in figure 1.7. Not all functions are given by formulas at all. A function can be given by an experimentally determined table of values, or by a description other than a formula. For example, the population y of the U.S. is a function of the time t: we can write y = f (t). This is a perfectly good function—you could graph it (up to the present) if you had data for various t—but you can’t find an algebraic formula for it. 1.3 Functions 13 v 20 10 0 ..................................................................................................................................................................................... ..... ... ..... ... ..... . . ... . .. ... ..... . . . ... . .. . . . ... . .. . . ... . . ... . ... . . . . . ... . . .. . . ... . . .. . . ... . . .. . ... . . . .. . ... . . . ... ... . . . . . ... . . . .. . ... . . . .. ... . . . . .. ... . . . . ... ... . . . . ... . . . . .. ... . . . . ... .. . . . . ... .. . . . . ... .. . . . . ... ... . . . ... . . . . . ... .. . . . . ... .. . . . . .. .. 10 Figure 1.7 25 t 30 A velocity function. Exercises Find the domain of each of the following functions: √ 1. y = f (x) = 2x − 3 ⇒ 2. y = f (x) = 1/(x + 1) ⇒ 3. y = f (x) = 1/(x2 − 1) ⇒ p 4. y = f (x) = −1/x ⇒ √ 5. y = f (x) = 3 x ⇒ √ 6. y = f (x) = 4 x ⇒ p 7. y = f (x) = r2 − (x − h)2 , where r and h are positive constants. ⇒ p 8. y = f (x) = 1 − (1/x) ⇒ p 9. y = f (x) = 1/ 1 − (3x)2 ⇒ √ 10. y = f (x) = x + 1/(x − 1) ⇒ √ 11. y = f (x) = 1/( x − 1) ⇒  2 12. Find the domain of h(x) = (x − 9)/(x − 3) x 6= 3 ⇒ 6 if x = 3. √ 13. Suppose f (x) = 3x − 9 and g(x) = x. What is the domain of the composition (g ◦ f )(x)? (Recall that composition is defined as (g ◦ f )(x) = g(f (x)).) What is the domain of (f ◦ g)(x)? ⇒ 14. A farmer wants to build a fence along a river. He has 500 feet of fencing and wants to enclose a rectangular pen on three sides (with the river providing the fourth side). If x is the length of the side perpendicular to the river, determine the area of the pen as a function of x. What is the domain of this function? ⇒ 15. A can in the shape of a cylinder is to be made with a total of 100 square centimeters of material in the side, top, and bottom; the manufacturer wants the can to hold the maximum possible volume. Write the volume as a function of the radius r of the can; find the domain of the function. ⇒ 14 Chapter 1 Analytic Geometry 16. A can in the shape of a cylinder is to be made to hold a volume of one liter (1000 cubic centimeters). The manufacturer wants to use the least possible material for the can. Write the surface area of the can (total of the top, bottom, and side) as a function of the radius r of the can; find the domain of the function. ⇒ 1.4 Shifts and Dilations Many functions in applications are built up from simple functions by inserting constants in various places. It is important to understand the effect such constants have on the appearance of the graph. Horizontal shifts. If you replace x by x − C everywhere it occurs in the formula for f (x), then the graph shifts over C to the right. (If C is negative, then this means that the graph shifts over |C| to the left.) For example, the graph of y = (x − 2)2 is the x2 -parabola shifted over to have its vertex at the point 2 on the x-axis. The graph of y = (x + 1)2 is the same parabola shifted over to the left so as to have its vertex at −1 on the x-axis. Vertical shifts. If you replace y by y − D, then the graph moves up D units. (If D is negative, then this means that the graph moves down |D| units.) If the formula is written in the form y = f (x) and if y is replaced by y − D to get y − D = f (x), we can equivalently move D to the other side of the equation and write y = f (x) + D. Thus, this principal can be stated: to get the graph of y = f (x) + D, take the graph of y = f (x) and move it D units up. For example, the function y = x2 − 4x = (x − 2)2 − 4 can be obtained from y = (x − 2)2 (see the last paragraph) by moving the graph 4 units down. The result is the x2 -parabola shifted 2 units to the right and 4 units down so as to have its vertex at the point (2, −4). Warning. Do not confuse f (x) + D and f (x + D). For example, if f (x) is the function x2 , then f (x) + 2 is the function x2 + 2, while f (x + 2) is the function (x + 2)2 = x2 + 4x + 4. EXAMPLE 1.6 Circles An important example of the above two principles starts 2 2 2 with the circle x + y = r . This is the circle of radius r centered at the origin. (As we √ saw, this is not a single function y = f (x), but rather two functions y = ± r2 − x2 put together; in any case, the two shifting principles apply to equations like this one that are not in the form y = f (x).) If we replace x by x − C and replace y by y − D—getting the equation (x − C)2 + (y − D)2 = r2 —the effect on the circle is to move it C to the right and D up, thereby obtaining the circle of radius r centered at the point (C, D). This tells us how to write the equation of any circle, not necessarily centered at the origin. We will later want to use two more principles concerning the effects of constants on the appearance of the graph of a function. 1.4 Shifts and Dilations 15 Horizontal dilation. If x is replaced by x/A in a formula and A > 1, then the effect on the graph is to expand it by a factor of A in the x-direction (away from the y-axis). If A is between 0 and 1 then the effect on the graph is to contract by a factor of 1/A (towards the y-axis). We use the word “dilate” in both cases. For example, replacing x by x/0.5 = 2x has the effect of contracting toward the y-axis by a factor of 2. If A is negative, we dilate by a factor of |A| and then flip about the y-axis. Thus, replacing x by −x has the effect of taking the mirror image of the graph √ with respect to the y-axis. For example, the function y = −x, which has domain x ≤ 0, √ is obtained by taking the graph of x and flipping it around the y-axis into the second quadrant. Vertical dilation. If y is replaced by y/B in a formula and B > 0, then the effect on the graph is to dilate it by a factor of B in the vertical direction. Note that if we have a function y = f (x), replacing y by y/B is equivalent to multiplying the function on the right by B: y = Bf (x). The effect on the graph is to expand the picture away from the x-axis by a factor of B if B > 1, to contract it toward the x-axis by a factor of 1/B if 0 < B < 1, and to dilate by |B| and then flip about the x-axis if B is negative. EXAMPLE 1.7 Ellipses A basic example of the two expansion principles is given by an ellipse of semimajor axis a and semiminor axis b. We get such an ellipse by starting with the unit circle—the circle of radius 1 centered at the origin, the equation of which is x2 + y 2 = 1—and dilating by a factor of a horizontally and by a factor of b vertically. To get the equation of the resulting ellipse, which crosses the x-axis at ±a and crosses the y-axis at ±b, we replace x by x/a and y by y/b in the equation for the unit circle. This gives ³ x ´2 ³ y ´2 + = 1. a b Finally, if you want to analyze a function that involves both shifts and dilations, it is usually simplest to work with the dilations first, and then the shifts. For instance, if you want to dilate a function by a factor of A in the x-direction and then shift C to the right, you do this by replacing x first by x/A and then by (x − C) in the formula. As an example, suppose that, after dilating our unit circle by a in the x-direction and by b in the y-direction to get the ellipse in the last paragraph, we then wanted to shift it a distance h to the right and a distance k upward, so as to be centered at the point (h, k). The new 16 Chapter 1 Analytic Geometry ellipse would have equation µ x−h a ¶2 µ + y−k b ¶2 = 1. Note well that this is different than first doing shifts by h and k and then dilations by a and b: ³x ´2 ³ y ´2 −h + − k = 1. a b 4 6 3 5 2 4 1 3 ...................... ............ ....... ...... ....... ..... ...... ..... ..... . . . . ..... . . . . ... .. . . ... . . ... . . . ... . . . ... . . . ... . . . ... . . . ... . .. ... . . ... . ... .... ... ... .. ... ... ... .. ... .. ... .... . ... .. ... ... . ... .. . ... .. ... .. ... ... . ... . . ... ... ... ... ... ... . ... . . ... ... ... ... ... ... ... . . .... ... ..... .... ..... ..... ...... ..... . . . ....... . . .......... ...... ........................... −1 −1 1 2 2 3 1 −2 0 ...................... ............ ....... ...... ....... ..... ...... ..... ..... . . . . ..... . . . . ... .. . . ... . . ... . . . ... . . . ... . . . ... . . . ... . . . ... . .. ... . . ... . ... .... ... ... .. ... ... ... .. ... .. ... .... . ... .. ... ... . ... .. . ... .. ... .. ... ... . ... . . ... ... ... ... ... ... . ... . . ... ... ... ... ... ... ... . . .... ... ..... .... ..... ..... ...... ..... . . . ....... . . .......... ...... ........................... 0 Figure 1.8 Ellipses: right. ` x−1 ´2 2 + ` y−1 ´2 3 = 1 on the left, 1 `x 2 −1 2 ´2 + 3 `y 3 4 ´2 − 1 = 1 on the Exercises √ √ Starting with the graph of y = x, the graph of y = 1/x, and the graph of y = 1 − x2 (the upper unit semicircle), sketch the graph of each of the following functions: √ 1. f (x) = x − 2 2. f (x) = −1 − 1/(x + 2) √ 3. f (x) = 4 + x + 2 4. y = f (x) = x/(1 − x) √ 5. y = f (x) = − −x p 6. f (x) = 2 + 1 − (x − 1)2 p 7. f (x) = −4 + −(x − 2) 1.4 8. f (x) = 2 p Shifts and Dilations 1 − (x/3)2 9. f (x) = 1/(x + 1) p 10. f (x) = 4 + 2 1 − (x − 5)2 /9 11. f (x) = 1 + 1/(x − 1) p 12. f (x) = 100 − 25(x − 1)2 + 2 The graph of f (x) is shown below. Sketch the graphs of the following functions. 13. y = f (x − 1) 2 14. y = 1 + f (x + 2) 15. y = 1 + 2f (x) 16. y = 2f (3x) 17. y = 2f (3(x − 2)) + 1 18. y = (1/2)f (3x − 3) 19. y = f (1 + x/3) + 2 1 0 .... ........... .... ..... ... ..... ... ..... ... ... ... ... .. ... . . . . . ... . ... ... ... ... . ... ... . ... . . . ... . ... . . .. . ... . . ... . ... ..... ....... .. .......... . ... .. .... . .. ... .... . .. .. .... .. 1 −1 2 3 17 2 Instantaneous Rate Of Change: The Derivative 2.1 The slope of a function Suppose that y is a function of x, say y = f (x). It is often necessary to know how sensitive the value of y is to small changes in x. √ EXAMPLE 2.1 Take, for example, y = f (x) = 625 − x2 (the upper semicircle of √ radius 25 centered at the origin). When x = 7, we find that y = 625 − 49 = 24. Suppose we want to know how much y changes when x increases a little, say to 7.1 or 7.01. In the case of a straight line y = mx+b, the slope m = ∆y/∆x measures the change in y per unit change in x. This can be interpreted as a measure of “sensitivity”; for example, if y = 100x + 5, a small change in x corresponds to a change one hundred times as large in y, so y is quite sensitive to changes in x. √ Let us look at the same ratio ∆y/∆x for our function y = f (x) = 625 − x2 when x changes from 7 to 7.1. Here ∆x = 7.1 − 7 = 0.1 is the change in x, and ∆y = f (x + ∆x) − f (x) = f (7.1) − f (7) p p = 625 − 7.12 − 625 − 72 ≈ 23.9706 − 24 = −0.0294. Thus, ∆y/∆x ≈ −0.0294/0.1 = −0.294. This means that y changes by less than one third the change in x, so apparently y is not very sensitive to changes in x at x = 7. We say “apparently” here because we don’t really know what happens between 7 and 7.1. Perhaps y changes dramatically as x runs through the values from 7 to 7.1, but at 7.1 y 19 20 Chapter 2 Instantaneous Rate Of Change: The Derivative just happens to be close to its value at 7. This is not in fact the case for this particular function, but we don’t yet know why. One way to interpret the above calculation is by reference to a line. We have computed the slope of the line through (7, 24) and (7.1, 23.9706), called a chord of the circle. In general, if we draw the chord from the point (7, 24) to a nearby point on the semicircle (7 + ∆x, f (7 + ∆x)), the slope of this chord is the so-called difference quotient p 625 − (7 + ∆x)2 − 24 f (7 + ∆x) − f (7) slope of chord = = . ∆x ∆x For example, if x changes only from 7 to 7.01, then the difference quotient (slope of the chord) is approximately equal to (23.997081 − 24)/0.01 = −0.2919. This is slightly less steep than the chord from (7, 24) to (7.1, 23.9706). As the second value 7 + ∆x moves in towards 7, the chord joining (7, f (7)) to (7 + ∆x, f (7 + ∆x)) shifts slightly. As indicated in figure 2.1, as ∆x gets smaller and smaller, the chord joining (7, 24) to (7 + ∆x, f (7 + ∆x)) gets closer and closer to the tangent line to the circle at the point (7, 24). (Recall that the tangent line is the line that just grazes the circle at that point, i.e., it doesn’t meet the circle at any second point.) Thus, as ∆x gets smaller and smaller, the slope ∆y/∆x of the chord gets closer and closer to the slope of the tangent line. This is actually quite difficult to see when ∆x is small, because of the scale of the graph. The values of ∆x used for the figure are 1, 5, 10 and 15, not really very small values. The tangent line is the one that is uppermost at the right hand endpoint. 25 20 15 10 5 ..... ..... ..... . ....... ......... ................................ ................................... ... ... .. . ................................................................................... ................ .................... ...................................... ................................. ..... ...... ......... ........ . ..... ....... ........... ................ ..... ...................... ....................... ..... ........... ......... ....................... . . . ..... . .. ..... ...................... ................ ........................................... .. ..... .. . ....... ..... ......... ................ ....... ..... ........... ......... ..... ........ ............. ..... ..... ....... ..... .... ........ ..... ....... ... ..... . . ....... . ..... . ..... ..... ..... ... ..... .. ..... ... ..... .. ........ ...... ....... ......... ... ..... ... ... ... ... ... ... ... ... ... ... . 5 Figure 2.1 10 15 20 25 Chords approximating the tangent line. So far we have found the slopes of two chords that should be close to the slope of the tangent line, but what is the slope of the tangent line exactly? Since the tangent line 2.1 The slope of a function 21 touches the circle at just one point, we will never be able to calculate its slope directly, using two “known” points on the line. What we need is a way to capture what happens to the slopes of the chords as they get “closer and closer” to the tangent line. Instead of looking at more particular values of ∆x, let’s see what happens if we do some algebra with the difference quotient using just ∆x. The slope of a chord from (7, 24) to a nearby point is given by p p p 625 − (7 + ∆x)2 − 24 625 − (7 + ∆x)2 − 24 625 − (7 + ∆x)2 + 24 p = ∆x ∆x 625 − (7 + ∆x)2 + 24 = 625 − (7 + ∆x)2 − 242 p ∆x( 625 − (7 + ∆x)2 + 24) 49 − 49 − 14∆x − ∆x2 p = ∆x( 625 − (7 + ∆x)2 + 24) = ∆x(−14 − ∆x) p ∆x( 625 − (7 + ∆x)2 + 24) −14 − ∆x =p 625 − (7 + ∆x)2 + 24 Now, can we tell by looking at this last formula what happens when ∆x gets very close to zero? The numerator clearly gets very close to −14 while the denominator gets very close to √ 625 − 72 + 24 = 48. Is the fraction therefore very close to −14/48 = −7/24 ∼ = −0.29167? It certainly seems reasonable, and in fact it is true: as ∆x gets closer and closer to zero, the difference quotient does in fact get closer and closer to −7/24, and so the slope of the tangent line is exactly −7/24. What about the slope of the tangent line at x = 12? Well, 12 can’t be all that different from 7; we just have to redo the calculation with 12 instead of 7. This won’t be hard, but it will be a bit tedious. What if we try to do all the algebra without using a specific value for x? Let’s copy from above, replacing 7 by x. We’ll have to do a bit more than that—for 22 Chapter 2 Instantaneous Rate Of Change: The Derivative √ example, the “24” in the calculation came from 625 − 72 , so we’ll need to fix that too. p √ 625 − (x + ∆x)2 − 625 − x2 = ∆x p p √ √ 625 − (x + ∆x)2 − 625 − x2 625 − (x + ∆x)2 + 625 − x2 p √ = ∆x 625 − (x + ∆x)2 + 625 − x2 = 625 − (x + ∆x)2 − 625 + x2 p √ ∆x( 625 − (x + ∆x)2 + 625 − x2 ) = 625 − x2 − 2x∆x − ∆x2 − 625 + x2 p √ ∆x( 625 − (x + ∆x)2 + 625 − x2 ) = ∆x( ∆x(−2x − ∆x) √ 625 − (x + ∆x)2 + 625 − x2 ) p −2x − ∆x √ =p 625 − (x + ∆x)2 + 625 − x2 Now what happens when ∆x is very close to zero? Again it seems apparent that the quotient will be very close to √ −2x −x −2x √ = √ =√ . 625 − x2 + 625 − x2 2 625 − x2 625 − x2 Replacing x by 7 gives −7/24, as before, and now we can easily do the computation for 12 or any other value of x between −25 and 25. √ So now we have a single, simple formula, −x/ 625 − x2 , that tells us the slope of the tangent line for any value of x. This slope, in turn, tells us how sensitive the value of y is to changes in the value of x. What do we call such a formula? That is, a formula with one variable, so that substituting an “input” value for the variable produces a new “output” value? This is a function. √ Starting with one function, 625 − x2 , we have derived, by means of some slightly nasty √ algebra, a new function, −x/ 625 − x2 , that gives us important information about the original function. This new function in fact is called the derivative of the original function. If the original is referred to as f or y then the derivative is often written f 0 or y 0 and √ pronounced “f prime” or “y prime”, so in this case we might write f 0 (x) = −x/ 625 − x2 . At a particular point, say x = 7, we say that f 0 (7) = −7/24 or “f prime of 7 is −7/24” or “the derivative of f at 7 is −7/24.” To summarize, we compute the derivative of f (x) by forming the difference quotient f (x + ∆x) − f (x) , ∆x 2.1 The slope of a function 23 which is the slope of a line, then we figure out what happens when ∆x gets very close to 0. We should note that in the particular case of a circle, there’s a simple way to find the derivative. Since the tangent to a circle at a point is perpendicular to the radius drawn to the point of contact, its slope is the negative reciprocal of the slope of the radius. The radius joining (0, 0) to (7, 24) has slope 24/7. Hence, the tangent line has slope −7/24. In √ √ general, a radius to the point (x, 625 − x2 ) has slope 625 − x2 /x, so the slope of the √ tangent line is −x/ 625 − x2 , as before. It is NOT always true that a tangent line is perpendicular to a line from the origin—don’t use this shortcut in any other circumstance. As above, and as you might expect, for different values of x we generally get different values of the derivative f 0 (x). Could it be that the derivative always has the same value? This would mean that the slope of f , or the slope of its tangent line, is the same everywhere. One curve that always has the same slope is a line; it seems odd to talk about the tangent line to a line, but if it makes sense at all the tangent line must be the line itself. It is not hard to see that the derivative of f (x) = mx + b is f 0 (x) = m; see exercise 6. Exercises √ 1. Draw the graph of the function y = f (x) = 169 − x2 between x = 0 and x = 13. Find the slope ∆y/∆x of the chord between the points of the circle lying over (a) x = 12 and x = 13, (b) x = 12 and x = 12.1, (c) x = 12 and x = 12.01, (d) x = 12 and x = 12.001. Now use the geometry of tangent lines on a circle to find (e) the exact value of the derivative f 0 (12). Your answers to (a)–(d) should be getting closer and closer to your answer to (e). ⇒ √ 2. Use geometry to find the derivative f 0 (x) of the function f (x) = 625 − x2 in the text for each of the following x: (a) 20, (b) 24, (c) −7, (d) −15. Draw a graph of the upper semicircle, and draw the tangent line at each of these four points. ⇒ 3. Draw the graph of the function y = f (x) = 1/x between x = 1/2 and x = 4. Find the slope of the chord between (a) x = 3 and x = 3.1, (b) x = 3 and x = 3.01, (c) x = 3 and x = 3.001. Now use algebra to find a simple formula for the slope of the chord between (3, f (3)) and (3 + ∆x, f (3 + ∆x)). Determine what happens when ∆x approaches 0. In your graph of y = 1/x, draw the straight line through the point (3, 1/3) whose slope is this limiting value of the difference quotient as ∆x approaches 0. ⇒ “ ” 4. Find an algebraic expression for the difference quotient f (1 + ∆x) − f (1) /∆x when f (x) = x2 − (1/x). Simplify the expression as much as possible. Then determine what happens as ∆x approaches 0. That value is f 0 (1). ⇒ 5. Draw the graph of y = f (x) = x3 between x = 0 and x = 1.5. Find the slope of the chord between (a) x = 1 and x = 1.1, (b) x = 1 and x = 1.001, (c) x = 1 and x = 1.00001. Then use algebra to find a simple formula for the slope of the chord between 1 and 1 + ∆x. (Use the expansion (A + B)3 = A3 + 3A2 B + 3AB 2 + B 3 .) Determine what happens as ∆x approaches 0, and in your graph of y = x3 draw the straight line through the point (1, 1) whose slope is equal to the value you just found. ⇒ 24 Chapter 2 Instantaneous Rate Of Change: The Derivative 6. Find an algebraic expression for the difference quotient (f (x + ∆x) − f (x))/∆x when f (x) = mx + b. Simplify the expression as much as possible. Then determine what happens as ∆x approaches 0. That value is f 0 (x). ⇒ 7. Sketch the unit circle. Discuss the behavior of the slope of the tangent line at various angles around the circle. Which trigonometric function gives the slope of the tangent line at an angle θ? Why? Hint: think in terms of ratios of sides of triangles. 8. Sketch the parabola y = x2 . For what values of x on the parabola is the slope of the tangent line positive? Negative? What do you notice about the graph at the point(s) where the sign of the slope changes from positive to negative and vice versa? 2.2 An example We started the last section by saying, “It is often necessary to know how sensitive the value of y is to small changes in x.” We have seen one purely mathematical example of this: finding the “steepness” of a curve at a point is precisely this problem. Here is a more applied example. With careful measurement it might be possible to discover that a dropped ball has height h(t) = h0 −kt2 , t seconds after it is released. (Here h0 is the initial height of the ball, when t = 0, and k is some number determined by the experiment.) A natural question is then, “How fast is the ball going at time t?” We can certainly get a pretty good idea with a little simple arithmetic. To make the calculation more concrete, let’s say h0 = 100 meters and k = 4.9 and suppose we’re interested in the speed at t = 2. We know that when t = 2 the height is 100 − 4 · 4.9 = 80.4. A second later, at t = 3, the height is 100 − 9 · 4.9 = 55.9, so in that second the ball has traveled 80.4 − 55.9 = 24.5 meters. This means that the average speed during that time was 24.5 meters per second. So we might guess that 24.5 meters per second is not a terrible estimate of the speed at t = 2. But certainly we can do better. At t = 2.5 the height is 100 − 4.9(2.5)2 = 69.375. During the half second from t = 2 to t = 2.5 the ball dropped 80.4 − 69.375 = 11.025 meters, at an average speed of 11.025/(1/2) = 22.05 meters per second; this should be a better estimate of the speed at t = 2. So it’s clear now how to get better and better approximations: compute average speeds over shorter and shorter time intervals. Between t = 2 and t = 2.01, for example, the ball drops 0.19649 meters in one hundredth of a second, at an average speed of 19.649 meters per second. We can’t do this forever, and we still might reasonably ask what the actual speed precisely at t = 2 is. If ∆t is some tiny amount of time, what we want to know is what happens to the average speed (h(2) − h(2 + ∆t))/∆t as ∆t gets smaller and smaller. Doing 2.2 An example 25 a bit of algebra: h(2) − h(2 + ∆t) 80.4 − (100 − 4.9(2 + ∆t)2 ) = ∆t ∆t 80.4 − 100 + 19.6 + 19.6∆t + 4.9∆t2 = ∆t 2 19.6∆t + 4.9∆t = ∆t = 19.6 + 4.9∆t When ∆t is very small, this is very close to 19.6, and indeed it seems clear that as ∆t goes to zero, the average speed goes to 19.6, so the exact speed at t = 2 is 19.6 meters per second. This calculation should look very familiar. In the language of the previous section, we might have started with f (x) = 100 − 4.9x2 and asked for the slope of the tangent line at x = 2. We would have answered that question by computing f (2 + ∆x) − f (2) −19.6∆x − 4.9∆x2 = = −19.6 − 4.9∆x ∆x ∆x The algebra is the same, except that following the pattern of the previous section the subtraction would be reversed, and we would say that the slope of the tangent line is −19.6. Indeed, in hindsight, perhaps we should have subtracted the other way even for the dropping ball. At t = 2 the height is 80.4; one second later the height is 55.9. The usual way to compute a “distance traveled” is to subtract the earlier position from the later one, or 55.9 − 80.4 = −24.5. This tells us that the distance traveled is 24.5 meters, and the negative sign tells us that the height went down during the second. If we continue the original calculation we then get −19.4 meters per second as the exact speed at t = 2. If we interpret the negative sign as meaning that the motion is downward, which seems reasonable, then in fact this is the same answer as before, but with even more information, since the numerical answer contains the direction of motion as well as the speed. Thus, the speed of the ball is the value of the derivative of a certain function, namely, of the function that gives the position of the ball. The upshot is that this problem, finding the speed of the ball, is exactly the same problem mathematically as finding the slope of a curve. This may already be enough evidence to convince you that whenever some quantity is changing (the height of a curve or the height of a ball or the size of the economy or the distance of a space probe from earth or the population of the world) the rate at which the quantity is changing can, in principle, be computed in exactly the same way, by finding a derivative. 26 Chapter 2 Instantaneous Rate Of Change: The Derivative Exercises 1. An object is traveling in a straight line so that its position (that is, distance from some fixed point) is given by this table: time (seconds) 0 1 2 3 distance (meters) 0 10 25 60 Find the average speed of the object during the following time intervals: [0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]. If you had to guess the speed at t = 2 just on the basis of these, what would you guess? ⇒ 2. Let y = f (t) = t2 , where t is the time in seconds and y is the distance in meters that an object falls on a certain airless planet. Draw a graph of this function between t = 0 and t = 3. Make a table of the average speed of the falling object between (a) 2 sec and 3 sec, (b) 2 sec and 2.1 sec, (c) 2 sec and 2.01 sec, (d) 2 sec and 2.001 sec. Then use algebra to find a simple formula for the average speed between time 2 and time 2 + ∆t. (If you substitute ∆t = 1, 0.1, 0.01, 0.001 in this formula you should again get the answers to parts (a)–(d).) Next, in your formula for average speed (which should be in simplified form) determine what happens as ∆t approaches zero. This is the instantaneous speed. Finally, in your graph of y = t2 draw the straight line through the point (2, 4) whose slope is the instantaneous velocity you just computed; it should of course be the tangent line. ⇒ 3. If an object is dropped from an 80-meter high window, its height y above the ground at time t seconds is given by the formula y = f (t) = 80−4.9t2 . (Here we are neglecting air resistance; the graph of this function was shown in figure 1.1.) Find the average velocity of the falling object between (a) 1 sec and 1.1 sec, (b) 1 sec and 1.01 sec, (c) 1 sec and 1.001 sec. Now use algebra to find a simple formula for the average velocity of the falling object between 1 sec and 1 + ∆t sec. Determine what happens to this average velocity as ∆t approaches 0. That is the instantaneous velocity at time t = 1 second (it will be negative, because the object is falling). ⇒ 2.3 Limits In the previous two sections we computed some quantities of interest (slope, speed) by seeing that some expression “goes to” or “approaches” or “gets really close to” a particular value. In the examples we saw, this idea may have been clear enough, but it is too fuzzy to rely on in more difficult circumstances. In this section we will see how to make the idea more precise. There is an important feature of the examples we have seen. Consider again the formula −19.6∆x − 4.9∆x2 . ∆x We wanted to know what happens to this fraction as “∆x goes to zero.” Because we were able to simplify the fraction, it was easy to see the answer, but it was not quite as simple 2.3 Limits 27 as “substituting zero for ∆x,” as that would give −19.6 · 0 − 4.9 · 0 , 0 which is meaningless. The quantity we are really interested in does not make sense “at zero,” and this is why the answer to the original problem (finding a speed or a slope) was not immediately obvious. In other words, we are generally going to want to figure out what a quantity “approaches” in situations where we can’t merely plug in a value. If you would like to think about a hard example (which we will analyze later) consider what happens to (sin x)/x as x approaches zero. √ EXAMPLE 2.2 Does x approach 1.41 as x approaches 2? In this case it is possible √ to compute the actual value 2 to a high precision to answer the question. But since √ in general we won’t be able to do that, let’s not. We might start by computing x for √ values of x close to 2, as we did in the previous sections. Here are some values: 2.05 = √ √ √ √ 1.431782106, 2.04 = 1.428285686, 2.03 = 1.424780685, 2.02 = 1.421267040, 2.01 = √ √ √ 1.417744688, 2.005 = 1.415980226, 2.004 = 1.415627070, 2.003 = 1.415273825, √ √ 2.002 = 1.414920492, 2.001 = 1.414567072. So it looks at least possible that indeed √ these values “approach” 1.41—already 2.001 is quite close. If we continue this process, √ however, at some point we will appear to “stall.” In fact, 2 = 1.414213562 . . ., so we will never even get as far as 1.4142, no matter how long we continue the sequence. √ So in a fuzzy, everyday sort of sense, it is true that x “gets close to” 1.41, but it does not “approach” 1.41 in the sense we want. To compute an exact slope or an exact speed, what we want to know is that a given quantity becomes “arbitrarily close” to a fixed value, meaning that the first quantity can be made “as close as we like” to the fixed value. Consider again the quantities −19.6∆x − 4.9∆x2 = −19.6 − 4.9∆x. ∆x These two quantities are equal as long as ∆x is not zero; if ∆x is zero, the left hand quantity is meaningless, while the right hand one is −19.6. Can we say more than we did before about why the right hand side “approaches” −19.6, in the desired sense? Can we really make it “as close as we want” to −19.6? Let’s try a test case. Can we make −19.6 − 4.9∆x within one millionth (0.000001) of −19.6? The values within a millionth of −19.6 are those in the interval (−19.600001, −19.599999). As ∆x approaches zero, does −19.6 − 4.9∆x eventually reside inside this interval? If ∆x is positive, this would require 28 Chapter 2 Instantaneous Rate Of Change: The Derivative that −19.6 − 4.9∆x > −19.600001. This is something we can manipulate with a little algebra: −19.6 − 4.9∆x > −19.600001 −4.9∆x > −0.000001 ∆x < −0.000001/ − 4.9 ∆x < 0.0000002040816327 . . . Thus, we can say with certainty that if ∆x is positive and less than 0.0000002, then ∆x < 0.0000002040816327 . . . and so −19.6 − 4.9∆x > −19.600001. We could do a similar calculation if ∆x is negative. So now we know that we can make −19.6 − 4.9∆x within one millionth of −19.6. But can we make it “as close as we want”? In this case, it is quite simple to see that the answer is yes, by modifying the calculation we’ve just done. It may be helpful to think of this as a game. I claim that I can make −19.6 − 4.9∆x as close as you desire to −19.6 by making ∆x “close enough” to zero. So the game is: you give me a number, like 10−6 , and I have to come up with a number representing how close ∆x must be to zero to guarantee that −19.6 − 4.9∆x is at least as close to −19.6 as you have requested. Now if we actually play this game, I could redo the calculation above for each new number you provide. What I’d like to do is somehow see that I will always succeed, and even more, I’d like to have a simple strategy so that I don’t have to do all that algebra every time. A strategy in this case would be a formula that gives me a correct answer no matter what you specify. So suppose the number you give me is ². How close does ∆x have to be to zero to guarantee that −19.6 − 4.9∆x is in (−19.6 − ², −19.6 + ²)? If ∆x is positive, we need: −19.6 − 4.9∆x > −19.6 − ² −4.9∆x > −² ∆x < −²/ − 4.9 ∆x < ²/4.9 So if I pick any number δ that is less than ²/4.9, the algebra tells me that whenever ∆x < δ then ∆x < ²/4.9 and so −19.6 − 4.9∆x is within ² of −19.6. (This is exactly what I did in the example: I picked δ = 0.0000002 < 0.0000002040816327 . . ..) A similar calculation again works for negative ∆x. The important fact is that this is now a completely general result—it shows that I can always win, no matter what “move” you make. Now we can codify this by giving a precise definition to replace the fuzzy, “gets closer and closer” language we have used so far. Henceforward, we will say something like “the 2.3 Limits 29 limit of (−19.6∆x−4.9∆x2 )/∆x as ∆x goes to zero is −19.6,” and abbreviate this mouthful as −19.6∆x − 4.9∆x2 lim = −19.6. ∆x→0 ∆x Here is the actual, official definition of “limit”. DEFINITION 2.3 Limit Suppose f is a function. We say that limx→a f (x) = L if for every ² > 0 there is a δ > 0 so that whenever 0 < |x − a| < δ, |f (x) − L| < ². The ² and δ here play exactly the role they did in the preceding discussion. The definition says, in a very precise way, that f (x) can be made as close as desired to L (that’s the |f (x) − L| < ² part) by making x close enough to a (the 0 < |x − a| < δ part). Note that we specifically make no mention of what must happen if x = a, that is, if |x − a| = 0. This is because in the cases we are most interested in, substituting a for x doesn’t even make sense. Make sure you are not confused by the names of important quantities. The generic definition talks about f (x), but the function and the variable might have other names. In the discussion above, the function we analyzed was −19.6∆x − 4.9∆x2 . ∆x and the variable of the limit was not x but ∆x. The x was the variable of the original function; when we were trying to compute a slope or a speed, x was essentially a fixed quantity, telling us at what point we wanted the slope. (In the speed problem, it was literally a fixed quantity, as we focused on the time 2.) The quantity a of the definition in all the examples was zero: we were always interested in what happened as ∆x became very close to zero. Armed with a precise definition, we can now prove that certain quantities behave in a particular way. The bad news is that even proofs for simple quantities can be quite tedious and complicated; the good news is that we rarely need to do such proofs, because most expressions act the way you would expect, and this can be proved once and for all. EXAMPLE 2.4 Let’s show carefully that limx→2 x + 4 = 6. This is not something we “need” to prove, since it is “obviously” true. But if we couldn’t prove it using our official definition there would be something very wrong with the definition. As is often the case in mathematical proofs, it helps to work backwards. We want to end up showing that under certain circumstances x + 4 is close to 6; precisely, we want to show that |x + 4 − 6| < ², or |x − 2| < ². Under what circumstances? We want this to be 30 Chapter 2 Instantaneous Rate Of Change: The Derivative true whenever 0 < |x − 2| < δ. So the question becomes: can we choose a value for δ that guarantees that 0 < |x − 2| < δ implies |x − 2| < ²? Of course: no matter what ² is, δ = ² works. So it turns out to be very easy to prove something “obvious,” which is nice. It doesn’t take long before things get trickier, however. EXAMPLE 2.5 It seems clear that limx→2 x2 = 4. Let’s try to prove it. We will want to be able to show that |x2 − 4| < ² whenever 0 < |x − 2| < δ, by choosing δ carefully. Is there any connection between |x − 2| and |x2 − 4|? Yes, and it’s not hard to spot, but it is not so simple as the previous example. We can write |x2 − 4| = |(x + 2)(x − 2)|. Now when |x − 2| is small, part of |(x + 2)(x − 2)| is small, namely (x − 2). What about (x + 2)? If x is close to 2, (x + 2) certainly can’t be too big, but we need to somehow be precise about it. Let’s recall the “game” version of what is going on here. You get to pick an ² and I have to pick a δ that makes things work out. Presumably it is the really tiny values of ² I need to worry about, but I have to be prepared for anything, even an apparently “bad” move like ² = 1000. I expect that ² is going to be small, and that the corresponding δ will be small, certainly less than 1. If δ ≤ 1 then |x + 2| < 5 when |x − 2| < δ (because if x is within 1 of 2, then x is between 1 and 3 and x + 2 is between 3 and 5). So then I’d be trying to show that |(x + 2)(x − 2)| < 5|x − 2| < ². So now how can I pick δ so that |x − 2| < δ implies 5|x − 2| < ²? This is easy: use δ = ²/5, so 5|x − 2| < 5(²/5) = ². But what if the ² you choose is not small? If you choose ² = 1000, should I pick δ = 200? No, to keep things “sane” I will never pick a δ bigger than 1. Here’s the final “game strategy:” When you pick a value for ² I will pick δ = ²/5 or δ = 1, whichever is smaller. Now when |x − 2| < δ, I know both that |x + 2| < 5 and that |x − 2| < ²/5. Thus |(x + 2)(x − 2)| < 5(²/5) = ². This has been a long discussion, but most of it was explanation and scratch work. If this were written down as a proof, it would be quite short, like this: Proof that limx→2 x2 = 4. Given any ², pick δ = ²/5 or δ = 1, whichever is smaller. Then when |x − 2| < δ, |x + 2| < 5 and |x − 2| < ²/5. Hence |x2 − 4| = |(x + 2)(x − 2)| < 5(²/5) = ². It probably seems obvious that limx→2 x2 = 4, and it is worth examining more closely why it seems obvious. If we write x2 = x · x, and ask what happens when x approaches 2, we might say something like, “Well, the first x approaches 2, and the second x approaches 2, so the product must approach 2 · 2.” In fact this is pretty much right on the money, except for that word “must.” Is it really true that if x approaches a and y approaches b then xy approaches ab? It is, but it is not really obvious, since x and y might be quite complicated. The good news is that we can see that this is true once and for all, and then 2.3 Limits 31 we don’t have to worry about it ever again. When we say that x might be “complicated” we really mean that in practice it might be a function. Here is then what we want to know: THEOREM 2.6 Suppose limx→a f (x) = L and limx→a g(x) = M . Then limx→a f (x)g(x) = LM . Proof. We have to use the official definition of limit to make sense of this. So given any ² we need to find a δ so that 0 < |x − a| < δ implies |f (x)g(x) − LM | < ². What do we have to work with? We know that we can make f (x) close to L and g(x) close to M , and we have to somehow connect these facts to make f (x)g(x) close to LM . We use, as is so often the case, a little algebraic trick: |f (x)g(x) − LM | = |f (x)g(x) − f (x)M + f (x)M − LM | = |f (x)(g(x) − M ) + (f (x) − L)M | ≤ |f (x)(g(x) − M )| + |(f (x) − L)M | = |f (x)||g(x) − M | + |f (x) − L||M |. This is all straightforward except perhaps for the “≤”. That is an example of the triangle inequality, which says that if a and b are any real numbers then |a + b| ≤ |a| + |b|. If you look at a few examples, using positive and negative numbers in various combinations for a and b, you should quickly understand why this is true. Since limx→a = L, there is a value δ1 so that 0 < |x − a| < δ1 implies |f (x) − L| < |²/(2M )|, This means that 0 < |x − a| < δ1 implies |f (x) − L||M | < ²/2. You can see where this is going: if we can make |f (x)||g(x) − M | < ²/2 also, then we’ll be done. We can make |g(x) − M | smaller than any fixed number by making x close enough to a; unfortunately, ²/(2f (x)) is not a fixed number, since x is a variable. Here we need another little trick, just like the one we used in analyzing x2 . We can find a δ2 so that |x − a| < δ2 implies that |f (x) − L| < 1, meaning that L − 1 < f (x) < L + 1. This means that |f (x)| < N , where N is either |L − 1| or |L + 1|, depending on whether L is negative or positive. The important point is that N doesn’t depend on x. Finally, we know that there is a δ3 so that 0 < |x − a| < δ3 implies |g(x) − M | < ²/(2N ). Now we’re ready to put everything together. Let δ be the smallest of δ1 , δ2 , and δ3 . Then |x − a| < δ implies that |f (x) − L| < |²/(2M )|, |f (x)| < N , and |g(x) − M | < ²/(2N ). Then |f (x)g(x) − LM | ≤ |f (x)||g(x) − M | + |f (x) − L||M | < N (²/(2N )) + |²/(2M )||M | = ²/2 + ²/2 = ². 32 Chapter 2 Instantaneous Rate Of Change: The Derivative This is just what we needed, so by the official definition, limx→a f (x)g(x) = LM . A handful of such theorems give us the tools to compute many limits without explicitly working with the definition of limit. THEOREM 2.7 constant. Then Suppose that limx→a f (x) = L and limx→a g(x) = M and k is some lim kf (x) = k lim f (x) = kL x→a x→a lim (f (x) + g(x)) = lim f (x) + lim g(x) = L + M x→a x→a x→a lim (f (x) − g(x)) = lim f (x) − lim g(x) = L − M x→a x→a x→a lim (f (x)g(x)) = lim f (x) · lim g(x) = LM x→a x→a x→a f (x) limx→a f (x) L = = , if M is not 0 x→a g(x) limx→a g(x) M lim Roughly speaking, these rules say that to compute the limit of an algebraic expression, it is enough to compute the limits of the “innermost bits” and then combine these limits. This often means that it is possible to simply plug in a value for the variable, since limx→a x = a. EXAMPLE 2.8 x2 − 3x + 5 . If we apply the theorem in all its gory x→1 x−2 Compute lim detail, we get x2 − 3x + 5 limx→1 (x2 − 3x + 5) = x→1 x−2 limx→1 (x − 2) lim = (limx→1 x2 ) − (limx→1 3x) + (limx→1 5) (limx→1 x) − (limx→1 2) = (limx→1 x)2 − 3(limx→1 x) + 5 (limx→1 x) − 2 12 − 3 · 1 + 5 1−2 1−3+5 = = −3 −1 = 2.3 Limits 33 It is worth commenting on the trivial limit limx→1 5. From one point of view this might seem meaningless, as the number 5 can’t “approach” any value, since it is simply a fixed number. But 5 can, and should, be interpreted here as the function that has value 5 everywhere, f (x) = 5, with graph a horizontal line. From this point of view it makes sense to ask what happens to the height of the function as x approaches 1. Of course, as we’ve already seen, we’re primarily interested in limits that aren’t so easy, namely, limits in which a denominator approaches zero. There are a handful of algebraic tricks that work on many of these limits. x2 + 2x − 3 . We can’t simply plug in x = 1 because x→1 x−1 that makes the denominator zero. We can note that if x 6= 1, EXAMPLE 2.9 Compute lim x2 + 2x − 3 (x − 1)(x + 3) = lim x→1 x→1 x−1 x−1 lim = lim (x + 3) = 4 x→1 Another of the most common algebraic tricks was used in section 2.1. Here’s another example: √ EXAMPLE 2.10 Compute lim x→−1 √ lim x→−1 x+5−2 . x+1 √ x+5−2 x+5+2 √ x+1 x+5+2 x+5−4 √ = lim x→−1 (x + 1)( x + 5 + 2) x+1 √ = lim x→−1 (x + 1)( x + 5 + 2) 1 1 = lim √ = x→−1 4 x+5+2 x+5−2 = lim x→−1 x+1 √ Occasionally we will need a slightly modified version of the limit definition. Consider √ the function f (x) = 1 − x2 , the upper half of the unit circle. What can we say about limx→1 f (x)? It is apparent from the graph of this familiar function that as x gets close to 1 from the left, the value of f (x) gets close to zero. It does not even make sense to 34 Chapter 2 Instantaneous Rate Of Change: The Derivative ask what happens as x approaches 1 from the right, since f (x) is not defined there. The definition of the limit, however, demands that f (1 + ∆x) be close to f (1) whether ∆x is positive or negative. Sometimes the limit of a function exists from one side or the other (or both) even though the limit does not exist. Since it is useful to be able to talk about this situation, we introduce the concept of one sided limit: DEFINITION 2.11 One-sided limit Suppose that f (x) is a function. We say that limx→a− f (x) = L if for every ² > 0 there is a δ > 0 so that whenever 0 < a − x < δ, |f (x) − L| < ². We say that limx→a+ f (x) = L if for every ² > 0 there is a δ > 0 so that whenever 0 < x − a < δ, |f (x) − L| < ². Usually limx→a− f (x) is read “the limit of f (x) from the left” and limx→a+ f (x) is read “the limit of f (x) from the right”. x x x , lim , and lim . x→0 |x| x→0− |x| x→0+ |x| The function f (x) = x/|x| is undefined at 0; when x > 0, |x| = x and so f (x) = 1; x when x < 0, |x| = −x and f (x) = −1. Thus limx→0− |x| = limx→0− −1 = −1 while EXAMPLE 2.12 limx→0+ x |x| Discuss lim = limx→0+ 1 = 1. The limit of f (x) must be equal to both the left and right limits; since they are different, the limit limx→0 x |x| does not exist. Exercises Compute the limits. If a limit does not exist, explain why. x2 + x − 12 ⇒ x→3 x−3 x2 + x − 12 lim ⇒ x→1 x−3 x2 + x − 12 lim ⇒ x→−4 x−3 x2 + x − 12 lim ⇒ x→2 x−2 √ x+8−3 lim ⇒ x→1 x−1 r r 1 1 lim +2− . ⇒ x→0 x x lim 3 ⇒ 1. lim 2. 3. 4. 5. 6. 7. x→2 8. lim 3x3 − 5x ⇒ x→4 4x − 5x2 ⇒ x→0 x−1 x2 − 1 lim ⇒ x→1 x − 1 √ 2 − x2 lim ⇒ x x→0+ √ 2 − x2 lim ⇒ x+1 x→0+ x3 − a3 lim ⇒ x→a x − a lim (x2 + 4)3 ⇒ x→2 n x − 5 x 6= 1, lim ⇒ x→1 7 x = 1. 9. lim 10. 11. 12. 13. 14. 15. 2.4 The Derivative Function 35 „ « 1 (Hint: Use the fact that | sin a| < 1 for any real number a. You should x→0 x probably use the definition of a limit here.) ⇒ 16. lim x sin 17. Give an ²–δ proof, similar to example 2.4, of the fact that limx→4 (2x − 5) = 3. 18. Evaluate the expressions by reference to this graph: 10 8 6 4 2 K4 K2 0 2 K2 x 4 6 (a) lim f (x) (b) lim f (x) (c) lim f (x) (d) lim f (x) (e) lim f (x) (f) f (−2) (g) lim f (x) (h) (j) f (0) (k) lim f (x − 4) x→4 x→0− x→2− ⇒ x→−3 x→0+ lim f (x) x→−2− x→1− x→0 (i) lim f (x + 1) x→0 (l) lim f (x − 2) x→0+ sin x . x tan(3x) 20. Use a calculator to estimate lim . x→0 tan(5x) 19. Use a calculator to estimate lim x→0 2.4 The Derivative Function We have seen how to create, or derive, a new function f 0 (x) from a function f (x), and that this new function carries important information. In one example we saw that f 0 (x) tells us how steep the graph of f (x) is; in another we saw that f 0 (x) tells us the speed of an object if f (x) tells us the position of the object at time x. As we said earlier, this same mathematical idea is useful whenever f (x) represents some changing quantity and we 36 Chapter 2 Instantaneous Rate Of Change: The Derivative want to know something about how it changes, or roughly, the “rate” at which it changes. Most functions encountered in practice are built up from a small collection of “primitive” functions in a few simple ways, for example, by adding or multiplying functions together to get new, more complicated functions. To make good use of the information provided by f 0 (x) we need to be able to compute it for a variety of such functions. We will begin to use different notations for the derivative of a function. While initially confusing, each is often useful so it is worth maintaining multiple versions of the same thing. √ Consider again the function f (x) = 625 − x2 . We have computed the derivative √ f 0 (x) = −x/ 625 − x2 , and have already noted that if we use the alternate notation √ √ y = 625 − x2 then we might write y 0 = −x/ 625 − x2 . Another notation is quite different, and in time it will become clear why it is often a useful one. Recall that to compute the the derivative of f we computed p 625 − (7 + ∆x)2 − 24 lim . ∆x→0 ∆x The denominator here measures a distance in the x direction, sometimes called the “run”, and the numerator measures a distance in the y direction, sometimes called the “rise,” and “rise over run” is the slope of a line. Recall that sometimes such a numerator is abbreviated ∆y, exchanging brevity for a more detailed expression. So in general, a derivative is given by ∆y y 0 = lim . ∆x→0 ∆x To recall the form of the limit, we sometimes say instead that dy ∆y = lim . dx ∆x→0 ∆x In other words, dy/dx is another notation for the derivative, and it reminds us that it is related to an actual slope between two points. This notation is called Leibniz notation, after Gottfried Leibniz, who developed the fundamentals of calculus independently, at about the same time that Isaac Newton did. Again, since we often use f and f (x) to mean the original function, we sometimes use df /dx and df (x)/dx to refer to the derivative. If the function f (x) is written out in full we often write the last of these something like this f 0 (x) = d p 625 − x2 dx with the function written to the side, instead of trying to fit it into the numerator. 2.4 EXAMPLE 2.13 We compute The Derivative Function 37 Find the derivative of y = f (t) = t2 . ∆y (t + ∆t)2 − t2 y = lim = lim ∆t→0 ∆t ∆t→0 ∆t 2 t + 2t∆t + ∆t2 − t2 = lim ∆t→0 ∆t 2t∆t + ∆t2 = lim ∆t→0 ∆t 0 = lim 2t + ∆t = 2t. ∆t→0 Remember that ∆t is a single quantity, not a “∆” times a “t”, and so ∆t2 is (∆t)2 not ∆(t2 ). EXAMPLE 2.14 Find the derivative of y = f (x) = 1/x. The computation: ∆y y = lim = lim ∆x→0 ∆x ∆x→0 0 = lim ∆x→0 = lim 1 x+∆x − 1 x ∆x x x(x+∆x) − x+∆x x(x+∆x) ∆x x−(x+∆x) x(x+∆x) ∆x x − x − ∆x = lim ∆x→0 x(x + ∆x)∆x ∆x→0 −∆x ∆x→0 x(x + ∆x)∆x −1 −1 = lim = 2 ∆x→0 x(x + ∆x) x = lim Note. If you happen to know some “derivative formulas” from an earlier course, for the time being you should pretend that you do not know them. In examples like the ones above and the homework below, you are required to know how to find the derivative formula starting from basic principles. We will later develop some formulas so that we do not always need to do such computations, but we will continue to need to know how to do the more involved computations. 38 Chapter 2 Instantaneous Rate Of Change: The Derivative Sometimes one encounters a point in the domain of a function y = f (x) where there is no derivative, because there is no tangent line. In order for the notion of the tangent line at a point to make sense, the curve must be “smooth” at that point. This means that if you imagine a particle traveling at some steady speed along the curve, then the particle does not experience an abrupt change of direction. There are two types of situations you should be aware of—corners and cusps—where there’s a sudden change of direction and hence no derivative. EXAMPLE 2.15 Discuss the derivative of the absolute value function y = f (x) = |x|. If x is positive, then this is the function y = x, whose derivative is the constant 1. (Recall that when y = f (x) = mx + b, the derivative is the slope m.) If x is negative, then we’re dealing with the function y = −x, whose derivative is the constant −1. If x = 0, then the function has a corner, i.e., there is no tangent line. A tangent line would have to point in the direction of the curve—but there are two directions of the curve that come together at the origin. We can summarize this as ( y0 = 1 if x > 0; −1 if x < 0; undefined if x = 0. EXAMPLE 2.16 Discuss the derivative of the function y = x2/3 , shown in figure 2.2. We will later see how to compute this derivative; for now we use the fact that y 0 = (2/3)x−1/3 . Visually this looks much like the absolute value function, but it technically has a cusp, not a corner. The absolute value function has no tangent line at 0 because there are (at least) two obvious contenders—the tangent line of the left side of the curve and the tangent line of the right side. The function y = x2/3 does have a tangent line at 0, the y-axis, a line with no slope (sometimes called “infinite” slope, though this is hard to make precise). But the slope of x2/3 just to the right of 0 is very large and positive, while the slope to the left is very large and negative, so in fact there is no “single slope” at 0. In practice we won’t worry much about the distinction between these examples; in both cases the function has a “sharp point” where there is no tangent line and no derivative. 2.4 The Derivative Function 39 ....... ....... ....... ....... ....... ...... ....... ...... ....... . . . . . ....... ....... ....... ....... ...... ...... ...... ...... . ...... . . . . ...... ..... ...... ...... ...... ...... ...... ...... . . ...... . . . ...... ...... ..... ..... ..... ..... ..... ..... . . ..... . . ..... ..... ..... .... .... .... ... ...... ... .. ...... ... 1 0 −2 −1 0 Figure 2.2 1 2 A cusp on x2/3 . Exercises 1. Find the derivative of y = f (x) = √ 169 − x2 . ⇒ 2. Find the derivative of y = f (t) = 80 − 4.9t2 . ⇒ 3. Find the derivative of y = f (x) = x2 − (1/x). ⇒ 4. Find the derivative of y = f (x) = ax2 + bx + c (where a, b, and c are constants). ⇒ 5. Find the derivative of y = f (x) = x3 . ⇒ 6. Shown is the graph of a function f (x). Sketch the graph of f 0 (x) by estimating the derivative at a number of points in the interval: estimate the derivative at regular intervals from one end of the interval to the other, and also at “special” points, as when the derivative is zero. Make sure you indicate any places where the derivative does not exist. 1.6 1.4 1.2 1.0 0.8 .............................. ........ ....... ..... ...... ..... ...... . . . . ...... .. . . . . ..... ... .. . . ..... ... . . . . . . . ..... . .. ..... ... ... ..... ... ... ..... ... ..... ... . . . ..... .. .. ..... .. ... ..... .. ... ..... ... .. ..... . . . . ..... .. .... ...... ... .... ...... .... ....... ..... . ........ . . . . ............................. 0.6 0.4 0.2 −1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0 7. Shown is the graph of a function f (x). Sketch the graph of f 0 (x) by estimating the derivative at a number of points in the interval: estimate the derivative at regular intervals from one end of the interval to the other, and also at “special” points, as when the derivative is zero. 40 Chapter 2 Instantaneous Rate Of Change: The Derivative Make sure you indicate any places where the derivative does not exist. 4 3 2 1 0 .. ... ... . . ... ... ... . . . ... ... ... . . ... ... ... . . .. ... ... ... . . ... ... ... . . .. .. .... ... ..... ... ..... .... ... . . . . . . ... ..... ... ... .... ... ... ..... ... ... ..... . . . . . . . . ... ..... ..... ... ..... .... ..... .... .... ..... ..... .... . . . . . . . . . . . . ...... ..... ...... ........ ..... ........................ ..... .... . . . . . ..... ..... ..... ..... . . . .... ..... ..... ..... . . . . . ..... ..... ..... 0 1 2 3 4 5 2.5 Adjectives For Functions As we have defined it in Section 1.3, a function is a very general object. At this point, it is useful to introduce a collection of adjectives to describe certain kinds of functions. Consider the graphs of the functions in Figure 2.3. It would clearly be useful to have words to help us describe the distinct features of each of them. We will point out and define a few adjectives (there are many more) for the functions pictured here. For the sake of the discussion, we will assume that the graphs do not exhibit any unusual behavior off-stage (i.e., outside the view of the graphs). Functions. Each graph in Figure 2.3 certainly represents a function—since each passes the vertical line test. In other words, as you sweep a vertical line across the graph of each function, the line never intersects the graph more than once. If it did, then the graph would not represent a function. Bounded. The graph in (c) appears to approach zero as x goes to both positive and negative infinity. It also never exceeds the value 1 or drops below the value 0. Because the graph never increases or decreases without bound, we say that the function represented by the graph in (c) is a bounded function. DEFINITION 2.17 Bounded A function f is bounded if there is a number M such that |f (x)| < M for every x in the domain of f . 2.5 (a) 3 2 1 −3 −2 2 3 ..... −1 ... ... ... ... ... ... ... −2 ... ... ... ... ... ... −3 −3 −2 1 −1 −3 (d) 3 3 2 2 1 −1 −1 −2 −3 Figure 2.3 3 −2 1....................... ...... ...... ..... ..... ..... ..... . . . . ...... .. . . . ...... . .. . . . ........ . . . .. . . . ............... . . . . . . . . ................................ ....................................... −2 2 −1 (c) −3 41 (b) .... 3 ..... .. ..... ..... . . . ... ..... ... ..... ... ..... ..... ... . . . . . . ... 2 . . .. . . ... . . .. . . ... . . .. . . ... . . ... . ... . . . . . ... . . .. . . ... . . .... ... 1 ....... ... ....... ... ...... ... ......... ... ... ...... .... ... ... ... ... ... ... ... ... ... ... ... ... ... .... ..... ..... ..... ..... ..... ..... ..... ..... ... 1 −1 Adjectives For Functions 2 3 1 ...................... −2 −3 −1 .............. .......... ....... ...... ..... ..... −1 ... ... ... ... ... ... .. −2 .. .. .. .. .. ... ... . −3 ... ... ... .. .. .. .. .. ... ... ... ... ... ... .... ..... ...... ....... ......... ............. .................... ..... 1 2 3 Function Types: (a) a discontinuous function, (b) a continuous function, (c) a bounded, differentiable function, (d) an unbounded, differentiable function For the function in (c), one such choice for M would be 10. However, the smallest (optimal) choice would be M = 1. In either case, simply finding an M is enough to establish boundedness. No such M exists for the hyperbola in (d) and hence we can say that it is unbounded. Continuity. The graphs shown in (b) and (c) both represent continuous functions. Geometrically, this is because there are no jumps in the graphs. That is, if you pick a point on the graph and approach it from the left and right, the values of the function approach the value of the function at that point. For example, we can see that this is not 42 Chapter 2 Instantaneous Rate Of Change: The Derivative true for function values near x = −1 on the graph in (a) which is not continuous at that location. DEFINITION 2.18 Continuity at a Point a if limx→a f (x) = f (a). DEFINITION 2.19 Continuous every point in its domain. A function f is continuous at a point A function f is continuous if it is continuous at Strangely, we can also say that (d) is continuous even though there is a vertical asymptote. A careful reading of the definition of continuous reveals the phrase “at every point in its domain.” Because the location of the asymptote, x = 0, is not in the domain of the function, and because the rest of the function is well-behaved, we can say that (d) is continuous. Differentiability. Now that we have introduced the derivative of a function at a point, we can begin to use the adjective differentiable. We can see that the tangent line is welldefined at every point on the graph in (c). Therefore, we can say that (c) is a differentiable function. DEFINITION 2.20 Differentiable at a Point A function f is differentiable at point a (excluding endpoints and isolated points in the domain of f ) if f 0 (a) exists. DEFINITION 2.21 Differentiable A function f is differentiable if is differentiable at every point (excluding endpoints and isolated points in the domain of f ) in the domain of f . Take note that, for technical reasons not discussed here, both of these definitions exclude endpoints and isolated points in the domain from consideration. We now have a collection of adjectives to describe the very rich and complex set of objects known as functions. We close with a useful theorem about continuous functions: THEOREM 2.22 Intermediate Value Theorem If f is continuous on the interval [a, b] and d is between f (a) and f (b), then there is a number c in [a, b] such that f (c) = d. This is most frequently used when d = 0. EXAMPLE 2.23 and 1. Explain why the function f = x3 + 3x2 + x − 2 has a root between 0 2.5 Adjectives For Functions 43 Since f (0) = −2 and f (1) = 3, and 0 is between −2 and 3, there is a c ∈ [0, 1] such that f (c) = 0. This example also points the way to a simple method for approximating roots. EXAMPLE 2.24 Approximate the root of the previous example to one decimal place. If we compute f (0.1), f (0.2), and so on, we find that f (0.6) < 0 and f (0.7) > 0, so by the Intermediate Value Theorem, f has a root between 0.6 and 0.7. Repeating the process with f (0.61), f (0.62), and so on, we find that f (0.61) < 0 and f (0.62) > 0, so f has a root between 0.61 and 0.62, and the root is 0.6 rounded to one decimal place. Exercises 1. Along the lines of Figure 2.3, for each part below sketch the graph of a function that is: a. bounded, but not continuous. b. differentiable and unbounded. c. continuous at x = 0, not continuous at x = 1, and bounded. d. differentiable everywhere except at x = −1, continuous, and unbounded. 2. Is f (x) = sin(x) a bounded function? If so, find the smallest M . 3. Is s(t) = 1/(1 + t2 ) a bounded function? If so, find the smallest M . 4. Is v(u) = 2 ln |u| a bounded function? If so, find the smallest M . 5. Consider the function  h(x) = 2x − 3, 0, if x < 1 if x ≥ 1. Show that it is continuous at the point x = 0. Is h a continuous function? 6. Approximate a root of f = x3 − 4x2 + 2x + 2 to one decimal place. 7. Approximate a root of f = x4 + x3 − 5x + 1 to one decimal place. 3 Rules For Finding Derivatives It is tedious to compute a limit every time we need to know the derivative of a function. Fortunately, we can develop a small collection of examples and rules that allow us to compute the derivative of almost any function we are likely to encounter. Many functions involve quantities raised to a constant power, such as polynomials and more complicated combinations like y = (sin x)4 . So we start by examining powers of a single variable; this gives us a building block for more complicated examples. 3.1 The Power Rule We start with the derivative of a power function, f (x) = xn . Here n is a number of any kind: integer, rational, positive, negative, even irrational, as in xπ . We have already computed some simple examples, so the formula should not be a complete surprise: d n x = nxn−1 . dx It is not easy to show this is true for any n. We will do some of the easier cases now, and discuss the rest later. The easiest, and most common, is the case that n is a positive integer. To compute the derivative we need to compute the following limit: d n (x + ∆x)n − xn x = lim . ∆x→0 dx ∆x 45 46 Chapter 3 Rules For Finding Derivatives For a specific, fairly small value of n, we could do this by straightforward algebra. EXAMPLE 3.1 Find the derivative of f (x) = x3 . d 3 (x + ∆x)3 − x3 x = lim . ∆x→0 dx ∆x x3 + 3x2 ∆x + 3x∆x2 + ∆x3 − x3 = lim . ∆x→0 ∆x 3x2 ∆x + 3x∆x2 + ∆x3 = lim . ∆x→0 ∆x = lim 3x2 + 3x∆x + ∆x2 = 3x2 . ∆x→0 The general case is really not much harder as long as we don’t try to do too much. The key is understanding what happens when (x + ∆x)n is multiplied out: (x + ∆x)n = xn + nxn−1 ∆x + a2 xn−2 ∆x2 + · · · + +an−1 x∆xn−1 + ∆xn . We know that multiplying out will give a large number of terms all of the form xi ∆xj , and in fact that i + j = n in every term. One way to see this is to understand that one method for mulitplying out (x + ∆x)n is the following: In every (x + ∆x) factor, pick either the x or the ∆x, then multiply the n choices together; do this in all possible ways. For example, for (x + ∆x)3 , there are eight possible ways to do this: (x + ∆x)(x + ∆x)(x + ∆x) = xxx + xx∆x + x∆xx + x∆x∆x + ∆xxx + ∆xx∆x + ∆x∆xx + ∆x∆x∆x = x3 + x2 ∆x + x2 ∆x + x∆x2 + x2 ∆x + x∆x2 + x∆x2 + ∆x3 = x3 + 3x2 ∆x + 3x∆x2 + ∆x3 No matter what n is, there are n ways to pick ∆x in one factor and x in the remaining n−1 factors; this means one term is nxn−1 ∆x. The other coefficients are somewhat harder to understand, but we don’t really need them, so in the formula above they have simply been called a2 , a3 , and so on. We know that every one of these terms contains ∆x to at 3.1 The Power Rule 47 least the power 2. Now let’s look at the limit: d n (x + ∆x)n − xn x = lim ∆x→0 dx ∆x n x + nxn−1 ∆x + a2 xn−2 ∆x2 + · · · + an−1 x∆xn−1 + ∆xn − xn = lim ∆x→0 ∆x nxn−1 ∆x + a2 xn−2 ∆x2 + · · · + an−1 x∆xn−1 + ∆xn = lim ∆x→0 ∆x = lim nxn−1 + a2 xn−2 ∆x + · · · + an−1 x∆xn−2 + ∆xn−1 = nxn−1 . ∆x→0 Now without much trouble we can verify the formula for negative integers. First let’s look at an example: EXAMPLE 3.2 −3x−4 . Find the derivative of y = x−3 . Using the formula, y 0 = −3x−3−1 = Here is the general computation. Suppose n is a negative integer; the algebra is easier to follow if we use n = −m in the computation, where m is a positive integer. d n d −m (x + ∆x)−m − x−m x = x = lim ∆x→0 dx dx ∆x = lim 1 (x+∆x)m − 1 xm ∆x x − (x + ∆x)m = lim ∆x→0 (x + ∆x)m xm ∆x ∆x→0 m xm − (xm + mxm−1 ∆x + a2 xm−2 ∆x2 + · · · + am−1 x∆xm−1 + ∆xm ) ∆x→0 (x + ∆x)m xm ∆x = lim −mxm−1 − a2 xm−2 ∆x − · · · − am−1 x∆xm−2 − ∆xm−1 ) ∆x→0 (x + ∆x)m xm = lim = −mxm−1 −mxm−1 = = −mxm−1−2m = nx−m−1 = nxn−1 . m m 2m x x x We will later see why the other cases of the power rule work, but from now on we will use the power rule whenever n is any real number. Let’s note here a simple case in which the power rule applies, or almost applies, but is not really needed. Suppose that f (x) = 1; remember that this “1” is a function, not “merely” a number, and that f (x) = 1 has a graph that is a horizontal line, with slope zero everywhere. So we know that f 0 (x) = 0. 48 Chapter 3 Rules For Finding Derivatives We might also write f (x) = x0 , though there is some question about just what this means at x = 0. If we apply the power rule, we get f 0 (x) = 0x−1 = 0/x = 0, again noting that there is a problem at x = 0. So the power rule “works” in this case, but it’s really best to just remember that the derivative of any constant function is zero. Exercises Find the derivatives of the given functions. 1. x100 ⇒ 4. xπ ⇒ 2. x−100 ⇒ 5. x3/4 ⇒ 1 6. x−9/7 ⇒ 3. 5 ⇒ x 7. Suppose the position of an object at time t is given by f (t) = −49t2 /10. Find a function giving the speed of the object at time t. The acceleration of an object is the rate at which its speed is changing, which means it is given by the deriviative of the speed function. Find the acceleration of the object at time t. ⇒ 3.2 Linearity of the Derivative An operation is linear if it behaves “nicely” with respect to multiplication by a constant and addition. The name comes from the equation of a line through the origin, f (x) = mx, and the following two properties of this equation. First, f (cx) = m(cx) = c(mx) = cf (x), so the constant c can be “moved outside” or “moved through” the function f . Second, f (x + y) = m(x + y) = mx + my = f (x) + f (y), so the addition symbol likewise can be moved through the function. The corresponding properties for the derivative are: (cf (x))0 = and (f (x) + g(x))0 = d d cf (x) = c f (x) = cf 0 (x), dx dx d d d (f (x) + g(x)) = f (x) + g(x) = f 0 (x) + g 0 (x). dx dx dx It is easy to see, or at least to believe, that these are true by thinking of the distance/speed interpretation of derivatives. If one object is at position f (t) at time t, we know its speed is given by f 0 (t). Suppose another object is at position 5f (t) at time t, namely, that it is always 5 times as far along the route as the first object. Then it “must” be going 5 times as fast at all times. The second rule is somewhat more complicated, but here is one way to picture it. Suppose a flat bed railroad car is at position f (t) at time t, so the car is traveling at a 3.2 Linearity of the Derivative 49 speed of f 0 (t) (to be specific, let’s say that f (t) gives the position on the track of the rear end of the car). Suppose that an ant is crawling from the back of the car to the front so that its position on the car is g(t) and its speed relative to the car is g 0 (t). Then in reality, at time t, the ant is at position f (t) + g(t) along the track, and its speed is “obviously” f 0 (t) + g 0 (t). We don’t want to rely on some more-or-less obvious physical interpretation to determine what is true mathematically, so let’s see how to verify these rules by computation. We’ll do one and leave the other for the exercises. d f (x + ∆x) + g(x + ∆x) − (f (x) + g(x)) (f (x) + g(x)) = lim ∆x→0 dx ∆x f (x + ∆x) + g(x + ∆x) − f (x) − g(x) = lim ∆x→0 ∆x f (x + ∆x) − f (x) + g(x + ∆x) − g(x) = lim ∆x→0 ∆x µ ¶ f (x + ∆x) − f (x) g(x + ∆x) − g(x) = lim + ∆x→0 ∆x ∆x f (x + ∆x) − f (x) g(x + ∆x) − g(x) + lim ∆x→0 ∆x→0 ∆x ∆x = lim = f 0 (x) + g 0 (x) This is sometimes called the sum rule for derivatives. EXAMPLE 3.3 twice here: f 0 (x) = Find the derivative of f (x) = x5 + 5x2 . We have to invoke linearity d 5 d 5 d d (x + 5x2 ) = x + (5x2 ) = 5x4 + 5 (x2 ) = 5x4 + 5 · 2x1 = 5x4 + 10x. dx dx dx dx Because it is so easy with a little practice, we can usually combine all uses of linearity into a single step. The following example shows an acceptably detailed computation. EXAMPLE 3.4 d f (x) = dx 0 µ Find the derivative of f (x) = 3/x4 − 2x2 + 6x − 7. ¶ 3 − 2x2 + 6x − 7 4 x = d (3x−4 − 2x2 + 6x − 7) = −12x−5 − 4x + 6. dx 50 Chapter 3 Rules For Finding Derivatives Exercises Find the derivatives of the functions in 1–6. 1. 5x3 + 12x2 − 15 ⇒ 2. −4x5 + 3x2 − 5/x2 ⇒ 3. 5(−3x2 + 5x + 1) ⇒ 4. f (x) + g(x), where f (x) = x2 − 3x + 2 and g(x) = 2x3 − 5x ⇒ 5. (x + 1)(x2 + 2x − 3) ⇒ √ 6. 625 − x2 + 3x3 + 12 (See section 2.1.) ⇒ 7. Find an equation for the tangent line to f (x) = x3 /4 − 1/x at x = −2. ⇒ 8. Find an equation for the tangent line to f (x) = 3x2 − π 3 at x = 4. ⇒ 9. Suppose the position of an object at time t is given by f (t) = −49t2 /10 + 5t + 10. Find a function giving the speed of the object at time t. The acceleration of an object is the rate at which its speed is changing, which means it is given by the deriviative of the speed function. Find the acceleration of the object at time t. ⇒ 10. Let f (x) = x3 and c = 3. Sketch the graphs of f , cf , f 0 , and (cf )0 on the same diagram. 11. Suppose that f and g are differentiable at x. Show that f − g is differentiable at x using the two linearity properties from this section. n X 12. The general polynomial P of degree n in the variable x has the form P (x) = ak xk = a0 + a1 x + . . . + an xn . What is the derivative (with respect to x) of P ? ⇒ k=0 13. Find a cubic polynomial whose graph has horizontal tangents at (−2, 5) and (2, 3). ⇒ 3.3 The Product Rule Consider the product of two simple functions, say f (x) = (x2 + 1)(x3 − 3x). An obvious guess for the derivative of f is the product of the derivatives of the constituent functions: (2x)(3x2 −3) = 6x3 −6x. Is this correct? We can easily check, by rewriting f and doing the calculation in a way that is known to work. First, f (x) = x5 −3x3 +x3 −3x = x5 −2x3 −3x, and then f 0 (x) = 5x4 − 6x2 − 3. Not even close! What went “wrong”? Well, nothing really, except the guess was wrong. So the derivative of f (x)g(x) is NOT as simple as f 0 (x)g 0 (x). Surely there is some rule for such a situation? There is, and it is instructive to “discover” it by trying to do 3.3 The Product Rule 51 the general calculation even without knowing the answer in advance. d f (x + ∆x)g(x + ∆x) − f (x)g(x) (f (x)g(x)) = lim ∆x→0 dx ∆x f (x + ∆x)g(x + ∆x) − f (x + ∆x)g(x) + f (x + ∆x)g(x) − f (x)g(x) = lim ∆x→0 ∆x f (x + ∆x)g(x + ∆x) − f (x + ∆x)g(x) f (x + ∆x)g(x) − f (x)g(x) = lim + lim ∆x→0 ∆x→0 ∆x ∆x g(x + ∆x) − g(x) f (x + ∆x) − f (x) + lim g(x) = lim f (x + ∆x) ∆x→0 ∆x→0 ∆x ∆x = f (x)g 0 (x) + f 0 (x)g(x) A couple of items here need discussion. First, we used a standard trick, “add and subtract the same thing”, to transform what we had into a more useful form. After some rewriting, we realize that we have two limits that produce f 0 (x) and g 0 (x). Of course, f 0 (x) and g 0 (x) must actually exist for this to make sense. We also replaced lim∆x→0 f (x + ∆x) with f (x)—why is this justified? What we really need to know here is that limx→a f (x) = f (a), that is, the values of f (x) get close to f (a) as x gets close to a. This is certainly not always true, as in the “greatest integer” or “floor” function, for example. The value of the function f (x) = bxc is “x rounded down” or “the greatest integer less than or equal to x.” For example, b4.5c = 4, b47c = 47. The graph of this function is indicated in figure 3.1. As x gets close to 1 from the left, f (x) gets close to 0 (it actually is 0) but f (1) = 1. If limx→a f (x) = f (a) we say that f is continuous at a. What we need to know above is that f (x) is continuous, at least at some point that we’re interested in. We already know that f 0 (x) exists (or the whole approach, writing the derivative of f g in terms of f 0 and g 0 , doesn’t make sense). This turns out to imply that f is continuous as well. Here’s why: lim f (x + ∆x) = lim (f (x + ∆x) − f (x) + f (x)) ∆x→0 ∆x→0 f (x + ∆x) − f (x) ∆x + lim f (x) ∆x→0 ∆x→0 ∆x = lim = f 0 (x) · 0 + f (x) = f (x) To summarize: the product rule says that d (f (x)g(x)) = f (x)g 0 (x) + f 0 (x)g(x). dx Returning to the example we started with, let f (x) = (x2 + 1)(x3 − 3x). Then f 0 (x) = (x2 + 1)(3x2 − 3) + (2x)(x3 − 3x) = 3x4 − 3x2 + 3x2 − 3 + 2x4 − 6x2 = 5x4 − 6x2 − 3, 52 Chapter 3 Rules For Finding Derivatives • • • • • Figure 3.1 ◦ ◦ ◦ ◦ The greatest integer function. as before. In this case it is probably simpler to multiply f (x) out first, then compute the derivative; here’s an example for which we really need the product rule. √ EXAMPLE 3.5 Compute the derivative of f (x) = x2 625 − x2 . We have already √ √ d computed dx 625 − x2 = −x/ 625 − x2 . Now f 0 (x) = x2 √ p −x3 + 2x(625 − x2 ) −3x3 + 1250x −x √ + 2x 625 − x2 = = √ . 625 − x2 625 − x2 625 − x2 Exercises In 1–4, find the derivatives of the functions using the product rule. 1. x3 (x3 − 5x + 10) ⇒ 2. (x2 + 5x − 3)(x5 − 6x3 + 3x2 − 7x + 1) ⇒ √ √ 3. x 625 − x2 ⇒ √ 625 − x2 4. ⇒ x20 5. Use the product rule to compute the derivative of f (x) = (2x − 3)2 . Sketch the function. Find an equation of the tangent line to the curve at x = 2. Sketch the tangent line at x = 2. ⇒ 6. Suppose that f , g, and h are differentiable functions. Show that (f gh)0 (x) = f 0 (x)g(x)h(x) + f (x)g 0 (x)h(x) + f (x)g(x)h0 (x). 7. State and prove a rule to compute (f ghi)0 (x), similar to the rule in the previous problem. Product notation. be written Suppose f1 , f2 , . . . fn are functions. The product of all these functions can n Y k=1 fk . 3.4 This is similar to the use of P The Quotient Rule 53 to denote a sum. For example, 5 Y fk = f1 f2 f3 f4 f5 k=1 and n Y k = 1 · 2 · . . . · n = n!. k=1 We sometimes use somewhat more complicated conditions; for example n Y fk k=1,k6=j denotes the product of f1 through fn except for fj . For example, 5 Y xk = x · x2 · x3 · x5 = x11 . k=1,k6=4 8. The generalized product rule says that if f1 , f2 , . . . , fn are differentiable functions at x then 1 0 n n n X Y Y d @fj0 (x) fk (x) = fk (x)A . dx j=1 k=1 k=1,k6=j Verify that this is the same as your answer to the previous problem when n = 4, and write out what this says when n = 5. 3.4 The Quotient Rule What is the derivative of (x2 + 1)/(x3 − 3x)? More generally, we’d like to have a formula to compute the derivative of f (x)/g(x) if we already know f 0 (x) and g 0 (x). Instead of attacking this problem head-on, let’s notice that we’ve already done part of the problem: f (x)/g(x) = f (x) · (1/g(x)), that is, this is “really” a product, and we can compute the derivative if we know f 0 (x) and (1/g(x))0 . So really the only new bit of information we need is (1/g(x))0 in terms of g 0 (x). As with the product rule, let’s set this up and see how 54 Chapter 3 Rules For Finding Derivatives far we can get: d 1 = lim dx g(x) ∆x→0 = lim 1 g(x+∆x) − 1 g(x) ∆x g(x)−g(x+∆x) g(x+∆x)g(x) ∆x g(x) − g(x + ∆x) = lim ∆x→0 g(x + ∆x)g(x)∆x ∆x→0 = lim − ∆x→0 =− g(x + ∆x) − g(x) 1 ∆x g(x + ∆x)g(x) g 0 (x) g(x)2 Now we can put this together with the product rule: −g 0 (x) −f (x)g 0 (x) + f 0 (x)g(x) d f (x) 1 f 0 (x)g(x) − f (x)g 0 (x) 0 = f (x) = + f (x) = . dx g(x) g(x)2 g(x) g(x)2 g(x)2 EXAMPLE 3.6 Compute the derivative of (x2 + 1)/(x3 − 3x). d x2 + 1 2x(x3 − 3x) − (x2 + 1)(3x2 − 3) −x4 − 6x2 + 3 = = . dx x3 − 3x (x3 − 3x)2 (x3 − 3x)2 It is often possible to calculate derivatives in more than one way, as we have already seen. Since every quotient can be written as a product, it is always possible to use the product rule to compute the derivative, though it is not always simpler. √ √ EXAMPLE 3.7 Find the derivative of 625 − x2 / x in two ways: using the quotient rule, and using the product rule. Quotient rule: √ √ √ √ √ d 625 − x2 x(−x/ 625 − x2 ) − 625 − x2 · 1/(2 x) √ = . dx x x Note that we have used Product rule: √ x = x1/2 to compute the derivative of √ x by the power rule. p d p −x −1 −3/2 x +√ 625 − x2 x−1/2 = 625 − x2 x−1/2 . 2 dx 2 625 − x 3.4 The Quotient Rule 55 With a bit of algebra, both of these simplify to x2 + 625 . − √ 2 625 − x2 x3/2 Occasionally you will need to compute the derivative of a quotient with a constant numerator, like 10/x2 . Of course you can use the quotient rule, but it is usually not the easiest method. If we do use it here, we get x2 · 0 − 10 · 2x −20 d 10 = = 3 , 2 4 dx x x x since the derivative of 10 is 0. But it is simpler to do this: d 10 d = 10x−2 = −20x−3 . dx x2 dx Admittedly, x2 is a particularly simple denominator, but we will see that a similar calculation is usually possible. Another approach is to remember that −g 0 (x) d 1 = , dx g(x) g(x)2 but this requires extra memorization. Using this formula, d 10 −2x = 10 . dx x2 x4 Note that we first use linearity of the derivative to pull the 10 out in front. 56 Chapter 3 Rules For Finding Derivatives Exercises Find the derivatives of the functions in 1–4 using the quotient rule. √ x3 1. 3 ⇒ x − 5x + 10 2. x 3. √ ⇒ 625 − x2 √ 625 − x2 4. ⇒ x20 x2 + 5x − 3 ⇒ x5 − 6x3 + 3x2 − 7x + 1 5. Find an equation for the tangent line to f (x) = (x2 − 4)/(5 − x) at x = 3. ⇒ 6. Find an equation for the tangent line to f (x) = (x − 2)/(x3 + 4x − 1) at x = 1. ⇒ 7. Let P be a polynomial of degree n and let Q be a polynomial of degree m (with Q not the zero polynomial). Using sigma notation we can write P = n X k=0 ak xk , Q= m X bk x k . k=0 Use sigma notation to write the derivative of the rational function P/Q. 8. The curve y = 1/(1 + x2 ) is an example of a class of curves each of which is called a witch of Agnesi. Sketch the curve and find the tangent line to the curve at x = 5. (The word witch here is a mistranslation of the original Italian, as described at http://mathworld.wolfram.com/WitchofAgnesi.html and http://instructional1.calstatela.edu/sgray/Agnesi/ WitchHistory/Historynamewitch.html.) ⇒ d f 9. If f 0 (4) = 5, g 0 (4) = 12, (f g)(4) = f (4)g(4) = 2, and g(4) = 6, compute f (4) and at 4. dx g ⇒ 3.5 The Chain Rule So far we have seen how to compute the derivative of a function built up from other functions by addition, subtraction, multiplication and division. But there is another very important way that we combine simple functions to make more complicated functions. For √ example, how is 625 − x2 “built”? It has many simpler components, like 625 and x2 , √ and then there is that square root symbol, so in a way the square root function x = x1/2 is involved. One way to view this is that we have taken a simple function, 625 − x2 , and √ √ put it “inside” the function x—that is, we have replaced the variable x in x by another function. The obvious question is: can we compute the derivative using the derivatives √ of the constituents 625 − x2 and x? We can indeed. In general, if f (x) and g(x) are functions, we can compose them to get a new function by replacing the variable in one with the other function. There are two ways to do this, and they are generally quite different. 3.5 The Chain Rule 57 √ EXAMPLE 3.8 Form the two possible compositions of f (x) = x and g(x) = 625−x2 . √ √ First, f (g(x)) = 625 − x2 ; second g(f (x)) = 625 − ( x)2 = 625 − x. Suppose we want the derivative of f (g(x)). Again, let’s set up the derivative and play some algebraic tricks: d f (g(x + ∆x)) − f (g(x)) f (g(x)) = lim ∆x→0 dx ∆x f (g(x + ∆x)) − f (g(x)) g(x + ∆x)) − g(x) = lim ∆x→0 g(x + ∆x)) − g(x) ∆x Now we see immediately that the second fraction turns into g 0 (x) when we take the limit. The first fraction is more complicated, but it too looks something like a derivative. The denominator, g(x + ∆x)) − g(x), is a change in the value of g, so let’s abbreviate it as ∆g = g(x + ∆x)) − g(x), which also means g(x + ∆x) = g(x) + ∆g. This gives us f (g(x) + ∆g) − f (g(x)) . ∆x→0 ∆g lim As ∆x goes to 0, it is also true that ∆g goes to 0, because g(x + ∆x) goes to g(x). So we can rewrite this limit as f (g(x) + ∆g) − f (g(x)) lim . ∆g→0 ∆g Now this looks exactly like a derivative, namely f 0 (g(x)), that is, the function f 0 (x) with x replaced by g(x). If this all withstands scrutiny, we then get d f (g(x)) = f 0 (g(x))g 0 (x). dx Unfortunately, there is a small flaw in the argument. Recall that what we mean by lim∆x→0 involves what happens when ∆x is close to 0 but not equal to 0. The qualification is very important, since we must be able to divide by ∆x. But when ∆x is close to 0 but not equal to 0, ∆g = g(x + ∆x)) − g(x) is close to 0 but possibly equal to 0. This means it doesn’t really make sense to divide by ∆g. Fortunately, it is possible to recast the argument to avoid this difficulty, but it is a bit tricky; we will not include the details, which can be found in many calculus books. The chain rule has a particularly simple expression if we use the Leibniz notation for the derivative. The quantity f 0 (g(x)) is the derivative of f with x replaced by g; this can be written df /dg. As usual, g 0 (x) = dg/dx. Then the chain rule becomes df df dg = . dx dg dx 58 Chapter 3 Rules For Finding Derivatives This looks like trivial arithmetic, but it is not: dg/dx is not a fraction, that is, not literal division, but a single symbol that means g 0 (x). Nevertheless, it turns out that what looks like trivial arithmetic, and is therefore easy to remember, is really true. It will take a bit of practice to make the use of the chain rule come naturally—it is more complicated than the earlier differentiation rules we have seen. √ EXAMPLE 3.9 Compute the derivative of 625 − x2 . We already know that the √ answer is −x/ 625 − x2 , computed directly from the limit. In the context of the chain √ rule, we have f (x) = x, g(x) = 625 − x2 . We know that f 0 (x) = (1/2)x−1/2 , so f 0 (g(x)) = (1/2)(625 − x2 )−1/2 . Note that this is a two step computation: first compute f 0 (x), then replace x by g(x). Since g 0 (x) = −2x we have 1 −x f 0 (g(x))g 0 (x) = √ (−2x) = √ . 2 625 − x2 625 − x2 √ EXAMPLE 3.10 Compute the derivative of 1/ 625 − x2 . This is a quotient with a constant numerator, so we could use the quotient rule, but it is simpler to use the chain rule. The function is (625 − x2 )−1/2 , the composition of f (x) = x−1/2 and g(x) = 625 − x2 . We compute f 0 (x) = (−1/2)x−3/2 using the power rule, and then f 0 (g(x))g 0 (x) = −1 x (−2x) = . 2 3/2 2(625 − x ) (625 − x2 )3/2 In practice, of course, you will need to use more than one of the rules we have developed to compute the derivative of a complicated function. EXAMPLE 3.11 Compute the derivative of x2 − 1 f (x) = √ . x x2 + 1 The “last” operation here is division, so to get started we need to use the quotient rule first. This gives √ √ 2 0 2 + 1 − (x2 − 1)(x x2 + 1)0 (x − 1) x x f 0 (x) = x2 (x2 + 1) √ √ 2x2 x2 + 1 − (x2 − 1)(x x2 + 1)0 . = x2 (x2 + 1) 3.5 The Chain Rule 59 √ Now we need to compute the derivative of x x2 + 1. This is a product, so we use the product rule: p d p 2 d p 2 x + 1 + x2 + 1. x x +1=x dx dx Finally, we use the chain rule: d p 2 d 2 1 x x +1= . (x + 1)1/2 = (x2 + 1)−1/2 (2x) = √ dx dx 2 x2 + 1 And putting it all together: √ √ 2x2 x2 + 1 − (x2 − 1)(x x2 + 1)0 . f (x) = x2 (x2 + 1) µ ¶ √ √ x 2 2 2 2 2x x + 1 − (x − 1) x √ + x +1 x2 + 1 = . x2 (x2 + 1) 0 This can be simplified of course, but we have done all the calculus, so that only algebra is left. q EXAMPLE 3.12 Compute the derivative of 1+ p √ 1 + x. Here we have a more complicated chain of compositions, so we use the chain rule twice. At the outermost p √ √ “layer” we have the function g(x) = 1 + 1 + x plugged into f (x) = x, so applying the chain rule once gives d dx r ¶ ¶ µ µ q q q √ √ −1/2 d √ 1 1+ 1+ x= 1+ 1+ x 1+ 1+ x . 2 dx Now we need the derivative of d dx p 1+ √ x. Using the chain rule again: q √ √ ¢−1/2 1 −1/2 1¡ 1+ x= 1+ x x . 2 2 So the original derivative is d dx r µ ¶ q q √ √ −1/2 1 ¡ √ ¢−1/2 1 −1/2 1 1+ 1+ x= 1+ 1+ x 1+ x x . 2 2 2 1 q = √ p p √ √ 8 x 1+ x 1+ 1+ x 60 Chapter 3 Rules For Finding Derivatives Using the chain rule, the power rule, and the product rule, it is possible to avoid using the quotient rule entirely. EXAMPLE 3.13 then Compute the derivative of f (x) = x3 x2 +1 . Write f (x) = x3 (x2 + 1)−1 , d 2 (x + 1)−1 + 3x2 (x2 + 1)−1 dx = x3 (−1)(x2 + 1)−2 (2x) + 3x2 (x2 + 1)−1 f 0 (x) = x3 = −2x4 (x2 + 1)−2 + 3x2 (x2 + 1)−1 = −2x4 3x2 + (x2 + 1)2 x2 + 1 = −2x4 3x2 (x2 + 1) + (x2 + 1)2 (x2 + 1)2 = −2x4 + 3x4 + 3x2 x4 + 3x2 = (x2 + 1)2 (x2 + 1)2 Note that we already had the derivative on the second line; all the rest is simplification. It is easier to get to this answer by using the quotient rule, so there’s a trade off: more work for fewer memorized formulas. Exercises Find the derivatives of the functions. For extra practice, and to check your answers, do some of these in more than one way if possible. r 169 1. x4 − 3x3 + (1/2)x2 + 7x − π ⇒ 12. −x ⇒ x √ 3 2 p 2. x − 2x + 4 x ⇒ 13. x3 − x2 − (1/x) ⇒ 3. (x2 + 1)3 ⇒ √ 14. 100/(100 − x2 )3/2 ⇒ √ 4. x 169 − x2 ⇒ √ 15. 3 x + x3 ⇒ 2 q 5. (x − 4x + 5) 25 − x2 ⇒ p √ 2 + 1)2 + 16. (x 1 + (x2 + 1)2 ⇒ 2 2 6. r − x , r is a constant ⇒ √ 17. (x + 8)5 ⇒ 7. 1 + x4 ⇒ 1 18. (4 − x)3 ⇒ 8. p √ . ⇒ 5− x 19. (x2 + 5)3 ⇒ 9. (1 + 3x)2 ⇒ 2 (x + x + 1) ⇒ (1 − x) √ 25 − x2 11. ⇒ x 10. 20. (6 − 2x2 )3 ⇒ 21. (1 − 4x3 )−2 ⇒ 22. 5(x + 1 − 1/x) ⇒ 3.5 23. 4(2x2 − x + 3)−2 ⇒ 1 24. ⇒ 1 + 1/x −3 25. ⇒ 2 4x − 2x + 1 26. (x2 + 1)(5 − 2x)/2 ⇒ 27. (3x2 + 1)(2x − 4)3 ⇒ x+1 28. ⇒ x−1 x2 − 1 29. 2 ⇒ x +1 The Chain Rule 61 (x − 1)(x − 2) ⇒ x−3 2x−1 − x−2 31. ⇒ 3x−1 − 4x−2 32. 3(x2 + 1)(2x2 − 1)(2x + 3) ⇒ 1 33. ⇒ (2x + 1)(x − 3) 30. 34. ((2x + 1)−1 + 3)−1 ⇒ 35. (2x + 1)3 (x2 + 1)2 ⇒ 36. Find an equation for the tangent line to f (x) = (x − 2)1/3 /(x3 + 4x − 1)2 at x = 1. ⇒ 37. Find an equation for the tangent line to y = 9x−2 at (3, 1). ⇒ √ 38. Find an equation for the tangent line to (x2 − 4x + 5) 25 − x2 at (3, 8). ⇒ (x2 + x + 1) at (2, −7). ⇒ (1 − x) q p p √ 40. Find an equation for the tangent line to (x2 + 1)2 + 1 + (x2 + 1)2 at (1, 4 + 5). ⇒ 39. Find an equation for the tangent line to 4 Transcendental Functions So far we have used only algebraic functions as examples when finding derivatives, that is, functions that can be built up by the usual algebraic operations of addition, subtraction, multiplication, division, and raising to constant powers. Both in theory and practice there are other functions, called transcendental, that are very useful. Most important among these are the trigonometric functions, the inverse trigonometric functions, exponential functions, and logarithms. 4.1 Trigonometric Functions When you first encountered the trigonometric functions it was probably in the context of “triangle trigonometry,” defining, for example, the sine of an angle as the “side opposite over the hypotenuse.” While this will still be useful in an informal way, we need to use a more expansive definition of the trigonometric functions. First an important note: while degree measure of angles is sometimes convenient because it is so familiar, it turns out to be ill-suited to mathematical calculation, so (almost) everything we do will be in terms of radian measure of angles. 63 64 Chapter 4 Transcendental Functions To define the radian measurement system, we consider the unit circle in the xy-plane: ......................................................... .......... ........ ........ ....... ....... ...... . . . . . ...... ... . . . . ..... .. . . ..... . . ... ..... . . . . . ..... . . . .. ... . . ... (cos x, sin x) . . . .. . . . A . ........ . . . . .. .. .... . . . . . . . . . ... . ... . . . . . . . ... ...... ... ... ...... ... ... ....... . . . . .... . ... ... . . . . . .. ... .. . . . . . ... . ... ... . . . . . .. .. x ... . . . . . .. . .......... ... .. ....... y ... ... ....... . ... . ....... .. ....... ... . ....... .. ... ....... ... ... ....... ... ....... ... . . ....... ... ....... ... ... ....... .... ... ...... ... . . ... . B... ... .. ..... ..... ..... .... ..... . . . ..... .. . . . . ...... ...... ...... ...... ....... ....... ........ ........ ........... . . . . . . . . . . .............................................. An angle, x, at the center of the circle is associated with an arc of the circle which it is said to subtend. In the figure, this arc is the portion of the circle from point (1, 0) to point A. The length of this arc is the radian measure of the angle x; the fact that the radian measure is an actual geometric length is largely responsible for the usefulness of radian measure. The circumference of the unit circle is 2πr = 2π(1) = 2π, so the radian measure of the full circular angle (that is, of the 360 degree angle) is 2π. While an angle with a particular measure can appear anywhere around the circle, we need a fixed, conventional location so that we can use the coordinate system to define properties of the angle. The standard convention is to place the starting radius for the angle on the positive x-axis, and to measure positive angles counterclockwise around the circle. In the figure, x is the standard location of the angle π/6, that is, the length of the arc from (1, 0) to A is π/6. The angle y in the picture is −π/6, because the distance from (1, 0) to B along the circle is also π/6, but in a clockwise direction. Now the fundamental trigonometric definitions are: the cosine of x and the sine of x are the first and second coordinates of the point A, as indicated in the figure. The angle x shown can be viewed as an angle of a right triangle, meaning the usual triangle definitions of the sine and cosine also make sense. Since the hypotenuse of the triangle is 1, the “side opposite over hypotenuse” definition of the sine is the second coordinate of point A over 1, which is just the second coordinate; in other words, both methods give the same value for the sine. The simple triangle definitions work only for angles that can “fit” in a right triangle, namely, angles between 0 and π/2. The coordinate definitions, on the other hand, apply 4.1 Trigonometric Functions 65 to any angles, as indicated in this figure: ................................................................................. ................... ........................... ..... ..... ..... ..... .. . . . . . ................... . . . . .... ... ... . ... ............... . . . . . . .. .. . . . .............. . . ... ... ... ...... . . . . . . .... . . . .... . . .... ........ ............. ......... ....... ........ ........... ......... ....... ......... x ....... ...... ... . ........ . . . . .... .. ...... . . . ........ . . . . . ..... ... ...... . . . ......... . . . . . ....... .. ... ... ... ........ ......................... ... . . .. ........A ... ....... ....... ... .. ... (cos x, sin x) ... .. .... ... ..... .... ..... ..... . . ..... . . . ...... ..... ...... ..... ....... ...... ........ ....... . . . . .......... . . . ....... .................. ............................... The angle x subtends the heavy arc in the figure, that is, x = 7π/6. Both coordinates of point A in this figure are negative, so the sine and cosine of 7π/6 are both negative. The remaining trigonometric functions can be most easily defined in terms of the sine and cosine, as usual: sin x tan x = cos x cos x cot x = sin x 1 sec x = cos x 1 csc x = sin x and they can also be defined as the corresponding ratios of coordinates. Although the trigonometric functions are defined in terms of the unit circle, the unit circle diagram is not what we normally consider the graph of a trigonometric function. (The unit circle is the graph of, well, the circle.) We can easily get a qualitatively correct idea of the graphs of the trigonometric functions from the unit circle diagram. Consider the sine function, y = sin x. As x increases from 0 in the unit circle diagram, the second coordinate of the point A goes from 0 to a maximum of 1, then back to 0, then to a minimum of −1, then back to 0, and then it obviously repeats itself. So the graph of y = sin x must look something like this: ........................... .............................. 1 ........ ....... ....... ...... ...... ...... ...... ...... ..... . ..... . ..... . . . . . . . . . . . ..... ..... .. .. . . . . . . . . . . ..... ..... .. .. . . . . . ..... . . . . ..... . .. . . . . ..... . . . . . ..... .. . ..... . .... . . . ..... .. ..... . ..... . . ..... . ..... .. ..... . . ..... . . ..... ..... .. ..... . . . . . . . . . . −2π −3π/2 −π ........... −π/2 ............ π/2 π ............ 3π/2 ............ 2π ....... ....... ....... ....... ......... ......... ........................ −1 ........................ 66 Chapter 4 Transcendental Functions Similarly, as angle x increases from 0 in the unit circle diagram, the first coordinate of the point A goes from 1 to 0 then to −1, back to 0 and back to 1, so the graph of y = cos x must look something like this: ............... 1.................................... .............. ....... ....... ....... ...... ...... ...... ...... ..... ..... ..... ..... ..... . . . . . . . . . . . . ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... .... ..... ..... ..... .... ..... . . . ..... . . . . . . . . . ..... ..... ..... ..... ..... ..... ..... ..... ..... ... ..... ..... ..... ..... 3π/2 ..... −π/2 ...... −2π −3π/2.............. −π π/2 π 2π . . . . . . . . . ...... .... ....... ...... ....... ....... ......... ................................. ....................... −1 Exercises Some useful trigonometric identities are in the introduction, on page xiv. 1. Find all values of θ such that sin(θ) = −1; give your answer in radians. ⇒ 2. Find all values of θ such that cos(2θ) = 1/2; give your answer in radians. ⇒ 3. Use an angle sum identity to compute cos(π/12). ⇒ 4. Use an angle sum identity to compute tan(5π/12). ⇒ 5. Verify the identity cos2 (t)/(1 − sin(t)) = 1 + sin(t). 6. Verify the identity 2 csc(2θ) = sec(θ) csc(θ). 7. Verify the identity sin(3θ) − sin(θ) = 2 cos(2θ) sin(θ). 8. Sketch y = 2 sin(x). 9. Sketch y = sin(3x). 10. Sketch y = sin(−x). 11. Find all of the solutions of 2 sin(t) − 1 − sin2 (t) = 0 in the interval [0, 2π]. ⇒ 4.2 The Derivative of sin x What about the derivative of the sine function? The rules for derivatives that we have are of no help, since sin x is not an algebraic function. We need to return to the definition of the derivative, set up a limit, and try to compute it. Here’s the definition: d sin(x + ∆x) − sin x sin x = lim . ∆x→0 dx ∆x Using some trigonometric identies, we can make a little progress on the quotient: sin(x + ∆x) − sin x sin x cos ∆x + sin ∆x cos x − sin x = ∆x ∆x sin ∆x cos ∆x − 1 + cos x . = sin x ∆x ∆x 4.3 A hard limit 67 This isolates the difficult bits in the two limits cos ∆x − 1 ∆x→0 ∆x lim and sin ∆x . ∆x→0 ∆x lim Here we get a little lucky: it turns out that once we know the second limit the first is quite easy. The second is quite tricky, however. Indeed, it is the hardest limit we will actually compute, and we devote a section to it. 4.3 A hard limit We want to compute this limit: sin ∆x . ∆x→0 ∆x lim Equivalently, to make the notation a bit simpler, we can compute lim x→0 sin x . x In the original context we need to keep x and ∆x separate, but here it doesn’t hurt to rename ∆x to something more convenient. To do this we need to be quite clever, and to employ some indirect reasoning. The indirect reasoning is embodied in a theorem, frequently called the squeeze theorem. THEOREM 4.1 Squeeze Theorem Suppose that g(x) ≤ f (x) ≤ h(x) for all x close to a but not equal to a. If limx→a g(x) = L = limx→a h(x), then limx→a f (x) = L. This theorem can be proved using the official definition of limit. We won’t prove it here, but point out that it is easy to understand and believe graphically. The condition says that f (x) is trapped between g(x) below and h(x) above, and that at x = a, both g and h approach the same value. This means the situation looks something like figure 4.1. The wiggly curve is x2 sin(π/x), the upper and lower curves are x2 and −x2 . Since the sine function is always between −1 and 1, −x2 ≤ x2 sin(π/x) ≤ x2 , and it is easy to see that limx→0 −x2 = 0 = limx→0 x2 . It is not so easy to see directly, that is algebraically, that limx→0 x2 sin(π/x) = 0, because the π/x prevents us from simply plugging in x = 0. The squeeze theorem makes this “hard limit” as easy as the trivial limits involving x2 . To do the really hard limit that we want, limx→0 (sin x)/x, we will find two simpler functions g and h so that g(x) ≤ (sin x)/x ≤ h(x), and so that limx→0 g(x) = limx→0 h(x). Not too surprisingly, this will require some trigonometry and geometry. Refering to figure 4.2, x is the measure of the angle in radians. Since the circle has radius 1, the coordinates of 68 Chapter 4 Transcendental Functions ... ... ... ... ... ... ... .. ... . ... ... ... ... ... ... ... . . . ... ... ... ... ... ... ... ... .... . . . .... ... .... ... .... ............ .... . ..... ........ . . . . . .... ... ...... ... .... ......... ... .... ... ..... ... .... . . . . . . . ... ..... . .... ... ...... ... ..... ... ....... ... ..... ...... ...... ... ... . . . . . . . . . . . ....... ... .. ....... ... ....... ... ... .......... ..... ......... ... ......... .. ... . ..... ... ................ .... . ... . . . .. .. ......................................... . ... . . . . . . . . . . ... ... .. .. . ..................................................................... ... .... .. ... ............. .. ... ... ........................... ... . ........... ... . . . . ... . . . . . ... ........ . ... ............. . . . . . ... ....... .............. ...... ... ... ...... ..... ... ...... ..... .. ... ..... . . . . . . . . . . . . ..... .. . . ..... ... ..... ... .... ....... ... .... ...... ... .... ..... . . . . . .... .... .... ....... ... .............. ... .... ... . .... . . . . .... . . .. ... . . ... .. . . ... . . . ... .. . ... . . ... . . . ... . . ... . . . ... .. . ... . . . ... .. . ... .. ... . .. Figure 4.1 The squeeze theorem. point A are (cos x, sin x), and the area of the small triangle is (cos x sin x)/2. This triangle is completely contained within the circular wedge-shaped region bordered by two lines and the circle from (1, 0) to point A. Comparing the areas of the triangle and the wedge we see (cos x sin x)/2 ≤ x/2, since the area of a circular region with angle θ and radius r is θr2 /2. With a little algebra this turns into (sin x)/x ≤ 1/ cos x, giving us the h we seek. To find g, we note that the circular wedge is completely contained inside the larger triangle. The height of the triangle, from (1, 0) to point B, is tan x, so comparing areas we get x/2 ≤ (tan x)/2 = sin x/(2 cos x). With a little algebra this becomes cos x ≤ (sin x)/x. So now we have sin x 1 cos x ≤ ≤ . x cos x Finally, the two limits limx→0 cos x and limx→0 1/ cos x are easy, because cos(0) = 1. By the squeeze theorem, limx→0 (sin x)/x = 1 as well. Before we can complete the calculation of the derivative of the sine, we need one other limit: cos x − 1 lim . x→0 x This limit is just as hard as sin x/x, but closely related to it, so that we don’t have to a similar calculation; instead we can do a bit of tricky algebra. cos x − 1 cos x + 1 cos2 x − 1 − sin2 x sin x sin x cos x − 1 = = = =− . x x cos x + 1 x(cos x + 1) x(cos x + 1) x cos x + 1 4.3 1 ............................ ............ ......... ........ ....... ...... ...... ...... ..... ..... ..... ..... ..... ... ... ... .......... .. ...... ........ . . . .... ..... ....... . ... . . . . ... ...... ...... ... ....... . ... . . . . ... . ... . . . . .. . . . ... . . . ... . . ... . . . ... . . . ... . . ... . . . ... . . .. . . . . . ... . ... . . . . . ... ... ... . . . . . .. . . A hard limit 69 B A 0 x 1 Figure 4.2 Visualizing sin x/x. To compute the desired limit it is sufficient to compute the limits of the two final fractions, as x goes to 0. The first of these is the hard limit we’ve just done, namely 1. The second turns out to be simple, because the denominator presents no problem: sin 0 0 sin x = = = 0. x→0 cos x + 1 cos 0 + 1 2 lim Thus, lim x→0 cos x − 1 = 0. x Exercises sin(5x) ⇒ x sin(7x) lim ⇒ x→0 sin(2x) cot(4x) lim ⇒ x→0 csc(3x) tan x lim ⇒ x→0 x sin x − cos x lim ⇒ x→π/4 cos(2x) 1. Compute lim x→0 2. Compute 3. Compute 4. Compute 5. Compute 6. For all x ≥ 0, 4x − 9 ≤ f (x) ≤ x2 − 4x + 7. Find lim f (x). ⇒ x→4 4 2 7. For all x, 2x ≤ g(x) ≤ x − x + 2. Find lim g(x). ⇒ x→1 8. Use the Squeeze Theorem to show that lim x4 cos(2/x) = 0. x→0 70 Chapter 4 Transcendental Functions 4.4 The Derivative of sin x , continued Now we can complete the calculation of the derivative of the sine: d sin(x + ∆x) − sin x sin x = lim ∆x→0 dx ∆x cos ∆x − 1 sin ∆x = lim sin x + cos x ∆x→0 ∆x ∆x = sin x · 0 + cos x · 1 = cos x. The derivative of a function measures the slope or steepness of the function; if we examine the graphs of the sine and cosine side by side, it should be that the latter appears to accurately describe the slope of the former, and indeed this is true: 1 −1 ......................... ......... ....... ...... ...... ...... . ..... . . . .. ..... . . . . ..... .. . . . ..... . .. . . ..... . . ..... ..... ..... ..... ..... ..... ..... ..... . . . ..... . ...... ..... 2π π/2 π ...... 3π/2 ......... . ....... ................................... sin x 1 −1 ............... ........ ....... ......... ...... ....... ...... ..... . . . . ..... ..... ..... ..... ..... ..... ..... ..... ..... . . . . ..... .... ..... ..... ..... ..... ..... ..... . . . . . . . . . π/2 ....... π 2π .. 3π/2 ...... ....... ....... ......... ....................... cos x Notice that where the cosine is zero the sine does appear to have a horizontal tangent line, and that the sine appears to be steepest where the cosine takes on its extreme values of 1 and −1. Of course, now that we know the derivative of the sine, we can compute derivatives of more complicated functions involving the sine. EXAMPLE 4.2 Compute the derivative of sin(x2 ). d sin(x2 ) = cos(x2 ) · 2x = 2x cos(x2 ). dx EXAMPLE 4.3 Compute the derivative of sin2 (x3 − 5x). d d sin2 (x3 − 5x) = (sin(x3 − 5x))2 dx dx = 2(sin(x3 − 5x))1 cos(x3 − 5x)(3x2 − 5) = 2(3x2 − 5) cos(x3 − 5x) sin(x3 − 5x). Exercises Find the derivatives of the following functions. √ 1. sin2 ( x) ⇒ 4.5 Derivatives of the Trigonometric Functions 71 x2 + x ⇒ sin x p 5. 1 − sin2 x ⇒ √ x sin x ⇒ 1 3. ⇒ sin x 2. 4. 4.5 Derivatives of the Trigonometric Functions All of the other trigonometric functions can be expressed in terms of the sine, and so their derivatives can easily be calculated using the rules we already have. For the cosine we need to use two identities, π cos x = sin(x + ), 2 π sin x = − cos(x + ). 2 Now: d cos x = dx d tan x = dx d sec x = dx d π π sin(x + ) = cos(x + ) · 1 = − sin x dx 2 2 2 2 d sin x cos x + sin x 1 = = = sec2 x 2 dx cos x cos x cos2 x d sin x (cos x)−1 = −1(cos x)−2 (− sin x) = = sec x tan x dx cos2 x The derivatives of the cotangent and cosecant are similar and left as exercises. Exercises Find the derivatives of the following functions. 1. sin x cos x ⇒ 6. csc x ⇒ 2. sin(cos x) ⇒ √ 3. x tan x ⇒ 7. x3 sin(23x2 ) ⇒ 4. tan x/(1 + sin x) ⇒ 9. sin(cos(6x)) ⇒ 8. sin2 x + cos2 x ⇒ 5. cot x ⇒ d sec θ . ⇒ dθ 1 + sec θ d 5 11. Compute t cos(6t). ⇒ dt d t3 sin(3t) 12. Compute . ⇒ dt cos(2t) 10. Compute 13. Find all points on the graph of f (x) = sin2 (x) at which the tangent line is horizontal. ⇒ 14. Find all points on the graph of f (x) = 2 sin(x)−sin2 (x) at which the tangent line is horizontal. ⇒ 72 Chapter 4 Transcendental Functions 15. Find an equation for the tangent line to sin2 (x) at x = π/3. ⇒ 16. Find an equation for the tangent line to sec2 x at x = π/3. ⇒ 17. Find an equation for the tangent line to cos2 x − sin2 (4x) at x = π/6. ⇒ 18. Find the points on the curve y = x + 2 cos x that have a horizontal tangent line. ⇒ 19. Let C be a circle of radius r. Let A be an arc on C subtended by a central angle θ. Let B be the the chord of C whose endpoints are the endpoints of A. (Hence, B is also subtended by a central angle θ.) Let s be the length of A and let d be the length of B. Sketch a diagram of the situation and compute limθ→0+ s/d. 4.6 Exponential and Logarithmic functions An exponential function has the form ax , where a is a constant; examples are 2x , 10x , ex . The logarithmic functions are the inverses of the exponential functions, that is, functions that “undo” the exponential functions, just as, for example, the cube root function “un√ 3 does” the cube function: 23 = 2. Note that the original function also undoes the inverse √ function: ( 3 8)3 = 8. Let f (x) = 2x . The inverse of this function is called the logarithm base 2, denoted log2 (x) or (especially in computer science circles) lg(x). What does this really mean? The logarithm must undo the action of the exponential function, so for example it must be that lg(23 ) = 3—starting with 3, the exponential function produces 23 = 8, and the logarithm of 8 must get us back to 3. A little thought shows that it is not a coincidence that lg(23 ) simply gives the exponent—the exponent is the original value that we must get back to. In other words, the logarithm is the exponent. Remember this catchphrase, and what it means, and you won’t go wrong. (You do have to remember what it means. Like any good mnemonic, “the logarithm is the exponent” leaves out a lot of detail, like “Which exponent?” and “Exponent of what?”) EXAMPLE 4.4 What is the value of log10 (1000)? The “10” tells us the appropriate number to use for the base of the exponential function. The logarithm is the exponent, so the question is, what exponent E makes 10E = 1000? If we can find such an E, then log10 (1000) = log10 (10E ) = E; finding the appropriate exponent is the same as finding the logarithm. In this case, of course, it is easy: E = 3 so log10 (1000) = 3. Let’s review some laws of exponents and logarithms. Since a5 = a · a · a · a · a and a3 = a·a·a, it’s clear that a5 ·a3 = a·a·a·a·a·a·a·a = a8 = a5+3 , and in general that am an = am+n . Since “the logarithm is the exponent,” it’s no surprise that this translates directly into a fact about the logarithm function. Here are three facts from the example: loga (a5 ) = 5, loga (a3 ) = 3, loga (a8 ) = 8. So loga (a5 a3 ) = loga (a8 ) = 8 = 5 + 3 = loga (a5 ) + loga (a3 ). 4.6 Exponential and Logarithmic functions 73 Now let’s make this a bit more general. Suppose A and B are two numbers, and A = ax , B = ay . Then loga (AB) = loga (ax ay ) = loga (ax+y ) = x + y = loga (A) + loga (B). Now consider (a5 )3 = a5 · a5 · a5 = a5+5+5 = a5·3 = a15 . Again it’s clear that more generally (am )n = amn , and again this gives us a fact about logarithms. If A = ax then Ay = (ax )y = axy , so loga (Ay ) = xy = y loga (A)—the exponent can be “pulled out in front.” We have cheated a bit in the previous two paragraphs. It is obvious that a5 = a·a·a·a·a and a3 = a · a · a and that the rest of the example follows; likewise for the second example. But when we consider an exponential function ax we can’t be limited to substituting integers for x. What does a2.5 or a−1.3 or aπ mean? And is it really true that a2.5 a−1.3 = a2.5−1.3 ? The answer to the first question is actually quite difficult, so we will evade it; the answer to the second question is “yes.” We’ll evade the full answer to the hard question, but we have to know something about exponential functions. You need first to understand that since it’s not “obvious” what 2x should mean, we are really free to make it mean whatever we want, so long as we keep the behavior that is obvious, namely, when x is a positive integer. What else do we want to be true about 2x ? We want the properties of the previous two paragraphs to be true for all exponents: 2x 2y = 2x+y and (2x )y = 2xy . After the positive integers, the next easiest number to understand is 0: 20 = 1. You have presumably learned this fact in the past; why is it true? It is true precisely because we want 2a 2b = 2a+b to be true about the function 2x . We need it to be true that 20 2x = 20+x = 2x , and this only works if 20 = 1. The same argument implies that a0 = 1 for any a. The next easiest set of numbers to understand is the negative integers: for example, −3 2 = 1/23 . We know that whatever 2−3 means it must be that 2−3 23 = 2−3+3 = 20 = 1, which means that 2−3 must be 1/23 . In fact, by the same argument, once we know what 2x means for some value of x, 2−x must be 1/2x and more generally a−x = 1/ax . Next, consider an exponent 1/q, where q is a positive integer. We want it to be true √ that (2x )y = 2xy , so (21/q )q = 2. This means that 21/q is a q-th root of 2, 21/q = q 2 . This √ √ is all we need to understand that 2p/q = (21/q )p = ( q 2 )p and ap/q = (a1/q )p = ( q a )p . What’s left is the hard part: what does 2x mean when x cannot be written as a √ fraction, like x = 2 or x = π? What we know so far is how to assign meaning to 2x 74 Chapter 4 Transcendental Functions whenever x = p/q; if we were to graph this we’d see something like this: . ... ... ... . . . ... ... ... . . .. ... ... .... . . . . ..... ..... ..... ...... . . . . . .. ...... ...... ....... ........ . . . . . . . . . . .......... ................. ........................... ................................................................................................ But this is a poor picture, because you can’t see that the “curve” is really a whole lot of individual points, above the rational numbers on the x-axis. There are really a lot of “holes” in the curve, above x = π, for example. But (this is the hard part) it is possible to prove that the holes can be “filled in”, and that the resulting function, called 2x , really does have the properties we want, namely that 2x 2y = 2xy and (2x )y = 2xy . Exercises 1. Expand ln((x + 45)7 (x − 2)). 2. Expand ln x3 . 3x − 5 + (7/x) 3. Sketch the graph of y = ln(x − 7)3 + 14. 4. Sketch the graph of y = ln |x| for x 6= 0. 5. Write ln 3x + 17 ln(x − 2) − 2 ln(x2 + 4x + 1) as a single logarithm. 6. Differentiate f (x) = x ln x. 7. Differentiate f (x) = ln(ln(3x)). 8. Sketch the graph of ln(x2 − 2x). √ 9. Solve ln(1 + x) = 6 for x. 2 10. Solve ex = 8 for x. 11. Solve ln(ln(x)) = 1 for x. 12. Sketch the graph of f (x) = e4x−5 + 6. 13. Sketch the graph of f (x) = 3ex+6 − 4. 14. Sketch the graph of y = 36x−1 + 5. 15. Sketch the graph of y = −(1/2)−3x . 16. Sketch the graph of y = 4 log2 (12x + 6) − 2. 17. Sketch the graph of y = ax in the three cases a > 1, a = 1, and 0 < a < 1. What happens to the graph as a → 0+ ? What happens to the graph as a → ∞? 18. Sketch the graph of y = loga x in the two cases a > 1 and 0 < a < 1. What happens to the graph as a → 0+ ? What happens to the graph as a → ∞? 4.7 Derivatives of the exponential and logarithmic functions 75 4.7 Derivatives of the exponential and logarithmic functions As with the sine, we don’t know anything about derivatives that allows us to compute the derivatives of the exponential and logarithmic functions without going back to basics. Let’s do a little work with the definition again: d x ax+∆x − ax a = lim ∆x→0 dx ∆x ax a∆x − ax ∆x→0 ∆x = lim ∆x xa −1 ∆x ∆x a −1 = ax lim ∆x→0 ∆x = lim a ∆x→0 There are two interesting things to note here: As in the case of the sine function we are left with a limit that involves ∆x but not x, which means that whatever lim∆x→0 (a∆x − 1)/∆x is, we know that it is a number, that is, a constant. This means that ax has a remarkable property: its derivative is a constant times itself. We earlier remarked that the hardest limit we would compute was limx→0 sin x/x = 1; we now have a limit that is just a bit too hard to include here. In fact the hard part is to see that lim∆x→0 (a∆x − 1)/∆x even exists—does this fraction really get closer and closer to some fixed value? Yes it does, but we will not prove this fact. We can look at some examples. Consider (2x − 1)/x for some small values of x: 1, 0.828427124, 0.756828460, 0.724061864, 0.70838051, 0.70070877 when x is 1, 1/2, 1/4, 1/8, 1/16, 1/32, respectively. It looks like this is settling in around 0.7, which turns out to be true. Consider next (3x − 1)/x: 2, 1.464101616, 1.264296052, 1.177621520, 1.13720773, 1.11768854, at the same values of x. It turns out to be true that in the limit this is about 1.1. Two examples don’t establish a pattern, but if you do more examples you will find that the limit varies directly with the value of a: bigger a, bigger limit; smaller a, smaller limit. As we can already see, some of these limits will be less than 1 and some larger than 1. Somewhere between a = 2 and a = 3 the limit will be exactly 1; the value at which this happens is called e, so that e∆x − 1 lim = 1. ∆x→0 ∆x As you might guess from our two examples, e is closer to 3 than to 2, and in fact e ≈ 2.718. 76 Chapter 4 Transcendental Functions Now we see that the function ex has a truly remarkable property: d x ex+∆x − ex e = lim ∆x→0 dx ∆x ex e∆x − ex ∆x→0 ∆x e∆x − 1 = lim ex ∆x→0 ∆x ∆x e −1 = ex lim ∆x→0 ∆x = lim = ex That is, ex is its own derivative, or in other words, the slope of ex is the same as its height, or the same as its second coordinate: The function f (x) = ex goes through the point (z, ez ) and has slope ez there, no matter what z is. What about the logarithm function? This too is hard, but as the cosine function was easier to do once the sine was done, so the logarithm is easier to do now that we know the derivative of the exponential function. Let’s start with loge x, which as you probably know is often abbreviated ln x and called the “natural logarithm” function. Consider the relationship between the two functions, namely, that they are inverses, that one “undoes” the other. Graphically this means that they have the same graph except that one is “flipped” or “reflected” through the line y = x: .. ... ... . . .. ... ... .. . . . ... ... ... . . ... .. ... ... . ... .... ..... ..... . . . . ..... ...... ...... ...... . . . . . . .. ........ ......... ........... ................ Figure 4.3 .. ......... ........ ........ . . . . . . . . ....... ...... ....... ...... . . . . . . ...... ..... ..... ..... . . . ... .... ... ... . . . ... ... ... . . ... ... ... . . ... .. .. ..... . The exponential and logarithmic functions. This means that the slopes of these two functions are closely related as well: For example, the slope of ex is e at x = 1; at the corresponding point on the ln(x) curve, the slope must be 1/e, because the “rise” and the “run” have been interchanged. Since the slope of ex is e at the point (1, e), the slope of ln(x) is 1/e at the point (e, 1). 4.7 Derivatives of the exponential and logarithmic functions . ... ... ... . . .. .. ... ... ... .. ....... . . .... ...... ...... ..... . . .... ... ... ... . . .... ...... ........ .......... . . . . ..... .. ..... .. ...... ... ....... ..... . . . . . . . .... ... ......... .. ............ ............... ... ... . . Figure 4.4 77 ...... .......... ...... ......... ...... ........ .................. . . . . . . .. .......... ...... ..... ...... . . . . ..... ....... .......... .............. . . . . . ...... .... ...... ... ..... .. ...... ..... . . . . . .... ... ...... ... ... . . ... .. ... .. . .. .. ... .... The exponential and logarithmic functions. More generally, we know that the slope of ex is ez at the point (z, ez ), so the slope of ln(x) is 1/ez at (ez , z). In other words, the slope of ln x is the reciprocal of the first coordinate at any point; this means that the slope of ln x at (x, ln x) is 1/x. The upshot is: d 1 ln x = . dx x We have discussed this from the point of view of the graphs, which is easy to understand but is not normally considered a “rigorous” proof—it is too easy to be led astray by pictures that seem reasonable but that miss some hard point. It is possible to do this derivation without resorting to pictures, and indeed we will see an alternate approach soon. Note that ln x is defined only for x > 0. It is sometimes useful to consider the function ln |x|, a function defined for x 6= 0. When x < 0, ln |x| = ln(−x) and d d 1 1 ln |x| = ln(−x) = (−1) = . dx dx −x x Thus whether x is positive or negative, the derivative is the same. What about the functions ax and loga x? We know that the derivative of ax is some constant times ax itself, but what constant? Remember that “the logarithm is the exponent” and you will see that a = eln a . Then ax = (eln a )x = ex ln a , and we can compute the derivative using the chain rule: d ln a x d x ln a d x a = (e ) = e = (ln a)ex ln a = (ln a)ax . dx dx dx The constant is simply ln a. Likewise we can compute the derivative of the logarithm function loga x. Since x = eln x , 78 Chapter 4 Transcendental Functions we can take the logarithm base a of both sides to get loga (x) = loga (eln x ) = ln x loga e. Then d 1 loga x = loga e. dx x Finally, since a = eln a loga (a) = loga (eln a ) = ln a loga e 1 = ln a loga e 1 = loga e, ln a we can replace loga e to get d 1 loga x = . dx x ln a You may if you wish memorize the formulas d x a = (ln a)ax dx and d 1 loga x = . dx x ln a Because the “trick” a = eln a is often useful, and sometimes essential, it may be better to remember the trick, not the formula. EXAMPLE 4.5 Compute the derivative of f (x) = 2x . d x d ln 2 x 2 = (e ) dx dx d x ln 2 = e dx µ ¶ d = x ln 2 ex ln 2 dx = (ln 2)ex ln 2 4.7 EXAMPLE 4.6 Derivatives of the exponential and logarithmic functions 2 79 2 Compute the derivative of f (x) = 2x = 2(x ) . d x2 ln 2 d x2 2 = e dx dx µ ¶ 2 d 2 = x ln 2 ex ln 2 dx = (2 ln 2)xex 2 ln 2 2 = (2 ln 2)x2x EXAMPLE 4.7 Compute the derivative of f (x) = xx . At first this appears to be a new kind of function: it is not a constant power of x, and it does not seem to be an exponential function, since the base is not constant. But in fact it is no harder than the previous example. d x d x ln x x = e dx dx µ ¶ d = x ln x ex ln x dx 1 + ln x)xx x = (1 + ln x)xx = (x EXAMPLE 4.8 Recall that we have not justified the power rule except when the exponent is a positive or negative integer. We can use the exponential function to take care of other exponents. d r d r ln x x = e dx dx µ ¶ d = r ln x er ln x dx 1 = (r )xr x = rxr−1 80 Chapter 4 Transcendental Functions Exercises In 1–19, find the derivatives of the functions. 2 1. 3x ⇒ sin x 2. ⇒ ex 3. (ex )2 ⇒ 4. sin(ex ) ⇒ 11. ln(x3 + 3x) ⇒ 12. ln(cos(x)) ⇒ p 13. ln(x2 )/x ⇒ 14. ln(sec(x) + tan(x)) ⇒ 5. esin x ⇒ 15. xsin(x) ⇒ 6. xsin x ⇒ 16. x ln x 7. x3 ex ⇒ 17. ln(ln(3x)) 8. x + 2x ⇒ 18. 1 + ln(3x2 ) 1 + ln(4x) 19. x8 (x − 23)1/2 27x6 (4x − 6)8 2 9. (1/3)x ⇒ 10. e4x /x ⇒ 20. Find the value of a so that the tangent line to y = ln(x) at x = a is a line through the origin. Sketch the resulting situation. ⇒ 21. If f (x) = ln(x3 + 2) compute f 0 (e1/3 ). 4.8 Limits revisited We have defined and used the concept of limit, primarily in our development of the derivative. Recall that limx→a f (x) = L is true if, in a precise sense, f (x) gets closer and closer to L as x gets closer and closer to a. While some limits are easy to see, others take some ingenuity; in particular, the limits that define derivatives are always difficult on their face, since in f (x + ∆x) − f (x) lim ∆x→0 ∆x both the numerator and denominator approach zero. Typically this difficulty can be resolved when f is a “nice” function and we are trying to compute a derivative. Occasionally such limits are interesting for other reasons, and the limit of a fraction in which both numerator and denominator approach zero can be difficult to analyse. Now that we have the derivative available, there is another technique that can sometimes be helpful in such circumstances. Before we introduce the technique, we will also expand our concept of limit. We will occasionally want to know what happens to some quantity when a variable gets very large or “goes to infinity”. 4.8 Limits revisited 81 EXAMPLE 4.9 What happens to the function cos(1/x) as x goes to infinity? It seems clear that as x gets larger and larger, 1/x gets closer and closer to zero, so cos(1/x) should be getting closer and closer to cos(0) = 1. As with ordinary limits, this concept of “limit at infinity” can be made precise. Roughly, we want limx→∞ f (x) = L to mean that we can make f (x) as close as we want to L by making x large enough. Compare this definition to the definition of limit in section 2.3. DEFINITION 4.10 Limit at infinity Suppose f is a function. We say that limx→∞ f (x) = L if for every ² > 0 there is an N > 0 so that whenever x > N , |f (x) − L| < ². We include this definition for completeness, but we will not explore it in detail. Suffice it to say that such limits behave in much the same way that ordinary limits do; in particular there is a direct analog of theorem 2.7. Now consider this limit: x2 − π 2 . lim x→π sin x As x approaches π, both the numerator and denominator approach zero, so it is not obvious what, if anything, the quotient approaches. We can often compute such limits by application of the following theorem. THEOREM 4.11 L’Hôpital’s Rule For “sufficiently nice” functions f (x) and g(x), f 0 (x) if lim f (x) = 0 = lim g(x) or lim f (x) = ±∞ = lim g(x), and if lim 0 exists, then x→a x→a x→a x→a x→a g (x) f 0 (x) f (x) = lim 0 . This remains true if “x → a” is replaced by “x → ∞”. lim x→a g(x) x→a g (x) This theorem is somewhat difficult to prove, in part because it incorportates so many different possibilities, so we will not prove it here. We also will not need to worry about the precise definition of “sufficiently nice”, as the functions we encounter will be suitable. EXAMPLE 4.12 Compute lim x→π x2 − π 2 in two ways. sin x First we use L’Hôpital’s Rule: Since the numerator and denominator both approach zero, x2 − π 2 2x lim = lim , x→π sin x x→π cos x 82 Chapter 4 Transcendental Functions provided the latter exists. But in fact this is an easy limit, since the denominator now approaches −1, so 2π x2 − π 2 lim = = −2π. x→π sin x −1 We don’t really need L’Hôpital’s Rule to do this limit. Rewrite it as lim (x + π) x→π and note that lim x→π x−π sin x x−π x−π x = lim = lim − x→π − sin(x − π) x→0 sin x sin x since x − π approaches zero as x approaches π. Now lim (x + π) x→π x−π x = lim (x + π) lim − = 2π(−1) = −2π x→π x→0 sin x sin x as before. EXAMPLE 4.13 2x2 − 3x + 7 in two ways. x→∞ x2 + 47x + 1 Compute lim As x goes to infinity both the numerator and denominator go to infinity, so we may apply L’Hôpital’s Rule: 2x2 − 3x + 7 4x − 3 = lim . 2 x→∞ x + 47x + 1 x→∞ 2x + 47 lim In the second quotient, it is still the case that the numerator and denominator both go to infinity, so we are allowed to use L’Hôpital’s Rule again: 4x − 3 4 = lim = 2. x→∞ 2x + 47 x→∞ 2 lim So the original limit is 2 as well. Again, we don’t really need L’Hôpital’s Rule, and in fact a more elementary approach is easier—we divide the numerator and denominator by x2 : 2x2 − 3x + 7 2x2 − 3x + 7 lim = lim 2 x→∞ x2 + 47x + 1 x→∞ x + 47x + 1 1 x2 1 x2 2 − x3 + x72 = lim . x→∞ 1 + 47 + 12 x x Now as x approaches infinity, all the quotients with some power of x in the denominator approach zero, leaving 2 in the numerator and 1 in the denominator, so the limit again is 2. 4.8 EXAMPLE 4.14 Limits revisited 83 sec x − 1 . x→0 sin x Compute lim Both the numerator and denominator approach zero, so applying L’Hôpital’s Rule: sec x − 1 sec x tan x 1·0 = lim = = 0. x→0 x→0 sin x cos x 1 lim EXAMPLE 4.15 Compute lim+ x ln x. x→0 This doesn’t appear to be suitable for L’Hôpital’s Rule, but it also is not “obvious”. As x approaches zero, ln x goes to −∞, so the product looks like (something very small) · (something very large and negative). But this could be anything: it depends on how small and how large. For example, consider (x2 )(1/x), (x)(1/x), and (x)(1/x2 ). As x approaches zero, each of these is (something very small) · (something very large), yet the limits are respectively zero, 1, and ∞. We can in fact turn this into a L’Hôpital’s Rule problem: x ln x = ln x ln x = −1 . 1/x x Now as x approaches zero, both the numerator and denominator approach infinity (one −∞ and one +∞, but only the size is important). Using L’Hôpital’s Rule: lim x→0+ ln x 1/x 1 = lim = lim (−x2 ) = lim −x = 0. x−1 x→0+ −x−2 x→0+ x x→0+ One way to interpret this is that since lim x ln x = 0, the x approaches zero much faster x→0+ than the ln x approaches −∞. 84 Chapter 4 Transcendental Functions Exercises Compute the limits. cos x − 1 1. lim ⇒ x→0 sin x ex 2. lim 3 ⇒ x→∞ x p p 3. lim x2 + x − x2 − x ⇒ x→∞ 7. The function f (x) = √ x x2 ln x ⇒ x ln x 5. lim √ ⇒ x→∞ x 4. lim x→∞ ex + e−x x→∞ ex − e−x has two horizontal asymptotes. Find them and give a rough 6. lim +1 sketch of f with its horizontal asymptotes. ⇒ 4.9 Implicit Differentiation As we have seen, there is a close relationship between the derivatives of ex and ln x because these functions are inverses. Rather than relying on pictures for our understanding, we would like to be able to exploit this relationship computationally. In fact this technique can help us find derivatives in many situations, not just when we seek the derivative of an inverse function. We will begin by illustrating the technique to find what we already know, the derivative of ln x. Let’s write y = ln x and then x = eln x = ey , or more simply x = ey . We say that this equation defines the function y = ln x implicitly because while it is not an explicit expression y = . . ., it is true that if x = ey then y is in fact the natural logarithm function. Now, for the time being, pretend that all we know of y is that x = ey ; what can we say about derivatives? We can take the derivative of both sides of the equation: d d y x= e . dx dx Then using the chain rule on the right hand side: µ 1= ¶ d y ey = y 0 ey . dx Then we can solve for y 0 : y0 = 1 1 = . y e x There is one little difficulty here. To use the chain rule to compute d/dx(ey ) = y 0 ey we need to know that the function y has a derivative. All we have shown is that if it has a derivative then that derivative must be 1/x. When using this method we will always have 4.9 Implicit Differentiation 85 to assume that the desired derivative exists, but fortunately this is a safe assumption for most such problems. The example y = ln x involved an inverse function defined implicitly, but other functions can be defined implicitly, and sometimes a single equation can be used to implicitly define more than one function. Here’s a familiar example. The equation r2 = x2 + y 2 describes a circle of radius r. The circle is not a function y = f (x) because for some values of x there are two corresponding values of y. If we want to work with a function, we can break the circle into two pieces, the upper and lower semicircles, each of which is a function. Let’s call these y = U (x) and y = L(x); in fact this is a fairly simple example, and it’s √ √ possible to give explicit expressions for these: U (x) = r2 − x2 and L(x) = − r2 − x2 . But it’s somewhat easier, and quite useful, to view both functions as given implicitly by r2 = x2 + y 2 : both r2 = x2 + U (x)2 and r2 = x2 + L(x)2 are true, and we can think of r2 = x2 + y 2 as defining both U (x) and L(x). Now we can take the derivative of both sides as before, remembering that y is not simply a variable but a function—in this case, y is either U (x) or L(x) but we’re not yet specifying which one. When we take the derivative we just have to remember to apply the chain rule where y appears. d 2 d 2 r = (x + y 2 ) dx dx 0 = 2x + 2yy 0 y0 = −2x x =− 2y y Now we have an expression for y 0 , but it contains y as well as x. This means that if we want to compute y 0 for some particular value of x we’ll have to know or compute y at that value of x as well. It is at this point that we will need to know whether y is U (x) or L(x). Occasionally it will turn out that we can avoid explicit use of U (x) or L(x) by the nature of the problem √ EXAMPLE 4.16 Find the slope of the circle 4 = x2 + y 2 at the point (1, − 3). Since we know both the x and y coordinates of the point of interest, we do not need to explicitly recognize that this point is on L(x), and we do not need to use L(x) to compute y—but we could. Using the calculation of y 0 from above, y0 = − x 1 1 =− √ = √ . y − 3 3 It is instructive to compare this approach to others. 86 Chapter 4 Transcendental Functions √ We might have recognized at the start that (1, − 3) is on the function y = L(x) = √ − 4 − x2 . We could then take the derivative of L(x), using the power rule and the chain rule, to get 1 x L0 (x) = − (4 − x2 )−1/2 (−2x) = √ . 2 4 − x2 √ Then we could compute L0 (1) = 1/ 3 by substituting x = 1. Alternately, we could realize that the point is on L(x), but use the fact that y 0 = −x/y. Since the point is on L(x) we can replace y by L(x) to get y0 = − x x = −√ , L(x) 4 − x2 without computing the derivative of L(x) explicitly. Then we substitute x = 1 and get the same answer as before. In the case of the circle it is possible to find the functions U (x) and L(x) explicitly, but there are potential advantages to using implicit differentiation anyway. In some cases it is more difficult or impossible to find an explicit formula for y and implicit differentiation is the only way to find the derivative. EXAMPLE 4.17 Find the derivative of any function defined implicitly by yx2 +ey = x. We treat y as an unspecified function and use the chain rule: d d (yx2 + ey ) = x dx dx (y · 2x + y 0 · x2 ) + y 0 ey = 1 y 0 x2 + y 0 ey = 1 − 2xy y 0 (x2 + ey ) = 1 − 2xy y0 = 1 − 2xy x2 + ey You might think that the step in which we solve for y 0 could sometimes be difficult— after all, we’re using implicit differentiation here because we can’t solve the equation yx2 + ey = x for y, so maybe after taking the derivative we get something that is hard to solve for y 0 . In fact, this never happens. All occurrences y 0 come from applying the chain rule, and whenever the chain rule is used it deposits a single y 0 multiplied by some other expression. So it will always be possible to group the terms containing y 0 together and 4.9 Implicit Differentiation 87 factor out the y 0 , just as in the previous example. If you ever get anything more difficult you have made a mistake and should fix it before trying to continue. It is sometimes the case that a situation leads naturally to an equation that defines a function implicitly. EXAMPLE 4.18 Consider all the points (x, y) that have the property that the distance from (x, y) to (x1 , y1 ) plus the distance from (x, y) to (x2 , y2 ) is 2a (a is some constant). These points form an ellipse, which like a circle is not a function but can viewed as two functions pasted together. Because we know how to write down the distance between two points, we can write down an implicit equation for the ellipse: p p (x − x1 )2 + (y − y1 )2 + (x − x2 )2 + (y − y2 )2 = 2a. Then we can use implicit differentiation to find the slope of the ellipse at any point. EXAMPLE 4.19 We have already justified the power rule by using the exponential function, but we could also do it for rational exponents by using implicit differentiation. Suppose that y = xm/n , where m and n are positive integers. We can write this implicitly as y n = xm , then because we justified the power rule for integers, we can take the derivative of each side: ny n−1 y 0 = mxm−1 m xm−1 y = n y n−1 0 m xm−1 n (xm/n )n−1 m y 0 = xm−1−(m/n)(n−1) n m y 0 = xm−1−m+(m/n) n m (m/n)−1 0 y = x n y0 = 88 Chapter 4 Transcendental Functions Exercises In exercises 1–8, find a formula for the derivative y 0 at the point (x, y): 1. y 2 = 1 + x2 ⇒ 2. x2 + xy + y 2 = 7 ⇒ 3. x3 + xy 2 = y 3 + yx2 ⇒ 4. 4 cos x sin y = 1 ⇒ √ √ 5. x + y = 9 ⇒ 6. tan(x/y) = x + y ⇒ 7. sin(x + y) = xy ⇒ 1 1 8. + =7⇒ x y 9. A hyperbola passing through (8, 6) consists of all points whose distance from the origin is a constant more than its distance from the point (5,2). Find the slope of the tangent line to the hyperbola at (8, 6). ⇒ 10. Compute y 0 for the ellipse of example 4.18. 11. The graph of the equation x2 − xy + y 2 = 9 is an ellipse. Find the lines tangent to this curve at the two points where it intersects the x-axis. Show that these lines are parallel. ⇒ 12. Repeat the previous problem for the points at which the ellipse intersects the y-axis. ⇒ 13. Find the points on the ellipse from the previous two problems where the slope is horizontal and where it is vertical. ⇒ √ 14. Find an equation for the tangent line to x4 = y 2 +x2 at (2, 12). (This curve is the kampyle of Eudoxus.) ⇒ 15. Find an equation for the tangent line to x2/3 + y 2/3 = a2/3 at a point (x1 , y1 ) on the curve, with x1 6= 0 and y1 6= 0. (This curve is an astroid.) ⇒ 16. Find an equation for the tangent line to (x2 + y 2 )2 = x2 − y 2 at a point (x1 , y1 ) on the curve, with x1 6= 0, −1, 1. (This curve is a lemniscate.) ⇒ Definition. Two curves are orthogonal if at each point of intersection, the angle between their tangent lines is π/2 . Two families of curves, A and B, are orthogonal trajectories of each other if given any curve C in A and any curve D in B the curves C and D are orthogonal. For example, the family of horizontal lines in the plane is orthogonal to the family of vertical lines in the plane. 17. Show that x2 − y 2 = 5 is orthogonal to 4x2 + 9y 2 = 72. (Hint: You need to find the intersection points of the two curves and then show that the product of the derivatives at each intersection point is −1.) 18. Show that x2 + y 2 = r2 is orthogonal to y = mx. Conclude that the family of circles centered at the origin is an orthogonal trajectory of the family of lines that pass through the origin. Note that there is a technical issue when m = 0. The circles fail to be differentiable when they cross the x-axis. However, the circles are orthogonal to the x-axis. Explain why. 4.10 Inverse Trigonometric Functions 89 19. For k, c 6= 0 show that y 2 − x2 = k is orthogonal to yx = c. In the case where k and c are both zero, the curves intersect at the origin. Are the curves y 2 − x2 = 0 and yx = 0 orthogonal to each other? 20. Suppose that m 6= 0. Show that the family of curves {y = mx + b | b ∈ R} is orthogonal to the family of curves {y = −(x/m) + c | c ∈ R}. 4.10 Inverse Trigonometric Functions The trigonometric functions frequently arise in problems, and often it is necessary to invert the functions, for example, to find an angle with a specified sine. Of course, there are many angles with the same sine, so the sine function doesn’t actually have an inverse that reliably “undoes” the sine function. If you know that sin x = 0.5, you can’t reverse this to discover x, that is, you can’t solve for x, as there are infinitely many angles with sine 0.5. Nevertheless, it is useful to have something like an inverse to the sine, however imperfect. The usual approach is to pick out some collection of angles that produce all possible values of the sine exactly once. If we “discard” all other angles, the resulting function does have a proper inverse. The sine takes on all values between −1 and 1 exactly once on the interval [−π/2, π/2]. If we truncate the sine, keeping only the interval [−π/2, π/2], as shown in figure 4.5, then this truncated sine has an inverse function. We call this the inverse sine or the arcsine, and write y = arcsin(x). 1 .................................. ................................... ...... ....... ....... ...... ...... ...... ...... ...... ..... ..... ..... . . . . . . . . . . . . ..... ..... . .. . . . . . . . . ..... . . ..... . . . ... . . ..... . . . . . ..... . . . . ..... . . . . . . . ..... .. .. ..... . .. . . ..... . ..... .. ..... . . ..... . . ..... .. ..... . ..... . . ..... . ..... . .. ..... . . . . . . . . . . −2π −3π/2 −π .......... −π/2 ......... π/2 π ........... 3π/2 .......... 2π ....... ....... . .. .......... .......... ....... ...... ..................... ..................... −1 π/2 1 .............. ....... ...... . . . . . ..... ..... ..... .... . . . . ..... ..... ..... ..... . . . . −π/2 ........ π/2 ... ....... .............. −1 Figure 4.5 .. ... .. .. . . ... .. ... ..... . . . .... ..... .... ..... . . . . . ..... ..... −1 .......... 1 . ... ... . . ... ... .. −π/2 The sine, the truncated sine, the inverse sine. 90 Chapter 4 Transcendental Functions Recall that a function and its inverse undo each other in either order, for example, √ 3 ( x)3 = x and x3 = x. This does not work with the sine and the “inverse sine” because the inverse sine is the inverse of the truncated sine function, not the real sine function. It is true that sin(arcsin(x)) = x, that is, the sine undoes the arcsine. It is not true that the arcsine undoes the sine, for example, sin(5π/6) = 1/2 and arcsin(1/2) = π/6, so doing first the sine then the arcsine does not get us back where we started. This is because 5π/6 is not in the domain of the truncated sine. If we start with an angle between −π/2 and π/2 then the arcsine does reverse the sine: sin(π/6) = 1/2 and arcsin(1/2) = π/6. What is the derivative of the arcsine? Since this is an inverse function, we can discover the derivative by using implicit differentiation. Suppose y = arcsin(x). Then √ 3 sin(y) = sin(arcsin(x)) = x. Now taking the derivative of both sides, we get y 0 cos y = 1 y0 = 1 cos y As we expect when using implicit differentiation, y appears on the right hand side here. We would certainly prefer to have y 0 written in terms of x, and as in the case of ln x we can actually do that here. Since sin2 y + cos2 y = 1, cos2 y = 1 − sin2 y = 1 − x2 . So √ cos y = ± 1 − x2 , but which is it—plus or minus? It could in general be either, but this isn’t “in general”: since y = arcsin(x) we know that −π/2 ≤ y ≤ π/2, and the cosine of √ an angle in this interval is always positive. Thus cos y = 1 − x2 and d 1 arcsin(x) = √ . dx 1 − x2 Note that this agrees with figure 4.5: the graph of the arcsine has positive slope everywhere. We can do something similar for the cosine. As with the sine, we must first truncate the cosine so that it can be inverted, as shown in figure 4.6. Then we use implicit differentiation to find that d −1 arccos(x) = √ . dx 1 − x2 Note that the truncated cosine uses a different interval than the truncated sine, so that if y = arccos(x) we know that 0 ≤ y ≤ π. The computation of the derivative of the arccosine is left as an exercise. 4.10 1 −1 ............... ....... ...... ...... ..... ..... ..... ..... ..... ..... ..... ..... .. π/2 .............. π ....... ......... ........ Inverse Trigonometric Functions .. π ... ... ... ... ... ... .... ..... ..... ..... .... π/2 ............ ..... ..... ..... .... ... ... ... ... .. ... .. −1 Figure 4.6 . . . .. .. .. .. .. .. ... ... .. .. .. . . .. .. .. ... ... ... ... ... .... .... .... . . . .. .. .. ... ... ... ..... ..... ..... .. .. .. .. .. .. .. .. .. . . . . . . .. ... ... ... ... ... ...... ..... ..... ....... ....... ........ . . . . . . . . . . . . . . . . . . . . . .. ... ... ...... ...... ...... .... .... .... ... ... ... −π/2 π/2 .... .... .... . . . ... ... ... ... ... ... .... .... ..... . . . .. .. .. .. .. .. ..... .... ..... . . . ... ... ... .. .. .. ... ... ... . .. .. .. .. .. .. .. .. Figure 4.7 91 1 The truncated cosine, the inverse cosine. . .. .. ... .. .. .. .... .. .. .. ..... ... .. .. . .. ... .. ..... . . . . . . ......... ....... ..... ... . . −π/2 π/2 ... .. ... .... . ... .. ... . ... ... ... . .. .. ... π/2 ............ ................................................... ............ ...... ..... . . . ... ... ... . . . .. ...... ............. .............................................................. −π/2 The tangent, the truncated tangent, the inverse tangent. Finally we look at the tangent; the other trigonometric functions also have “partial inverses” but the sine, cosine and tangent are enough for most purposes. The tangent, truncated tangent and inverse tangent are shown in figure 4.7; the derivative of the arctangent is left as an exercise. Exercises 1 1. Show that the derivative of arccos x is − √ . 1 − x2 1 2. Show that the derivative of arctan x is . 1 + x2 3. Find the derivative of arccot x, the inverse cotangent. ⇒ 4. Find the derivative of arcsin(x2 ). ⇒ 5. Find the derivative of arctan(ex ). ⇒ 6. The inverse of cot is usually defined so that the range of arccot is (0, π). Sketch the graph of y = arccot x. In the process you will make it clear what the domain of arccot is. 92 Chapter 4 Transcendental Functions 7. Use the preceding exercise to show that arccot x + arctan x = π/2. 8. Find the derivative of arccos(sin x3 ) 9. Find the derivative of ln((arcsin x)2 ) 10. Find the derivative of arccos ex 11. Find the derivative of arcsin x + arccos x 12. Find the derivative of log5 (arctan(xx )) 5 Curve Sketching Whether we are interested in a function as a purely mathematical object or in connection with some application to the real world, it is often useful to know what the graph of the function looks like. We can obtain a good picture of the graph using certain crucial information provided by derivatives of the function and certain limits. 5.1 Maxima and Minima A local maximum point on a function is a point (x, y) on the graph of the function whose y coordinate is larger than all other y coordinates on the graph at points “close to” (x, y). More precisely, (x, f (x)) is a local maximum if there is an interval (a, b) with a < x < b and f (x) ≥ f (z) for every z in (a, b). Similarly, (x, y) is a local minimum point if it has locally the smallest y coordinate. Again being more precise: (x, f (x)) is a local minimum if there is an interval (a, b) with a < x < b and f (x) ≤ f (z) for every z in (a, b). A local extremum is either a local minimum or a local maximum. Local maximum and minimum points are quite distinctive on the graph of a function, and are therefore useful in understanding the shape of the graph. In many applied problems we want to find the largest or smallest value that a function achieves (for example, we might want to find the minimum cost at which some task can be performed) and so identifying maximum and minimum points will be useful for applied problems as well. Some examples of local maximum and minimum points are shown in figure 5.1. 93 94 Chapter 5 Curve Sketching . ... .. .... . ... ... . . ... ... ... ..................... . . . . . . ..... ... .. ..... ... ... ..... ... .. ..... ... ... ..... . . . . ..... .. ... ..... ... ...... .... ..................... ... ... . .. ... ... . . ... ... .. .... .. A • • B Figure 5.1 A • ..... ... ... ... .... ... ... . . ... ... ... ... ... . . ... . . . ... . . . ... .. ..... . . ... . . . . . . . . ... . ...... . . . . . ... ..... . . . . . . ... . ..... . . .. . ... . . ..... . . . ... . . . ..... .. . . ... . . . ..... .. . . ... . . . ..... . . ... . .. ... . . ... . . . . . . . .. . . ... ... A • • B Some local maximum points (A) and minimum points (B). If (x, f (x)) is a point where f (x) reaches a local maximum or minimum, and if the derivative of f exists at x, then the graph has a tangent line and the tangent line must be horizontal. This is important enough to state as a theorem: THEOREM 5.1 Fermat’s Theorem is differentiable at a, then f 0 (a) = 0. If f (x) has a local extremum at x = a and f Thus, the only points at which a function can have a local maximum or minimum are points at which the derivative is zero, as in the left hand graph in figure 5.1, or the derivative is undefined, as in the right hand graph. Any value of x for which f 0 (x) is zero or undefined is called a critical value for f . When looking for local maximum and minimum points, you are likely to make two sorts of mistakes: You may forget that a maximum or minimum can occur where the derivative does not exist, and so forget to check whether the derivative exists everywhere. You might also assume that any place that the derivative is zero is a local maximum or minimum point, but this is not true. A portion of the graph of f (x) = x3 is shown in figure 5.2. The derivative of f is f 0 (x) = 3x2 , and f 0 (0) = 0, but there is neither a maximum nor minimum at (0, 0). Since the derivative is zero or undefined at both local maximum and local minimum points, we need a way to determine which, if either, actually occurs. The most elementary approach, but one that is often tedious or difficult, is to test directly whether the y coordinates “near” the potential maximum or minimum are above or below the y coordinate at the point of interest. Of course, there are too many points “near” the point to test, but a little thought shows we need only test two provided we know that f is continuous (recall that this means that the graph of f has no jumps or gaps). Suppose, for example, that we have identified three points at which f 0 is zero or nonexistent: (x1 , y1 ), (x2 , y2 ), (x3 , y3 ), and x1 < x2 < x3 (see figure 5.3). Suppose that 5.1 Maxima and Minima 95 . ... ... ... . . . ... ... ... . . .. ... .. ... . . .. ... .... ..... . . . . . ...... ........ ..................................... ......... . . . . . .... ..... .... ... . . . ... ... ... . . . ... ... ... . . ... .. ... ... Figure 5.2 No maximum or minimum even though the derivative is zero. we compute the value of f (a) for x1 < a < x2 , and that f (a) < f (x2 ). What can we say about the graph between a and x2 ? Could there be a point (b, f (b)), a < b < x2 with f (b) > f (x2 )? No: if there were, the graph would go up from (a, f (a)) to (b, f (b)) then down to (x2 , f (x2 )) and somewhere in between would have a local maximum point. But at that local maximum point the derivative of f would be zero or nonexistent, yet we already know that the derivative is zero or nonexistent only at x1 , x2 , and x3 . The upshot is that one computation tells us that (x2 , f (x2 )) has the largest y coordinate of any point on the graph near x2 and to the left of x2 . We can perform the same test on the right. If we find that on both sides of x2 the values are smaller, then there must be a local maximum at (x2 , f (x2 )); if we find that on both sides of x2 the values are larger, then there must be a local minimum at (x2 , f (x2 )); if we find one of each, then there is neither a local maximum or minimum at x2 . • • • • x1 a Figure 5.3 b x2 x3 Testing for a maximum or minimum. It is not always easy to compute the value of a function at a particular point. The task is made easier by the availability of calculators and computers, but they have their own drawbacks—they do not always allow us to distinguish between values that are very 96 Chapter 5 Curve Sketching close together. Nevertheless, because this method is conceptually simple and sometimes easy to perform, you should always consider it. EXAMPLE 5.2 Find all local maximum and minimum points for the function f (x) = x3 − x. The derivative is f 0 (x) = 3x2 − 1. This is defined everywhere and is zero at √ √ √ √ x = ± 3/3. Looking first at x = 3/3, we see that f ( 3/3) = −2 3/9. Now we test √ two points on either side of x = 3/3, making sure that neither is farther away than √ √ the nearest critical value; since 3 < 3, 3/3 < 1 and we can use x = 0 and x = 1. √ √ Since f (0) = 0 > −2 3/9 and f (1) = 0 > −2 3/9, there must be a local minimum at √ √ √ √ x = 3/3. For x = − 3/3, we see that f ( 3/3) = 2 3/9. This time we can use x = 0 √ and x = −1, and we find that f (−1) = f (0) = 0 < 2 3/9, so there must be a local √ maximum at x = − 3/3. Of course this example is made very simple by our choice of points to test, namely x = −1, 0, 1. We could have used other values, say −5/4, 1/3, and 3/4, but this would have made the calculations considerably more tedious. EXAMPLE 5.3 Find all local maximum and minimum points for f (x) = sin x + cos x. The derivative is f 0 (x) = cos x − sin x. This is always defined and is zero whenever cos x = sin x. Recalling that the cos x and sin x are the x and y coordinates of points on a unit circle, we see that cos x = sin x when x is π/4, π/4 ± π, π/4 ± 2π, π/4 ± 3π, etc. Since both sine and cosine have a period of 2π, we need only determine the status of x = π/4 and x = 5π/4. We can use 0 and π/2 to test the critical value x = π/4. We find that √ √ f (π/4) = 2, f (0) = 1 < 2 and f (π/2) = 1, so there is a local maximum when x = π/4 and also when x = π/4 ± 2π, π/4 ± 4π, etc. We use π and 2π to test the critical value x = 5π/4. The relevant values are f (5π/4) = √ √ √ − 2, f (π) = −1 > − 2, f (2π) = 1 > − 2, so there is a local minimum at x = 5π/4, 5π/4 ± 2π, 5π/4 ± 4π, etc. 5.2 The first derivative test 97 Exercises In problems 1–12, find all local maximum and minimum points (x, y) by the method of this section. 1. y = x2 − x ⇒ 3 2. y = 2 + 3x − x ⇒ 3 2 3. y = x − 9x + 24x ⇒ 4. y = x4 − 2x2 + 3 ⇒ 5. y = 3x4 − 4x3 ⇒ 6. y = (x2 − 1)/x ⇒ 2 2 7. y = 3x − (1/x ) ⇒ 8. y = cos(2x) − x  x−1 9. f (x) = x2 8 5 11. f (x) = x2 − 98x + 4 ⇒  −2 x=0 12. f (x) = ⇒ 1/x2 x = 6 0 13. Recall that for any real number x there is a unique integer n such that n ≤ x < n + 1, and the greatest integer function is given by bxc = n, as shown in figure 3.1. Where are the critical values of the greatest integer function? Which are local maxima and which are local minima? 14. Explain why the function f (x) = 1/x has no local maxima or minima. 15. How many critical points can a quadratic polynomial function have? ⇒ 16. Show that a cubic polynomial can have at most two critical points. Give examples to show that a cubic polynomial can have zero, one, or two critical points. 17. Explore the family of functions f (x) = x3 + cx + 1 where c is a constant. How many and what types of local extremes are there? Your answer should depend on the value of c, that is, different values of c will give different answers. 18. We generalize the preceding two questions. Let n be a positive integer and let f be a polynomial of degree n. How many critical points can f have? (Hint: Recall the Fundamental Theorem of Algebra, which says that a polynomial of degree n has at most n roots.) 5.2 The first derivative test The method of the previous section for deciding whether there is a local maximum or minimum at a critical value is not always convenient. We can instead use information about the derivative f 0 (x) to decide; since we have already had to compute the derivative to find the critical values, there is often relatively little extra work involved in this method. How can the derivative tell us whether there is a maximum, minimum, or neither at a point? Suppose that f 0 (a) = 0. If there is a local maximum when x = a, the function must be lower near x = a than it is right at x = a. If the derivative exists near x = a, this means f 0 (x) > 0 when x is near a and x < a, because the function must “slope up” just to the left of a. Similarly, f 0 (x) < 0 when x is near a and x > a, because f slopes down from the local maximum as we move to the right. Using the same reasoning, if there is a local minimum at x = a, the derivative of f must be negative just to the left of a and 98 Chapter 5 Curve Sketching positive just to the right. If the derivative exists near a but does not change from positive to negative or negative to positive, that is, it is postive on both sides or negative on both sides, then there is neither a maximum nor minimum when x = a. See the first graph in figure 5.1 and the graph in figure 5.2 for examples. EXAMPLE 5.4 Find all local maximum and minimum points for f (x) = sin x + cos x using the first derivative test. The derivative is f 0 (x) = cos x − sin x and from example 5.3 the critical values we need to consider are π/4 and 5π/4. The graphs of sin x and cos x are shown in figure 5.4. Just to the left of π/4 the cosine is larger than the sine, so f 0 (x) is positive; just to the right the cosine is smaller than the sine, so f 0 (x) is negative. This means there is a local maximum at π/4. Just to the left of 5π/4 the cosine is smaller than the sine, and to the right the cosine is larger than the sine. This means that the derivative f 0 (x) is negative to the left and positive to the right, so f has a local minimum at 5π/4. .................. ......... ............... .............. ..................... ........ ......... ....... .............. ....... ....... .......... ...... ...... . . . . . . . . . . . . . . . . . ...... .... ...... .......... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... . . . . . . . . . . . . . ..... ..... .. ..... ..... ..... ..... .... ..... ..... ..... ..... ..... ..... ..... ..... .... ..... . ..... . . . ..... ..... .. ..... ..... ..... ..... ..... . . . . . . . . . . . ..... ..... . .... π ..... ..... 5π ......... ..... ..... ..... ..... ..... ..... 4 ......... 4 ...... ..... ...... ..... . . . . . ...... .. . ....... ...... ......... ........ ...... ...... .............. ....... ........... ........ ............ ............................ .......................... Figure 5.4 The sine and cosine. Exercises In 1–13, find all critical points and identify them as local maximum points, local minimum points, or neither. 1. y = x2 − x ⇒ 8. y = cos(2x) − x ⇒ 3 2. y = 2 + 3x − x ⇒ 9. f (x) = (5 − x)/(x + 2) ⇒ 3. y = x − 9x + 24x ⇒ 10. f (x) = |x2 − 121| ⇒ 4. y = x4 − 2x2 + 3 ⇒ 6. y = (x2 − 1)/x ⇒ 11. f (x) = x3 /(x + 1) ⇒  2 12. f (x) = x sin(1/x) x 6= 0 0 x=0 7. y = 3x2 − (1/x2 ) ⇒ 13. f (x) = sin2 x ⇒ 3 2 5. y = 3x4 − 4x3 ⇒ 14. Find the maxima and minima of f (x) = sec x. ⇒ 5.3 The second derivative test 99 15. Let f (θ) = cos2 (θ) − 2 sin(θ). Find the intervals where f is increasing and the intervals where f is decreasing inside of [0, 2π]. Use this information to classify the critical points of f as either local maximums, local minimums, or neither. ⇒ √ 16. Let r > 0. Find the local maxima and minima of the function f (x) = r2 − x2 on its domain (−r, r). Sketch the curve and explain why the result is unsurprising. 17. Let f (x) = ax2 + bx + c with a 6= 0. Show that f has exactly one critical point using the first derivative test. Give conditions on a and b which guarantee that the critical point will be a maximum. It is possible to see this without using calculus at all; explain. 5.3 The second derivative test The basis of the first derivative test is that if the derivative changes from positive to negative at a point at which the derivative is zero then there is a local maximum at the point, and similarly for a local minimum. If f 0 changes from positive to negative it is decreasing; this means that the derivative of f 0 , f 00 , might be negative, and if in fact f 00 is negative then f 0 is definitely decreasing, so there is a local maximum at the point in question. Note well that f 0 might change from positive to negative while f 00 is zero, in which case f 00 gives us no information about the critical value. Similarly, if f 0 changes from negative to positive there is a local minimum at the point, and f 0 is increasing. If f 00 > 0 at the point, this tells us that f 0 is increasing, and so there is a local minimum. EXAMPLE 5.5 Consider again f (x) = sin x + cos x, with f 0 (x) = cos x − sin x and √ √ √ f 00 (x) = − sin x − cos x. Since f 00 (π/4) = − 2/2 − 2/2 = − 2 < 0, we know there is a √ √ √ local maximum at π/4. Since f 00 (5π/4) = − − 2/2 − − 2/2 = 2 > 0, there is a local minimum at 5π/4. When it works, the second derivative test is often the easiest way to identify local maximum and minimum points. Sometimes the test fails, and sometimes the second derivative is quite difficult to evaluate; in such cases we must fall back on one of the previous tests. EXAMPLE 5.6 Let f (x) = x4 . The derivatives are f 0 (x) = 4x3 and f 00 (x) = 12x2 . Zero is the only critical value, but f 00 (0) = 0, so the second derivative test tells us nothing. However, f (x) is positive everywhere except at zero, so clearly f (x) has a local minimum at zero. On the other hand, f (x) = −x4 also has zero as its only critical value, and the second derivative is again zero, but −x4 has a local maximum at zero. 100 Chapter 5 Curve Sketching Exercises Find all local maximum and minimum points by the second derivative test. p 1. y = x2 − x ⇒ 10. y = (x + 1)/ 5x2 + 35 ⇒ 2. y = 2 + 3x − x3 ⇒ 11. y = x5 − x ⇒ 3. y = x3 − 9x2 + 24x ⇒ 12. y = 6x + sin 3x ⇒ 4 2 4. y = x − 2x + 3 ⇒ 13. y = x + 1/x ⇒ 5. y = 3x − 4x ⇒ 14. y = x2 + 1/x ⇒ 6. y = (x2 − 1)/x ⇒ 15. y = (x + 5)1/4 ⇒ 7. y = 3x2 − (1/x2 ) ⇒ 16. y = tan2 x ⇒ 8. y = cos(2x) − x ⇒ √ 9. y = 4x + 1 − x ⇒ 17. y = cos2 x − sin2 x ⇒ 4 3 18. y = sin3 x ⇒ 5.4 Concavity and inflection points We know that the sign of the derivative tells whether a function is increasing or decreasing. For example, any time that f 0 (x) > 0, that means that f (x) is increasing. The sign of the second derivative f 00 (x) tells us whether f 0 is increasing or decreasing; we have seen that if f 0 is zero and increasing at a point then there is a local minimum at the point, and if f 0 is zero and decreasing at a point then there is a local maximum at the point. Thus, we extracted information about f from information about f 00 . We can get information from the sign of f 00 even when f 0 is not zero. Suppose that f 00 (a) > 0. This means that near x = a f 0 is increasing. If f 0 (a) > 0, this means that f slopes up and is getting steeper; if f 0 (a) < 0, this means that f slopes down and is getting less steep. The two situations are shown in figure 5.5. A curve that is shaped like this is called concave up. Figure 5.5 ... ... ... . . .... .... .... ..... . . . . ...... ....... ............ ... ... ... ... ... ... .... .... .... ..... ..... ...... ............ .. a a f 00 (a) > 0: f 0 (a) positive and increasing, f 0 (a) negative and increasing. Now suppose that f 00 (a) < 0. This means that near x = a f 0 is decreasing. If f 0 (a) > 0, this means that f slopes up and is getting less steep; if f 0 (a) < 0, this means that f slopes 5.4 Figure 5.6 Concavity and inflection points .......... ....... ..... ..... . . . . ..... .... .... ... . . ... ... ....... ...... ...... ..... ..... ..... ..... ..... .... .... ... ... ... . a a 101 f 00 (a) < 0: f 0 (a) positive and decreasing, f 0 (a) negative and decreasing. down and is getting steeper. The two situations are shown in figure 5.6. A curve that is shaped like this is called concave down. If we are trying to understand the shape of the graph of a function, knowing where it is concave up and concave down helps us to get a more accurate picture. Of particular interest are points at which the concavity changes from up to down or down to up; such points are called inflection points. If the concavity changes from up to down at x = a, f 00 changes from positive to the left of a to negative to the right of a, and usually f 00 (a) = 0. We can identify such points by first finding where f 00 (x) is zero and then checking to see whether f 00 (x) does in fact go from positive to negative or negative to positive at these points. Note that it is possible that f 00 (a) = 0 but the concavity is the same on both sides; f (x) = x4 is an example. EXAMPLE 5.7 Describe the concavity of f (x) = x3 − x. f 0 (x) = 3x2 − 1, f 00 (x) = 6x. Since f 00 (0) = 0, there is potentially an inflection point at zero. Since f 00 (x) > 0 when x > 0 and f 00 (x) < 0 when x < 0 the concavity does change from down to up at zero, and the curve is concave down for all x < 0 and concave up for all x > 0. Note that we need to compute and analyze the second derivative to understand concavity, so we may as well try to use the second derivative test for maxima and minima. If for some reason this fails we can then try one of the other tests. Exercises Describe the concavity of the functions in 1–18. 1. y = x2 − x ⇒ 3. y = x3 − 9x2 + 24x ⇒ 8. y = sin x + cos x ⇒ √ 9. y = 4x + 1 − x ⇒ p 10. y = (x + 1)/ 5x2 + 35 ⇒ 4. y = x4 − 2x2 + 3 ⇒ 11. y = x5 − x ⇒ 5. y = 3x4 − 4x3 ⇒ 12. y = 6x + sin 3x ⇒ 3 2. y = 2 + 3x − x ⇒ 2 6. y = (x − 1)/x ⇒ 2 2 7. y = 3x − (1/x ) ⇒ 13. y = x + 1/x ⇒ 14. y = x2 + 1/x ⇒ 102 Chapter 5 Curve Sketching 15. y = (x + 5)1/4 ⇒ 17. y = cos2 x − sin2 x ⇒ 16. y = tan2 x ⇒ 18. y = sin3 x ⇒ 19. Identify the intervals on which the graph of the function f (x) = x4 − 4x3 + 10 is of one of these four shapes: concave up and increasing; concave up and decreasing; concave down and increasing; concave down and decreasing. ⇒ 20. Describe the concavity of y = x3 + bx2 + cx + d. You will need to consider different cases, depending on the values of the coefficients. 21. Let n be an integer greater than or equal to two, and suppose f is a polynomial of degree n. How many inflection points can f have? Hint: Use the second derivative test and the fundamental theorem of algebra. 5.5 Asymptotes and Other Things to Look For A vertical asymptote is a place where the function becomes infinite, typically because the formula for the function has a denominator that becomes zero. For example, the reciprocal function f (x) = 1/x has a vertical asymptote at x = 0, and the function tan x has a vertical asymptote at x = π/2 (and also at x = −π/2, x = 3π/2, etc.). Whenever the formula for a function contains a denominator it is worth looking for a vertical asymptote by checking to see if the denominator can ever be zero, and then checking the limit at such points. Note that there is not always a vertical asymptote where the derivative is zero: f (x) = (sin x)/x has a zero denominator at x = 0, but since limx→0 (sin x)/x = 1 there is no asymptote there. A horizontal asymptote is a horizontal line to which f (x) gets closer and closer as x approaches ∞ (or as x approaches −∞). For example, the reciprocal function has the x-axis for a horizontal asymptote. Horizontal asymptotes can be identified by computing the limits limx→∞ f (x) and limx→−∞ f (x). Since limx→∞ 1/x = limx→−∞ 1/x = 0, the line y = 0 (that is, the x-axis) is a horizontal asymptote in both directions. Some functions have asymptotes that are neither horizontal nor vertical, but some other line. Such asymptotes are somewhat more difficult to identify and we will ignore them. If the domain of the function does not extend out to infinity, we should also ask what happens as x approaches the boundary of the domain. For example, the function √ y = f (x) = 1/ r2 − x2 has domain −r < x < r, and y becomes infinite as x approaches either r or −r. In this case we might also identify this behavior because when x = ±r the denominator of the function is zero. 5.5 Asymptotes and Other Things to Look For 103 If there are any points where the derivative fails to exist (a cusp or corner), then we should take special note of what the function does at such a point. Finally, it is worthwhile to notice any symmetry. A function f (x) that has the same value for −x as for x, i.e., f (−x) = f (x), is called an “even function.” Its graph is symmetric with respect to the y-axis. Some examples of even functions are: xn when n is an even number, cos x, and sin2 x. On the other hand, a function that satisfies the property f (−x) = −f (x) is called an “odd function.” Its graph is symmetric with respect to the origin. Some examples of odd functions are: xn when n is an odd number, sin x, and tan x. Of course, most functions are neither even nor odd, and do not have any particular symmetry. Exercises Sketch the curves. Identify clearly any interesting features, including local maximum and minimum points, inflection points, asymptotes, and intercepts. 1. y = x5 − 5x4 + 5x3 3 2 14. y = x + 1/x 2. y = x − 3x − 9x + 5 15. y = x2 + 1/x 3. y = (x − 1)2 (x + 3)2/3 16. y = (x + 5)1/4 4. x2 + x2 y 2 = a2 y 2 , a > 0. 17. y = tan2 x 5. y = xex 18. y = cos2 x − sin2 x 6. y = (ex + e−x )/2 19. y = sin3 x 7. y = e−x cos x 20. y = x(x2 + 1) x 8. y = e − sin x 21. y = x3 + 6x2 + 9x 9. y = ex /x 22. y = x/(x2 − 9) √ 10. y = 4x + 1 − x p 11. y = (x + 1)/ 5x2 + 35 23. y = x2 /(x2 + 9 √ 24. y = 2 x − x 12. y = x5 − x 25. y = 3 sin(x) − sin3 (x), for x ∈ [0, 2π] 13. y = 6x + sin 3x 26. y = (x − 1)/(x2 ) For each of the following five functions, identify any vertical and horizontal asymptotes, and identify intervals on which the function is concave up and increasing; concave up and decreasing; concave down and increasing; concave down and decreasing. 27. f (θ) = sec(θ) 28. f (x) = 1/(1 + x2 ) 29. f (x) = (x − 3)/(2x − 2) 30. f (x) = 1/(1 − x2 ) 31. f (x) = 1 + 1/(x2 ) 32. Let f (x) = 1/(x2 − a2 ), where a ≥ 0. Find any vertical and horizontal asymptotes and the intervals upon which the given function is concave up and increasing; concave up and 104 Chapter 5 Curve Sketching decreasing; concave down and increasing; concave down and decreasing. Discuss how the value of a affects these features. 6 Applications of the Derivative 6.1 Optimization Many important applied problems involve finding the best way to accomplish some task. Often this involves finding the maximum or minimum value of some function: the minimum time to make a certain journey, the minimum cost for doing a task, the maximum power that can be generated by a device, and so on. Many of these problems can be solved by finding the appropriate function and then using techniques of calculus to find the maximum or the minimum value required. Generally such a problem will have the following mathematical form: Find the largest (or smallest) value of f (x) when a ≤ x ≤ b. Sometimes a or b are infinite, but frequently the real world imposes some constraint on the values that x may have. Such a problem differs in two ways from the local maximum and minimum problems we encountered when graphing functions: We are interested only in the function between a and b, and we want to know the largest or smallest value that f (x) takes on, not merely values that are the largest or smallest in a small interval. That is, we seek not a local maximum or minimum but a global maximum or minimum, sometimes also called an absolute maximum or minimum. Any global maximum or minimum must of course be a local maximum or minimum. If we find all possible local extrema, then the global maximum, if it exists, must be the largest of the local maxima and the global minimum, if it exists, must be the smallest of the local minima. We already know where local extrema can occur: only at those points at which f 0 (x) is zero or undefined. Actually, there are two additional points at which a 105 106 Chapter 6 Applications of the Derivative maximum or minimum can occur if the endpoints a and b are not infinite, namely, at a and b. We have not previously considered such points because we have not been interested in limiting a function to a small interval. An example should make this clear. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. ... ... ... ... ... . . . ... .. ... ... ..... ..... ..... .... . ...... . . . . ....................... −2 Figure 6.1 1 The function g(x) or f (x) = x2 truncated to [−2, 1] EXAMPLE 6.1 Find the maximum and minimum values of f (x) = x2 on the interval [−2, 1], shown in figure 6.1. We compute f 0 (x) = 2x, which is zero at x = 0 and is always defined. Since f 0 (1) = 2 we would not normally flag x = 1 as a point of interest, but it is clear from the graph that when f (x) is restricted to [−2, 1] there is a local maximum at x = 1. Likewise we would not normally pay attention to x = −2, but since we have truncated f at −2 we have introduced a new local maximum there as well. In a technical sense nothing new is going on here: When we truncate f we actually create a new function, let’s call it g, that is defined only on the interval [−2, 1]. If we try to compute the derivative of this new function we actually find that it does not have a derivative at −2 or 1. Why? Because to compute the derivative at 1 we must compute the limit g(1 + ∆x) − g(1) . ∆x→0 ∆x lim This limit does not exist because when ∆x > 0, g(1 + ∆x) is not defined. It is simpler, however, simply to remember that we must always check the endpoints. So the function g, that is, f restricted to [−2, 1], has one critical value and two finite endpoints, any of which might be the global maximum or minimum. We could first determine which of these are local maximum or minimum points (or neither); then the largest 6.1 Optimization 107 local maximum must be the global maximum and the smallest local minimum must be the global minimum. It is usually easier, however, to compute the value of f at every point at which the global maximum or minimum might occur; the largest of these is the global maximum, the smallest is the global minimum. So we compute f (−2) = 4, f (0) = 0, f (1) = 1. The global maximum is 4 at x = −2 and the global minimum is 0 at x = 0. It is possible that there is no global maximum or minimum. It is difficult, and not particularly useful, to express a set procedure for determining whether this is the case. Generally, the best approach is to gain enough understanding of the shape of the graph to decide. Fortunately, only a rough idea of the shape is usually needed. There are some particularly nice cases that are easy. If you have a continuous function on a closed interval [a, b], there is always both a global maximum and a global minimum, so examining the critical values and the endpoints is enough: THEOREM 6.2 Extreme value theorem If f is continuous on a closed interval [a, b], then it has both a minimum and a maximum point. That is, there are real numbers c and d in [a, b] so that for every x in [a, b], f (x) ≤ f (c) and f (x) ≥ f (d). Another easy case: If a function is continuous and has a single critical value, then if there is a local maximum at the critical value it is a global maximum, and if it is a local minimum it is a global minimum. There may also be a global minimum in the first case, or a global maximum in the second case, but that will generally require more effort to determine. EXAMPLE 6.3 Let f (x) = −x2 + 4x − 3. Find the maximum value of f (x) on the interval [0, 4]. First note that f 0 (x) = −2x+4 = 0 when x = 2, and f (2) = 1. Next observe that f 0 (x) is defined for all x, so there are no other critical values. Finally, f (0) = −3 and f (4) = −3. The largest value of f (x) on the interval [0, 4] is f (2) = 1. EXAMPLE 6.4 Let f (x) = −x2 + 4x − 3. Find the maximum value of f (x) on the interval [−1, 1]. First note that f 0 (x) = −2x + 4 = 0 when x = 2. But x = 2 is not in the interval, so we don’t use it. Thus the only two points to be checked are the endpoints; f (−1) = −8 and f (1) = 0. So the largest value of f (x) on [−1, 1] is f (1) = 0. EXAMPLE 6.5 Find the maximum and minimum values of the function f (x) = 7 + |x − 2| for x between 1 and 4 inclusive. The derivative f 0 (x) is never zero, but f 0 (x) is 108 Chapter 6 Applications of the Derivative undefined at x = 2, so we compute f (2) = 7. Checking the end points we get f (1) = 8 and f (4) = 9. The smallest of these numbers is f (2) = 7, which is, therefore, the minimum value of f (x) on the interval 1 ≤ x ≤ 4, and the maximum is f (4) = 9. . ... ... . . .. ... .. ... . . . ... ... ... . . .. ... .... .... . . . . ..... .................................... ...... .............. ......... ................................ ...... ..... . . . ... .... ... ... . . . ... ... ... . . . ... ... ... . . ... ... .. −2 −1 Figure 6.2 1 2 f (x) = x3 − x EXAMPLE 6.6 Find all local maxima and minima for f (x) = x3 − x, and determine whether there is a global maximum or minimum on the open interval (−2, 2). In √ √ example 5.2 we found a local maximum at (− 3/3, 2 3/9) and a local minimum at √ √ ( 3/3, −2 3/9). Since the endpoints are not in the interval (−2, 2) they cannot be considered. Is the lone local maximum a global maximum? Here we must look more closely at √ √ the graph. We know that on the closed interval [− 3/3, 3/3] there is a global maximum √ √ at x = − 3/3 and a global minimum at x = 3/3. So the question becomes: what hap√ √ pens between −2 and − 3/3, and between 3/3 and 2? Since there is a local minimum √ at x = 3/3, the graph must continue up to the right, since there are no more critical √ √ values. This means no value of f will be less than −2 3/9 between 3/3 and 2, but it √ says nothing about whether we might find a value larger than the local maximum 2 3/9. √ How can we tell? Since the function increases to the right of 3/3, we need to know what the function values do “close to” 2. Here the easiest test is to pick a number and do a √ computation to get some idea of what’s going on. Since f (1.9) = 4.959 > 2 3/9, there √ is no global maximum at − 3/3, and hence no global maximum at all. (How can we tell √ that 4.959 > 2 3/9? We can use a calculator to approximate the right hand side; if it is √ not even close to 4.959 we can take this as decisive. Since 2 3/9 ≈ 0.3849, there’s really no question. Funny things can happen in the rounding done by computers and calculators, however, so we might be a little more careful, especially if the values come out quite close. √ √ In this case we can convert the relation 4.959 > 2 3/9 into (9/2)4.959 > 3 and ask 6.1 Optimization 109 whether this is true. Since the left side is clearly larger than 4 · 4 which is clearly larger √ than 3, this settles the question.) A similar analysis shows that there is also no global minimum. The graph of f (x) on (−2, 2) is shown in figure 6.2. EXAMPLE 6.7 Of all rectangles of area 100, which has the smallest perimeter? First we must translate this into a purely mathematical problem in which we want to find the minimum value of a function. If x denotes one of the sides of the rectangle, then the adjacent side must be 100/x (in order that the area be 100). So the function we want to minimize is 100 f (x) = 2x + 2 x since the perimeter is twice the length plus twice the width of the rectangle. Not all values of x make sense in this problem: lengths of sides of rectangles must be positive, so x > 0. If x > 0 then so is 100/x, so we need no second condition on x. We next find f 0 (x) and set it equal to zero: 0 = f 0 (x) = 2 − 200/x2 . Solving f 0 (x) = 0 for x gives us x = ±10. We are interested only in x > 0, so only the value x = 10 is of interest. Since f 0 (x) is defined everywhere on the interval (0, ∞), there are no more critical values, and there are no endpoints. Is there a local maximum, minimum, or neither at x = 10? The second derivative is f 00 (x) = 400/x3 , and f 00 (10) > 0, so there is a local minimum. Since there is only one critical value, this is also the global minimum, so the rectangle with smallest perimeter is the 10 × 10 square. EXAMPLE 6.8 You want to sell a certain number n of items in order to maximize your profit. Market research tells you that if you set the price at $1.50, you will be able to sell 5000 items, and for every 10 cents you lower the price below $1.50 you will be able to sell another 1000 items. Suppose that your fixed costs (“start-up costs”) total $2000, and the per item cost of production (“marginal cost”) is $0.50. Find the price to set per item and the number of items sold in order to maximize profit, and also determine the maximum profit you can get. The first step is to convert the problem into a function maximization problem. Since we want to maximize profit by setting the price per item, we should look for a function P (x) representing the profit when the price per item is x. Profit is revenue minus costs, and revenue is number of items sold times the price per item, so we get P = nx − 2000 − 0.50n. The number of items sold is itself a function of x, n = 5000 + 1000(1.5 − x)/0.10, because (1.5 − x)/0.10 is the number of multiples of 10 cents that the price is below $1.50. Now 110 Chapter 6 Applications of the Derivative we substitute for n in the profit function: P (x) = (5000 + 1000(1.5 − x)/0.10)x − 2000 − 0.5(5000 + 1000(1.5 − x)/0.10) = −10000x2 + 25000x − 12000 We want to know the maximum value of this function when x is between 0 and 1.5. The derivative is P 0 (x) = −20000 ∗ x + 25000, which is zero when x = 1.25. Since P 00 (x) = −20000 < 0, there must be a local maximum at x = 1.25, and since this is the only critical value it must be a global maximum as well. (Alternately, we could compute P (0) = −12000, P (1.25) = 3625, and P (1.5) = 3000 and note that P (1.25) is the maximum of these.) Thus the maximum profit is $3625, attained when we set the price at $1.25 and sell 7500 items. ... .. ... .. ... ... . ... . . ... ... ... ... ... ... . ... . ....... ........ ....... ....... ....... ....... ....... ....... ....... ....... ....... ....... ....... ....... ....... ....... ................... ... ... ... ... ... .. ... . . ... ... .. .. .. .. .. . . .. .. .. .. .. .. .. .. . . .. ... .. .. ... ... ... . . ... ... ... .. ... ... ... ... ... . ... ... ... 2 ... .... ... .... ... . . . .... .. . . .... . ..... .... ..... ..... ...... ..... ........ ...... . . . . . . ............. . . .......................... • Figure 6.3 y=a (x, x ) Rectangle in a parabola. EXAMPLE 6.9 Find the largest rectangle (that is, the rectangle with largest area) that fits inside the graph of the parabola y = x2 below the line y = a (a is an unspecified constant value). (The top side of the rectangle should be on the horizontal line y = a; see figure 6.3.) We want to find the maximum value of some function A(x) representing area. Perhaps the hardest part of this problem is deciding what x should represent. The lower right corner of the rectangle is at (x, x2 ), and once this is chosen the rectangle is completely determined. So we can let the x in A(x) be the x of the parabola f (x) = x2 . Then the area is A(x) = (2x)(a − x2 ) = −2x3 + 2ax. We want the maximum value of A(x) when x is √ √ in [0, a]. (You might object to allowing x = 0 or x = a, since then the “rectangle” has 6.1 Optimization 111 either no width or no height, so is not “really” a rectangle. But the problem is somewhat easier if we simply allow such rectangles, which have zero area.) p Setting 0 = A0 (x) = 6x2 + 2a we get x = a/3 as the only critical value. Testing this p √ √ and the two endpoints, we have A(0) = A( a) = 0 and A( a/3) = (4/9) 3a3/2 . The p maximum area thus occurs when the rectangle has dimensions 2 a/3 × (2/3)a. (h − R, r) ............................... ......... .............. ........ ......... ....... . . . ........ . . .. . ...... ... ...... . . . . ...... ... .......... . ... . . . . . . . ... ..... . ... . . . . . . . . . ... ... .. ...... ... ... ... ....... ... ... ...... . . . . . ... . . . . ... . ... . . . ... . . . . . .. . . . . . ... . . . . . . ... ... . . . ... . . . . ... . . . ... . . . .... . .. . . . . . ... . . ... . . ... ... .. ............ .... ... .......... .......... ... . .. ... ... ......... . . . . . ... . ....... . . . . . . ... ....... ... ....... ... ... ....... .... ... ... ....... .. ... ... . . . . . . . . ....... ... ... ....... ... ... ....... .... ... ... ....... ... .. ... . ....... . ..... . . . ... ....... .. ..... ....... ..... ..... ....... .... ......... ...... ............. ....... . . . . ........ . ...... ........... ........................................... Figure 6.4 Cone in a sphere. EXAMPLE 6.10 If you fit the largest possible cone inside a sphere, what fraction of the volume of the sphere is occupied by the cone? (Here by “cone” we mean a right circular cone, i.e., a cone for which the base is perpendicular to the axis of symmetry, and for which the cross-section cut perpendicular to the axis of symmetry at any point is a circle.) Let R be the radius of the sphere, and let r and h be the base radius and height of the cone inside the sphere. What we want to maximize is the volume of the cone: πr2 h/3. Here R is a fixed value, but r and h can vary. Namely, we could choose r to be as large as possible—equal to R—by taking the height equal to R; or we could make the cone’s height h larger at the expense of making r a little less than R. See the side view depicted in figure 6.4. We have situated the picture in a convenient way relative to the x and y axes, namely, with the center of the sphere at the origin and the vertex of the cone at the far left on the x-axis. Notice that the function we want to maximize, πr2 h/3, depends on two variables. This is frequently the case, but often the two variables are related in some way so that “really” there is only one variable. So our next step is to find the relationship and use it to solve for one of the variables in terms of the other, so as to have a function of only one variable 112 Chapter 6 Applications of the Derivative to maximize. In this problem, the condition can be read off the above drawing: the upper corner of the triangle, whose coordinates are (h − R, r), must be on the circle of radius R. That is, (h − R)2 + r2 = R2 . We can solve for h in terms of r or for r in terms of h. Either involves taking a square root, but we notice that the volume function contains r2 , not r by itself, so it is easiest to solve for r2 directly: r2 = R2 − (h − R)2 . Then we substitute the result into πr2 h/3: V (h) = π(R2 − (h − R)2 )h/3 2 π = − h3 + πh2 R 3 3 We want to maximize V (h) when h is between 0 and 2R. Now we solve 0 = f 0 (h) = −πh2 + (4/3)πhR, getting h = 0 or h = 4R/3. We compute V (0) = V (2R) = 0 and V (4R/3) = (32/81)πR3 . The maximum is the latter; since the volume of the sphere is (4/3)πR3 , the fraction of the sphere occupied by the cone is (32/81)πR3 8 = ≈ 30%. (4/3)πR3 27 EXAMPLE 6.11 You are making cylindrical containers to contain a given volume. Suppose that the top and bottom are made of a material that is N times as expensive (cost per unit area) as the material used for the lateral side of the cylinder. Find (in terms of N ) the ratio of height to base radius of the cylinder that minimizes the cost of making the containers. Let us first choose letters to represent various things: h for the height, r for the base radius, V for the volume of the cylinder, and c for the cost per unit area of the lateral side of the cylinder; V and c are constants, h and r are variables. Now we can write the cost of materials: c(2πrh) + N c(2πr2 ). Again we have two variables; the relationship is provided by the fixed volume of the cylinder: V = πr2 h. We use this relation to eliminate h (we could eliminate r, but it’s a little easier if we eliminate h, which appears in only one place in the above formula for cost). The result is f (r) = 2cπr 2cV V + 2N cπr2 = + 2N cπr2 . 2 πr r 6.1 Optimization 113 We want to know the minimum value of this function when r is in (0, ∞). We now set p 0 = f 0 (r) = −2cV /r2 + 4N cπr, giving r = 3 V /(2N π). Since f 00 (r) = 4cV /r3 + 4N cπ is positive when r is positive, there is a local minimum at the critical value, and hence a global minimum since there is only one critical value. Finally, since h = V /(πr2 ), V h V = 3 = = 2N, r πr π(V /(2N π)) so the minimum cost occurs when the height h is 2N times the radius. If, for example, there is no difference in the cost of materials, the height is twice the radius (or the height is equal to the diameter). A . .. .• .. .. .. . .. b .. . .. . .. .. . a−x . x •...................................•.. • D Figure 6.5 B C Minimizing travel time. EXAMPLE 6.12 Suppose you want to reach a point A that is located across the sand from a nearby road (see figure 6.5). Suppose that the road is straight, and b is the distance from A to the closest point C on the road. Let v be your speed on the road, and let w, which is less than v, be your speed on the sand. Right now you are at the point D, which is a distance a from C. At what point B should you turn off the road and head across the sand in order to minimize your travel time to A? Let x be the distance short of C where you turn off, i.e., the distance from B to C. We want to minimize the total travel time. Recall that when traveling at constant velocity, time=distance/velocity. You travel the distance DB at speed v, and then the distance BA at speed w. Since √ DB = a − x and, by the Pythagorean theorem, BA = x2 + b2 , the total time for the trip is √ a−x x2 + b2 + . f (x) = v w 114 Chapter 6 Applications of the Derivative We want to find the minimum value of f when x is between 0 and a. As usual we set f 0 (x) = 0 and solve for x: 1 x 0 = f 0 (x) = − + √ v w x2 + b2 p w x2 + b2 = vx w2 (x2 + b2 ) = v 2 x2 w2 b2 = (v 2 − w2 )x2 x= √ wb v 2 − w2 Notice that a does not appear in the last expression, but a is not irrelevant, since we are √ interested only in critical values that are in [0, a], and wb/ v 2 − w2 is either in this interval or not. If it is, we can use the second derivative to test it: f 00 (x) = b2 . (x2 + b2 )3/2 w Since this is always positive there is a local minimum at the critical point, and so it is a global minimum as well. If the critical value is not in [0, a] it is larger than a. In this case the minimum must occur at one of the endpoints. We can compute a b + v w √ a2 + b2 f (a) = w f (0) = but it is difficult to determine which of these is smaller by direct comparison. If, as is likely in practice, we know the values of v, w, a, and b, then it is easy to determine this. With a little cleverness, however, we can determine the minimum in general. We have seen that f 00 (x) is always positive, so the derivative f 0 (x) is always increasing. We know that √ at wb/ v 2 − w2 the derivative is zero, so for values of x less than that critical value, the derivative is negative. This means that f (0) > f (a), so the minimum occurs when x = a. √ So the upshot is this: If you start farther away from C than wb/ v 2 − w2 then you √ always want to cut across the sand when you are a distance wb/ v 2 − w2 from point C. If you start closer than this to C, you should cut directly across the sand. 6.1 Optimization 115 Summary—Steps to solve an optimization problem. 1. Decide what the variables are and what the constants are, draw a diagram if appropriate, understand clearly what it is that is to be maximized or minimized. 2. Write a formula for the function for which you wish to find the maximum or minimum. 3. Express that formula in terms of only one variable, that is, in the form f (x). 4. Set f 0 (x) = 0 and solve. Check all critical values and endpoints to determine the extreme value. Exercises  1 + 4x − x2 for x ≤ 3 (x + 5)/2 for x > 3 Find the maximum value and minimum values of f (x) for x in [0, 4]. Graph f (x) to check your answers. ⇒ 1. Let f (x) = 2. Find the dimensions of the rectangle of largest area having fixed perimeter 100. ⇒ 3. Find the dimensions of the rectangle of largest area having fixed perimeter P . ⇒ 4. A box with square base and no top is to hold a volume 100. Find the dimensions of the box that requires the least material for the five sides. Also find the ratio of height to side of the base. ⇒ 5. A box with square base is to hold a volume 200. The bottom and top are formed by folding in flaps from all four sides, so that the bottom and top consist of two layers of cardboard. Find the dimensions of the box that requires the least material. Also find the ratio of height to side of the base. ⇒ 6. A box with square base and no top is to hold a volume V . Find (in terms of V ) the dimensions of the box that requires the least material for the five sides. Also find the ratio of height to side of the base. (This ratio will not involve V .) ⇒ 7. You have 100 feet of fence to make a rectangular play area alongside the wall of your house. The wall of the house bounds one side. What is the largest size possible (in square feet) for the play area? ⇒ 8. You have l feet of fence to make a rectangular play area alongside the wall of your house. The wall of the house bounds one side. What is the largest size possible (in square feet) for the play area? ⇒ 9. Marketing tells you that if you set the price of an item at $10 then you will be unable to sell it, but that you can sell 500 items for each dollar below $10 that you set the price. Suppose your fixed costs total $3000, and your marginal cost is $2 per item. What is the most profit you can make? ⇒ 10. Find the area of the largest rectangle that fits inside a semicircle of radius 10 (one side of the rectangle is along the diameter of the semicircle). ⇒ 11. Find the area of the largest rectangle that fits inside a semicircle of radius r (one side of the rectangle is along the diameter of the semicircle). ⇒ 116 Chapter 6 Applications of the Derivative 12. For a cylinder with surface area 50, including the top and the bottom, find the ratio of height to base radius that maximizes the volume. ⇒ 13. For a cylinder with given surface area S, including the top and the bottom, find the ratio of height to base radius that maximizes the volume. ⇒ 14. You want to make cylindrical containers to hold 1 liter using the least amount of construction material. The side is made from a rectangular piece of material, and this can be done with no material wasted. However, the top and bottom are cut from squares of side 2r, so that 2(2r)2 = 8r2 of material is needed (rather than 2πr2 , which is the total area of the top and bottom). Find the dimensions of the container using the least amount of material, and also find the ratio of height to radius for this container. ⇒ 15. You want to make cylindrical containers of a given volume V using the least amount of construction material. The side is made from a rectangular piece of material, and this can be done with no material wasted. However, the top and bottom are cut from squares of side 2r, so that 2(2r)2 = 8r2 of material is needed (rather than 2πr 2 , which is the total area of the top and bottom). Find the optimal ratio of height to radius. ⇒ 16. Given a right circular cone, you put an upside-down cone inside it so that its vertex is at the center of the base of the larger cone and its base is parallel to the base of the larger cone. If you choose the upside-down cone to have the largest possible volume, what fraction of the volume of the larger cone does it occupy? (Let H and R be the height and base radius of the larger cone, and let h and r be the height and base radius of the smaller cone. Hint: Use similar triangles to get an equation relating h and r.) ⇒ 17. In example 6.12, what happens if w ≥ v (i.e., your speed on sand is at least your speed on the road)? ⇒ 18. A container holding a fixed volume is being made in the shape of a cylinder with a hemispherical top. (The hemispherical top has the same radius as the cylinder.) Find the ratio of height to radius of the cylinder which minimizes the cost of the container if (a) the cost per unit area of the top is twice as great as the cost per unit area of the side, and the container is made with no bottom; (b) the same as in (a), except that the container is made with a circular bottom, for which the cost per unit area is 1.5 times the cost per unit area of the side. ⇒ 19. A piece of carboard is 1 meter by 1/2 meter. A square is to be cut from each corner and the sides folded up to make an open-top box. What are the dimensions of the box with maximum possible volume? ⇒ 20. (a) A square piece of cardboard of side a is used to make an open-top box by cutting out a small square from each corner and bending up the sides. How large a square should be cut from each corner in order that the box have maximum volume? (b) What if the piece of cardboard used to make the box is a rectangle of sides a and b? ⇒ 21. A window consists of a rectangular piece of clear glass with a semicircular piece of colored glass on top; the colored glass transmits only 1/2 as much light per unit area as the the clear glass. If the distance from top to bottom (across both the rectangle and the semicircle) is 2 meters and the window may be no more than 1.5 meters wide, find the dimensions of the rectangular portion of the window that lets through the most light. ⇒ 22. A window consists of a rectangular piece of clear glass with a semicircular piece of colored glass on top. Suppose that the colored glass transmits only k times as much light per unit 6.1 Optimization 117 area as the clear glass (k is between 0 and 1). If the distance from top to bottom (across both the rectangle and the semicircle) is a fixed distance H, find (in terms of k) the ratio of vertical side to horizontal side of the rectangle for which the window lets through the most light. ⇒ 23. You are designing a poster to contain a fixed amount A of printing (measured in square centimeters) and have margins of a centimeters at the top and bottom and b centimeters at the sides. Find the ratio of vertical dimension to horizontal dimension of the printed area on the poster if you want to minimize the amount of posterboard needed. ⇒ 24. The strength of a rectangular beam is proportional to the product of its width w times the square of its depth d. Find the dimensions of the strongest beam that can be cut from a cylindrical log of radius r. ⇒ ............................. .......... . ............................................................. ...... .... . . . .... ......... . ... .... . ... .. . . ... ... ... ... . ... . . .. ... . . ... . . ... .... ..... .... ... ... . .... .. . . ... . . ... . . ... . . . . . . ... ... . . . . . ... .... ... .... ... .. .. ... ... . ... ... ... ..... .... .. ....... ..... .. .. ..... ........ ............................................................... ................................ ↑ | | d | | ← − w − → ↓ Figure 6.6 Cutting a beam. 25. What fraction of the volume of a sphere is taken up by the largest cylinder that can be fit inside the sphere? ⇒ 26. The U.S. post office will accept a box for shipment only if the sum of the length and girth (distance around) is at most 108 in. Find the dimensions of the largest acceptable box with square front and back. ⇒ 27. Find the dimensions of the lightest cylindrical can containing 0.25 liter (=250 cm3 ) if the top and bottom are made of a material that is twice as heavy (per unit area) as the material used for the side. ⇒ 28. A conical paper cup is to hold 1/4 of a liter. Find the height and radius of the√cone which minimizes the amount of paper needed to make the cup. Use the formula πr r2 + h2 for the area of the side of a cone. ⇒ 29. A conical paper cup is to hold a fixed volume of water. Find the ratio of height to base radius of √ the cone which minimizes the amount of paper needed to make the cup. Use the formula πr r2 + h2 for the area of the side of a cone, called the lateral area of the cone. ⇒ 30. If you fit the cone with the largest possible surface area (lateral area plus area of base) into a sphere, what percent of the volume of the sphere is occupied by the cone? ⇒ 31. Two electrical charges, one a positive charge A of magnitude a and the other a negative charge B of magnitude b, are located a distance c apart. A positively charged particle P is situated on the line between A and B. Find where P should be put so that the pull away from A towards B is minimal. Here assume that the force from each charge is proportional 118 Chapter 6 Applications of the Derivative to the strength of the source and inversely proportional to the square of the distance from the source. ⇒ 32. Find the fraction of the area of a triangle that is occupied by the largest rectangle that can be drawn in the triangle (with one of its sides along a side of the triangle). Show that this fraction does not depend on the dimensions of the given triangle. ⇒ 33. How are your answers to Problem 9 affected if the cost per item for the x items, instead of being simply $2, decreases below $2 in proportion to x (because of economy of scale and volume discounts) by 1 cent for each 25 items produced? ⇒ 34. You are standing near the side of a large wading pool of uniform depth when you see a child in trouble. You can run at a speed v1 on land and at a slower speed v2 in the water. Your perpendicular distance from the side of the pool is a, the child’s perpendicular distance is b, and the distance along the side of the pool between the closest point to you and the closest point to the child is c (see the figure below). Without stopping to do any calculus, you instinctively choose the quickest route (shown in the figure) and save the child. Our purpose is to derive a relation between the angle θ1 your path makes with the perpendicular to the side of the pool when you’re on land, and the angle θ2 your path makes with the perpendicular when you’re in the water. To do this, let x be the distance between the closest point to you at the side of the pool and the point where you enter the water. Write the total time you run (on land and in the water) in terms of x (and also the constants a, b, c, v1 , v2 ). Then set the derivative equal to zero. The result, called “Snell’s law” or the “law of refraction,” also governs the bending of light when it goes into water. ⇒ . ... ... ... . . . ... ... ...............2 ............. . ... ... ... . . . ... ... ... . . .... ...... ...... ...... . . . . . ..... ...... ........... ...... .......... . . . . . ......... ..... .......... ..... ...... 1 ...... . . . . . ...... ...... ...... ...... . . . . . θ c−x x a Figure 6.7 b θ Wading pool rescue. 6.2 Related Rates Suppose we have two variables x and y (in most problems the letters will be different, but for now let’s use x and y) which are both changing with time. A “related rates” problem is a problem in which we know one of the rates of change at a given instant—say, ẋ = dx/dt—and we want to find the other rate ẏ = dy/dt at that instant. (The use of 6.2 Related Rates 119 ẋ to mean dx/dt goes back to Newton and is still used for this purpose, especially by physicists.) If y is written in terms of x, i.e., y = f (x), then this is easy to do using the chain rule: ẏ = dy dy dx dy = · = ẋ. dt dx dt dx That is, find the derivative of f (x), plug in the value of x at the instant in question, and multiply by the given value of ẋ = dx/dt to get ẏ = dy/dt. EXAMPLE 6.13 Suppose an object is moving along a path described by y = x2 , that is, it is moving on a parabolic path. At a particular time, say t = 5, the x coordinate is 6 and we measure the speed at which the x coordinate of the object is changing and find that dx/dt = 3. At the same time, how fast is the y coordinate changing? Using the chain rule, dy/dt = 2x · dx/dt. At t = 5 we know that x = 6 and dx/dt = 3, so dy/dt = 2 · 6 · 3 = 36. In many cases, particularly interesting ones, x and y will be related in some other way, for example x = f (y), or F (x, y) = k, or perhaps F (x, y) = G(x, y), where F (x, y) and G(x, y) are expressions involving both variables. In all cases, you can solve the related rates problem by taking the derivative of both sides, plugging in all the known values (namely, x, y, and ẋ), and then solving for ẏ. To summarize, here are the steps in doing a related rates problem: 1. Decide what the two variables are. 2. Find an equation relating them. 3. Take d/dt of both sides. 4. Plug in all known values at the instant in question. 5. Solve for the unknown rate. EXAMPLE 6.14 A plane is flying directly away from you at 500 mph at an altitude of 3 miles. How fast is the plane’s distance from you increasing at the moment when the plane is flying over a point on the ground 4 miles from you? To see what’s going on, we first draw a schematic representation of the situation. Because the plane is in level flight directly away from you, the rate at which x changes is the speed of the plane, dx/dt = 500. The distance between you and the plane is y; it is dy/dt that we wish to know. By the Pythagorean Theorem we know that x2 + 9 = y 2 . Taking the derivative: 2xẋ = 2y ẏ. 120 Chapter 6 Applications of the Derivative y ..... ...... ...... ....... ...... ....... ....... .. ....... ....... ...... −→ ..... 3 x Figure 6.8 Receding airplane. We are interested in the time at which x = 4; at this time we know that 42 + 9 = y 2 , so y = 5. Putting together all the information we have we get 2(4)(500) = 2(5)ẏ. Thus, ẏ = 400 mph. EXAMPLE 6.15 You are inflating a spherical balloon at the rate of 7 cm3 /sec. How fast is its radius increasing when the radius is 4 cm? Here the variables are the radius r and the volume V . We know dV /dt, and we want dr/dt. The two variables are related by means of the equation V = 4πr3 /3. Taking the derivative of both sides gives dV /dt = 4πr2 ṙ. We now substitute the values we know at the instant in question: 7 = 4π42 ṙ, so ṙ = 7π/64 cm/sec. EXAMPLE 6.16 Water is poured into a conical container at the rate of 10 cm3 /sec. The cone points directly down, and it has a height of 30 cm and a base radius of 10 cm. How fast is the water level rising when the water is 4 cm deep (at its deepest point)? The water forms a conical shape within the big cone; its height and base radius and volume are all increasing as water is poured into the container. This means that we actually have three things varying with time: the water level h (the height of the cone of water), the radius r of the circular top surface of water (the base radius of the cone of water), and the volume of water V . The volume of a cone is given by V = πr2 h/3. We know dV /dt, and we want dh/dt. At first something seems to be wrong: we have a third variable r whose rate we don’t know. But the dimensions of the cone of water must have the same proportions as those of the container. That is, because of similar triangles, r/h = 10/30 so r = h/3. Now we can eliminate r from the problem entirely: V = π(h/3)2 h/3 = πh3 /27. We take the derivative of both sides and plug in h = 4 and dV /dt = 10, obtaining 10 = (3π · 42 /27)(dh/dt). Thus, dh/dt = 90/(16π) cm/sec. 6.2 Related Rates 121 ← − 10 − → ....................................... ................... ......... ......... ...... ...... ... ..... .. ...... .... .............. ........... . . . . . . . . . . . . . . ... ..................................................... ... ... ... ... ... ... . . ... ... ... ... ... ... ... . . ... ... ... ... ... .. ... ................ ................... ... . . . .. r .. .. ... ... ........................................... ... .... ..... .......... .... ↑| ... ..................................... ... . . . ... 30 ... . ... . .. ... ↓| .. ... ... ... ... ... . . . . ... ↑| . ... ... ... ... h ... .... ... ... ... ... ↓| ..... .. Figure 6.9 Conical water tank. EXAMPLE 6.17 A swing consists of a board at the end of a 10 ft long rope. Think of the board as a point P at the end of the rope, and let Q be the point of attachment at the other end. Suppose that the swing is directly below Q at time t = 0, and is being pushed by someone who walks at 6 ft/sec from left to right. Find (a) how fast the swing is rising after 1 sec; (b) the angular speed of the rope in deg/sec after 1 sec. We start out by asking: What is the geometric quantity whose rate of change we know, and what is the geometric quantity whose rate of change we’re being asked about? Note that the person pushing the swing is moving horizontally at a rate we know. In other words, the horizontal coordinate of P is increasing at 6 ft/sec. In the xy-plane let us make the convenient choice of putting the origin at the location of P at time t = 0, i.e., a distance 10 directly below the point of attachment. Then the rate we know is dx/dt, and in part (a) the rate we want is dy/dt (the rate at which P is rising). In part (b) the rate we want is θ̇ = dθ/dt, where θ stands for the angle in radians through which the swing has swung from the vertical. (Actually, since we want our answer in deg/sec, at the end we must convert dθ/dt from rad/sec by multiplying by 180/π.) (a) From the diagram we see that we have a right triangle whose legs are x and 10 − y, and whose hypotenuse is 10. Hence x2 + (10 − y)2 = 100. Taking the derivative of both sides we obtain: 2xẋ + 2(10 − y)(0 − ẏ) = 0. We now look at what we know after 1 second, namely x = 6 (because x started at 0 and has been increasing at the rate of 6 ft/sec for 1 sec), y = 2 (because we get 10 − y = 8 from the Pythagorean theorem applied to the triangle with hypotenuse 10 and leg 6), and ẋ = 6. Putting in these values gives us 2 · 6 · 6 − 2 · 8ẏ = 0, from which we can easily solve for ẏ: ẏ = 4.5 ft/sec. 122 Chapter 6 Applications of the Derivative Q •...... ... ... ... ... .. ........ . . . . ....... ... ..... .. θ ....... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... x . ...... ... ...... .....• . . . ....... ... . . . . ....... . ...... ........ y ........ ......... ......... .......... .......... .............. ....................................................................... Figure 6.10 P Swing. (b) Here our two variables are x and θ, so we want to use the same right triangle as in part (a), but this time relate θ to x. Since the hypotenuse is constant (equal to 10), the best way to do this is to use the sine: sin θ = x/10. Taking derivatives we obtain (cos θ)θ̇ = 0.1ẋ. At the instant in question (t = 1 sec), when we have a right triangle with sides 6–8–10, cos θ = 8/10 and ẋ = 6. Thus (8/10)θ̇ = 6/10, i.e., θ̇ = 6/8 = 3/4 rad/sec, or approximately 43 deg/sec. We have seen that sometimes there are apparently more than two variables that change with time, but in reality there are just two, as the others can be expressed in terms of just two. But sometimes there really are several variables that change with time; as long as you know the rates of change of all but one of them you can find the rate of change of the remaining one. As in the case when there are just two variables, take the derivative of both sides of the equation relating all of the variables, and then substitute all of the known values and solve for the unknown rate. EXAMPLE 6.18 A road running north to south crosses a road going east to west at the point P . Car A is driving north along the first road, and car B is driving east along the second road. At a particular time car A is 10 kilometers to the north of P and traveling at 80 km/hr, while car B is 15 kilometers to the east of P and traveling at 100 km/hr. How fast is the distance between the two cars changing? Let a(t) be the distance of car A north of P at time t, and b(t) the distance of car B east of P at time t, and let c(t) the distance from car A to car B at time t. By the Pythagorean Theorem, c(t)2 = a(t)2 +b(t)2 . Taking derivatives we get 2c(t)c0 (t) = 2a(t)a0 (t)+2b(t)b0 (t), 6.2 ...... (0, a(t)) •........ P ....... ....... ....... • ....... Related Rates 123 c(t) ....... ....... ....... ....... ............ ....... • (b(t), 0) Figure 6.11 Cars moving apart. so ċ = aȧ + bḃ aȧ + bḃ =√ . c a2 + b2 Substituting known values we get: ċ = 10 · 80 + 15 · 100 460 √ = √ ≈ 127.6km/hr 2 2 13 10 + 15 at the time of interest. Notice how this problem differs from example 6.14. In both cases we started with the Pythagorean Theorem and took derivatives on both sides. However, in example 6.14 one of the sides was a constant (the altitude of the plane), and so the derivative of the square of that side of the triangle was simply zero. In this example, on the other hand, all three sides of the right triangle are variables, even though we are interested in a specific value of each side of the triangle (namely, when the sides have lengths 10 and 15). Make sure that you understand at the start of the problem what are the variables and what are the constants. 124 Chapter 6 Applications of the Derivative Exercises 1. A cylindrical tank standing upright (with one circular base on the ground) has radius 20 cm. How fast does the water level in the tank drop when the water is being drained at 25 cm3 /sec? ⇒ 2. A cylindrical tank standing upright (with one circular base on the ground) has radius 1 meter. How fast does the water level in the tank drop when the water is being drained at 3 liters per second? ⇒ 3. A ladder 13 meters long rests on horizontal ground and leans against a vertical wall. The foot of the ladder is pulled away from the wall at the rate of 0.6 m/sec. How fast is the top sliding down the wall when the foot of the ladder is 5 m from the wall? ⇒ 4. A ladder 13 meters long rests on horizontal ground and leans against a vertical wall. The top of the ladder is being pulled up the wall at 0.1 meters per second. How fast is the foot of the ladder approaching the wall when the foot of the ladder is 5 m from the wall? ⇒ 5. A rotating beacon is located 2 miles out in the water. Let A be the point on the shore that is closest to the beacon. As the beacon rotates at 10 rev/min, the beam of light sweeps down the shore once each time it revolves. Assume that the shore is straight. How fast is the point where the beam hits the shore moving at an instant when the beam is lighting up a point 2 miles along the shore from the point A? ⇒ 6. A baseball diamond is a square 90 ft on a side. A player runs from first base to second base at 15 ft/sec. At what rate is the player’s distance from third base decreasing when she is half way from first to second base? ⇒ 7. Sand is poured onto a surface at 15 cm3 /sec, forming a conical pile whose base diameter is always equal to its altitude. How fast is the altitude of the pile increasing when the pile is 3 cm high? ⇒ 8. A boat is pulled in to a dock by a rope with one end attached to the front of the boat and the other end passing through a ring attached to the dock at a point 5 ft higher than the front of the boat. The rope is being pulled through the ring at the rate of 0.6 ft/sec. How fast is the boat approaching the dock when 13 ft of rope are out? ⇒ 9. A balloon is at a height of 50 meters, and is rising at the constant rate of 5 m/sec. A bicyclist passes beneath it, traveling in a straight line at the constant speed of 10 m/sec. How fast is the distance between the bicylist and the balloon increasing 2 seconds later? ⇒ 10. A pyramid-shaped vat has square cross-section and stands on its tip. The dimensions at the top are 2 m × 2 m, and the depth is 5 m. If water is flowing into the vat at 3 m3 /min, how fast is the water level rising when the depth of water (at the deepest point) is 4 m? Note: the volume of any “conical” shape (including pyramids) is (1/3)(height)(area of base). ⇒ 11. The sun is rising at the rate of 1/4 deg/min, and appears to be climbing into the sky perpendicular to the horizon. How fast is the shadow of a 200 meter building shrinking at the moment when the shadow is 500 meters long? ⇒ 12. The sun is setting at the rate of 1/4 deg/min, and appears to be climbing into the sky perpendicular to the horizon. How fast is the shadow of a 25 meter wall lengthening at the moment when the shadow is 50 meters? ⇒ 13. The trough shown in figure 6.13 is constructed by fastening together three slabs of wood of dimensions 10 ft × 1 ft, and then attaching the construction to a wooden wall at each end. 6.2 k ...... . ...... . ...... . ...... . ...... . ...... . .. .. .. . ...... . ...... . Figure 6.12 ...... . ...... . Related Rates 125 ...... . Sunset. The angle θ was originally 30◦ , but because of poor construction the sides are collapsing. The trough is full of water. At what rate (in ft3 /sec) is the water spilling out over the top of the trough if the sides have each fallen to an angle of 45◦ , and are collapsing at the rate of 1◦ per second? ⇒ ................................................................. .......... .. ..... . ......... .. .......... ..... .......... . . . . . .. .................. . . . ... .. . . . . . . . . . . . . . . . . . ... 1 ...... . ...... .......... ... ........ ... ... ............... .......... . . . . . . . . . . . . . . . . . ... ... .......... .......... .......... .......... ... ... ... ... ................. .......... ......... ......... .. .......... ... ... ... ... ................. .......... . . . . . . . . . . . . . . . . . . . . . . ... ... .......... .......... .......... ......... .. ...... ... ................. ......... . .......... .......... .. ......... .. ... ... ... .................. ......... . . . . . . . . . . . . . . . . . . . . . . ... ... .......... .......... .......... ......... . ...... ... .................. ......... .......... .......... .. .......... .......... . ... ... ... .......... . . . . . . . . . . . . . . . . . . . . . . . . . 10 .... .... .... . .. .......... .......... .......... . ... .. .......... ... ...... .. .................. .......... . .......... .......... .. ............................................................................ ... ... ... .. .................. ... . .. .... ... ... ... ........ ...θ . . ......... .θ ... .. . ...... .......... . ................. .................. ......... . . . . . 1 ................................................... 1 Figure 6.13 Trough. 14. A woman 5 ft tall walks at the rate of 3.5 ft/sec away from a streetlight that is 12 ft above the ground. At what rate is the tip of her shadow moving? At what rate is her shadow lengthening? ⇒ 15. A man 1.8 meters tall walks at the rate of 1 meter per second toward a streetlight that is 4 meters above the ground. At what rate is the tip of his shadow moving? At what rate is his shadow shortening? ⇒ 16. A police helicopter is flying at 150 mph at a constant altitude of 0.5 mile above a straight road. The pilot uses radar to determine that an oncoming car is at a distance of exactly 1 mile from the helicopter, and that this distance is decreasing at 190 mph. Find the speed of the car. ⇒ 17. A police helicopter is flying at 200 kilometers per hour at a constant altitude of 1 km above a straight road. The pilot uses radar to determine that an oncoming car is at a distance of exactly 2 kilometers from the helicopter, and that this distance is decreasing at 250 kph. Find the speed of the car. ⇒ 18. A light shines from the top of a pole 20 m high. A ball is falling 10 meters from the pole, casting a shadow on a building 30 meters away. When the ball is 25 meters from the ground it is falling at 6 meters per second. How fast is its shadow moving? ⇒ 126 Chapter 6 Applications of the Derivative ...... .... ..... . . . ... .... ..... ...... . . . . . . k • Figure 6.14 Falling ball. 19. Do example 6.18 assuming that the angle between the two roads is 120◦ instead of 90◦ (that is, the “north–south” road actually goes in a somewhat northwesterly direction from P ). Recall the law of cosines: c2 = a2 + b2 − 2ab cos θ. ⇒ 20. Do example 6.18 assuming that car A is 300 meters north of P , car B is 400 meters east of P , both cars are going at constant speed toward P , and the two cars will collide in 10 seconds. ⇒ 21. Do example 6.18 assuming that 8 seconds ago car A started from rest at P and has been picking up speed at the steady rate of 5 m/sec2 , and 6 seconds after car A started car B passed P moving east at constant speed 60 m/sec. ⇒ 22. Referring again to example 6.18, suppose that instead of car B an airplane is flying at speed 200 km/hr to the east of P at an altitude of 2 km, as depicted in figure 6.15. How fast is the distance between car and airplane changing? ⇒ B ............... •. .... .. .. .. ... .. . .. .. c(t) ... ... . . .. .. .. ... .. .. . .. .......... . . . . . . . ........... ... . A............. .. .• . ....... ... ....... .................. . ............. ............. ... ............. ............. ......... .. .......... Figure 6.15 Car and airplane. 23. Referring again to example 6.18, suppose that instead of car B an airplane is flying at speed 200 km/hr to the east of P at an altitude of 2 km, and that it is gaining altitude at 10 km/hr. How fast is the distance between car and airplane changing? ⇒ 24. A light shines from the top of a pole 20 m high. An object is dropped from the same height from a point 10 m away, so that its height at time t seconds is h(t) = 20 − 9.8t2 /2. How fast is the object’s shadow moving on the ground one second later? ⇒ 25. The two blades of a pair of scissors are fastened at the point A. Let a denote the distance from A to the tip of the blade (the point B). Let β denote the angle at the tip of the blade that is formed by the line AB and the bottom edge of the blade, line BC, and let θ denote the angle between AB and the horizontal. Suppose that a piece of paper is cut in such a 6.3 Newton’s Method 127 way that the center of the scissors at A is fixed, and the paper is also fixed. As the blades are closed (i.e., the angle θ in the diagram is decreased), the distance x between A and C increases, cutting the paper. a. Express x in terms of a, θ, and β. b. Express dx/dt in terms of a, θ, β, and dθ/dt. c. Suppose that the distance a is 20 cm, and the angle β is 5◦ . Further suppose that θ is decreasing at 50 deg/sec. At the instant when θ = 30◦ , find the rate (in cm/sec) at which the paper is being cut. ⇒ . ... ......... ... ... ............. . . ... ... ........... .. . ..... .................... .. . . . . . ... ... . ... ...... ..... ...... ...... ..... ...... ...... .... .. ... ... ...... ......... . . . . . . ... . . . . .. ... ..... .. ..... ... ... ... ... ..... ...... ......... . . . . . . . ... ... ... . . ... .. ... . . . . . .. . ... ... . . . . .. . . . .. . . . . . . . . . . . . . . .. . . . . . .. ... . . . . .. ... ... ... ....... .. . . . . . . . .. . . . .. .. . . .. . . . ..... . . . . . . ... . . ... . . . . .... .. .. .. ... .. ... .. ..... ...... ... .... ...... ....... . . . . . ... . . . . . . . ...... ... ... .. . . . ... . . . . . . . . . .. . . . ... . .... .... .. . . . . . . . . . . . .... . ... .. . . . .. . . . .. .... ...... ... . . . . ... . . . ... ... ... . . . ... ..... . . . . ... ..... .... . ..... ...... θ . ... ... . . C . . . . . . ................................................... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ..... . . . . . . . . . ... A .... .. . . . . . . ... .... ... ... ... .... ..... ... ... ... ... .. .... . .... ...... ... . . . . . . . . ... .... ... . ... ... .... ... . ... ....... . .. ...... . . . . . . .. . . .... ... . .. .... ..... .. . . . . . .. . . .... ... . . . . . . . . . . . . . . . . . . . ... .... .. ..... .. . . . . . . . .. . . . . . . .... .... . . ... ... ... ... . . . . . . . ... . ..... .... ... . ... .... . .... ... .. ... . ... . . . . . . . . .. .... .... ... ... ... . . . . . . . .. . . . ..... . ... ... ... . . . . . . . ...... .... ... .. . . ... ... .... .... ... . ... ... .... ... . ... ... .... .... .... ... ... ... .. .... ... ... ... . ... ... . . . . . . .. ... ... ........ ... . ... ........... . Figure 6.16 B Scissors. 6.3 Newton's Method Suppose you have a function f (x), and you want to find as accurately as possible where it crosses the x-axis; in other words, you want to solve f (x) = 0. Suppose you know of no way to find an exact solution by any algebraic procedure, but you are able to use an approximation, provided it can be made quite close to the true value. Newton’s method is a way to find a solution to the equation to as many decimal places as you want. It is what is called an “iterative procedure,” meaning that it can be repeated again and again to get an answer of greater and greater accuracy. Iterative procedures like Newton’s method are well suited to programming for a computer. Newton’s method uses the fact that the tangent line to a curve is a good approximation to the curve near the point of tangency. √ √ EXAMPLE 6.19 Approximate 3. Since 3 is a solution to x2 = 3 or x2 − 3 = 0, we use f (x) = x2 − 3. We start by guessing something reasonably close to the true value; this 128 Chapter 6 Applications of the Derivative √ is usually easy to do. Let’s use 3 ≈ 2. Now use the tangent line to the curve when x = 2 as an approximation to the curve, as shown in figure 6.17. Since f 0 (x) = 2x, the slope of this tangent line is 4 and its equation is y = 4x − 7. The tangent line is quite close to f (x), √ so it crosses the x-axis near the point at which f (x) crosses, that is, near 3. It is easy to find where the tangent line crosses the x-axis: solve 0 = 4x − 7 to get x = 7/4 = 1.75. This is certainly a better approximation than 2, but let us say not close enough. We can improve it by doing the same thing again: find the tangent line at x = 1.75, find where this new tangent line crosses the x-axis, and use that value as a better approximation. We can continue this indefinitely, though it gets a bit tedious. Lets see if we can shortcut the process. Suppose the best approximation to the intercept we have so far is xi . To find a better approximation we will always do the same thing: find the slope of the tangent line at xi , find the equation of the tangent line, find the x-intercept. The slope will be 2xi . The tangent line will be y = (2xi )(x − xi ) + (x2i − 3), using the point-slope formula for a line. Finally, the intercept is found by solving 0 = (2xi )(x − xi ) + (x2i − 3). With a little algebra this turns into x = (x2i + 3)/(2xi ); this is the next approximation, which we naturally call xi+1 . Instead of doing the whole tangent line computation every time we can simply use this formula to get as many approximations as we want. Starting with x0 = 2, we get x1 = (x20 + 3)/(2x0 ) = (22 + 3)/4 = 7/4 (the same approximation we got above, of course), x2 = (x21 + 3)/(2x1 ) = ((7/4)2 + 3)/(7/2) = 97/56 ≈ 1.73214, x3 ≈ 1.73205, and so on. This is still a bit tedious by hand, but with a calculator or, even better, a good computer program, it is quite easy to get many, many approximations. We might guess already that 1.73205 is accurate to two decimal places, and in fact it turns out that it is accurate to 5 places. Let’s think about this process in more general terms. We want to approximate a solution to f (x) = 0. We start with a rough guess, which we call x0 . We use the tangent line to f (x) to get a new approximation that we hope will be closer to the true value. What is the equation of the tangent line when x = x0 ? The slope is f 0 (x0 ) and the line goes through (x0 , f (x0 )), so the equation of the line is y = f 0 (x0 )(x − x0 ) + f (x0 ). Now we find where this crosses the x-axis by substituting y = 0 and solving for x: x= x0 f 0 (x0 ) − f (x0 ) f (x0 ) = x − . 0 f 0 (x0 ) f 0 (x0 ) We will typically want to compute more than one of these improved approximations, so we number them consecutively; from x0 we have computed x1 : x1 = f (x0 ) x0 f 0 (x0 ) − f (x0 ) = x0 − 0 , 0 f (x0 ) f (x0 ) 6.3 Newton’s Method 129 .. ... ... ... . . ... .... ... ... ... ..... . . .. .. ... ... ... ... ... .... . .. .. ... ... ...... ........ . . ...... ...... ........ . . .... ..... ... ... . . . ... .. ... .. . . ... .... ...... ....... . . . ...... 1 ................ 2 ....... . . . . .. ... . . . . .... .. ..... ... ...... .. ...... ..... . . . . . . .. ......... .................. ... Figure 6.17 Newton’s method. and in general from xi we compute xi+1 : xi+1 = xi f 0 (xi ) − f (xi ) f (xi ) = xi − 0 . 0 f (xi ) f (xi ) EXAMPLE 6.20 Returning to the previous example, f (x) = x2 − 3, f 0 (x) = 2x, and the formula becomes xi+1 = xi − (x2i − 3)/(2xi ) = (x2i + 3)/(2xi ), as before. In practice, which is to say, if you need to approximate a value in the course of designing a bridge or a building or an airframe, you will need to have some confidence that the approximation you settle on is accurate enough. As a rule of thumb, once a certain number of decimal places stop changing from one approximation to the next it is likely that those decimal places are correct. Still, this may not be enough assurance, in which case we can test the result for accuracy. EXAMPLE 6.21 Find the x coordinate of the intersection of the curves y = 2x and y = tan x, accurate to three decimal places. To put this in the context of Newton’s method, we note that we want to know where 2x = tan x or tan x − 2x = 0. We compute f 0 (x) = sec2 x − 2 and set up the formula: xi+1 = xi − tan xi − 2xi . sec2 xi − 2 From the graph in figure 6.18 we guess x0 = 1 as a starting point, then using the formula we compute x1 = 1.310478030, x2 = 1.223929096, x3 = 1.176050900, x4 = 1.165926508, 130 Chapter 6 Applications of the Derivative x5 = 1.165561636. So we guess that the first three places are correct, but that is not the same as saying 1.165 is correct to three decimal places—1.166 might be the correct, rounded approximation. How can we tell? We can substitute 1.165, 1.1655 and 1.166 into the original function tan x − 2x; this gives −0.002483652, −0.000271247, 0.001948654. Since the first two are negative and the third is positive, tan x − 2x crosses the x axis between 1.1655 and 1.166, so the correct value to three places is 1.166. 15 10 5 0 ... ... .. .... . .. .. .... . .. ... ... . ... .. .... . .. ... ... . ... .. .... . .. ... . . ... ... ... . . .. ... .. ... . . ... .... ..... ..... . . . .............. . ................... ..... .......................... ......................... . . . . . . . . . . . . . . . . . . ..... ........... . .................... ................... ....................... ................... ............... ................... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ................................................................... ................... ... ..................................................... ........................................... 1 Figure 6.18 1.5 y = tan x and y = 2x. Exercises 1. Approximate the fifth root of 7, using x0 = 1.5 as a first guess. Use Newton’s method to find x3 as your approximation. ⇒ 2. Use Newton’s Method to approximate the cube root of 10 to two decimal places. ⇒ 3. The function f (x) = x3 − 3x2 − 3x + 6 has a root between 3 and 4, because f (3) = −3 and f (4) = 10. Approximate the root to two decimal places. ⇒ 4. A rectangular piece of cardboard of dimensions 8 × 17 is used to make an open-top box by cutting out a small square of side x from each corner and bending up the sides. (See exercise 6.1.20.) If x = 2, then the volume of the box is 2 · 4 · 13 = 104. Use Newton’s method to find a value of x for which the box has volume 100, accurate to 3 significant figures. ⇒ 6.4 Linear Approximations 131 6.4 Linear Approximations Newton’s method is one example of the usefulness of the tangent line as an approximation to a curve. Here we explore another such application. Recall that the tangent line to f (x) at a point x = a is given by L(x) = f 0 (a)(x − a) + f (a). The tangent line in this context is also called the linear approximation to f at a. If f is differentiable at a then L is a good approximation of f so long as x is “not too far” from a. Put another way, if f is differentiable at a then under a microscope f will look very much like a straight line. Figure 6.19 shows a tangent line to y = x2 at three different magnifications. If we want to approximate f (b), because computing it exactly is difficult, we can approximate the value using a linear approximation, provided that we can compute the tangent line at some a close to b. Figure 6.19 The linear approximation to y = x2 . √ √ EXAMPLE 6.22 Let f (x) = x + 4. Then f 0 (x) = 1/(2 x + 4). The linear ap√ √ proximation to f at x = 5 is L(x) = 1/(2 5 + 4)(x − 5) + 5 + 4 = (x − 5)/6 + 3. As an immediate application we can approximate square roots of numbers near 9 by hand. √ To estimate 10, we substitute 6 into the linear approximation instead of into f (x), so √ 6 + 4 ≈ (6 − 5)/6 + 3 = 19/6 ≈ 3.16. This rounds to 3.17 while the square root of 10 is actually 3.16 to two decimal places, so this estimate is only accurate to one decimal place. This is not too surprising, as 10 is really not very close to 9; on the other hand, for many calculations, 3.2 would be accurate enough. 132 Chapter 6 Applications of the Derivative With modern calculators and computing software it may not appear necessary to use linear approximations. But in fact they are quite useful. In cases requiring an explicit numerical approximation, they allow us to get a quick rough estimate which can be used as a “reality check” on a more complex calculation. In some complex calculations involving functions, the linear approximation makes an otherwise intractable calculation possible, without serious loss of accuracy. EXAMPLE 6.23 Consider the trigonometric function sin x. Its linear approximation at x = 0 is simply L(x) = x. When x is small this is quite a good approximation and is used frequently by engineers and scientists to simplify some calculations. DEFINITION 6.24 Let y = f (x) be a differentiable function. We define a new independent variable dx, and a new dependent variable dy = f 0 (x) dx. Notice that dy is a function both of x (since f 0 (x) is a function of x) and of dx. We say that dx and dy are differentials. Let ∆x = x − a and ∆y = f (x) − f (a). If x is near a then ∆x is small. If we set dx = ∆x then ∆y dy = f 0 (a) dx ≈ ∆x = ∆y. ∆x Thus, dy can be used to approximate ∆y, the actual change in the function f between a and x. This is exactly the approximation given by the tangent line: dy = f 0 (a)(x − a) = f 0 (a)(x − a) + f (a) − f (a) = L(x) − f (a). While L(x) approximates f (x), dy approximates how f (x) has changed from f (a). Figure 6.20 illustrates the relationships. ...... ........ ............. ....... ... ...............↑ . . ....... | ....... ......................... . . . . . . .. .. ... ↑ | ....... ........... ....... .......... .... ................. .. ∆y dy ..................... . . . . . . . . . ... .... .......... | .... ....... ↓| ↓ ........ ............................................................................................... . . . . . .. . . . . . .. ←−− dx = ∆x −−→ ...... a Figure 6.20 x Differentials. 6.5 The Mean Value Theorem 133 Exercises 1. Let f (x) = x4 . If a = 1 and dx = ∆x = 1/2, what are ∆y and dy? ⇒ √ 2. Let f (x) = x. If a = 1 and dx = ∆x = 1/10, what are ∆y and dy? ⇒ 3. Let f (x) = sin(2x). If a = π and dx = ∆x = π/100, what are ∆y and dy? 4. Use differentials to estimate the amount of paint needed to apply a coat of paint 0.02 cm thick to a sphere with diameter 40 meters. (Recall that the volume of a sphere of radius r is V = (4/3)πr3 . Notice that you are given that dr = 0.02.) ⇒ 5. Show in detail that the linear approximation of sin x at x = 0 is L(x) = x and the linear approximation of cos x at x = 0 is L(x) = 1. 6.5 The Mean Value Theorem Here are two interesting questions involving derivatives: 1. Suppose two different functions have the same derivative; what can you say about the relationship between the two functions? 2. Suppose you drive a car from toll booth on a toll road to another toll booth at an average speed of 70 miles per hour. What can be concluded about your actual speed during the trip? In particular, did you exceed the 65 mile per hour speed limit? While these sound very different, it turns out that the two problems are very closely related. We know that “speed” is really the derivative by a different name; let’s start by translating the second question into something that may be easier to visualize. Suppose that the function f (t) gives the position of your car on the toll road at time t. Your change in position between one toll booth and the next is given by f (t1 ) − f (t0 ), assuming that at time t0 you were at the first booth and at time t1 you arrived at the second booth. Your average speed for the trip is (f (t1 ) − f (t0 ))/(t1 − t0 ). If we think about the graph of f (t), the average speed is the slope of the line that connects the two points (t0 , f (t0 )) and (t1 , f (t1 )). Your speed at any particular time t between t0 and t1 is f 0 (t), the slope of the curve. Now question (2) becomes a question about slope. In particular, if the slope between endpoints is 70, what can be said of the slopes at points between the endpoints? As a general rule, when faced with a new problem it is often a good idea to examine one or more simplified versions of the problem, in the hope that this will lead to an understanding of the original problem. In this case, the problem in its “slope” form is somewhat easier to simplify than the original, but equivalent, problem. Here is a special instance of the problem. Suppose that f (t0 ) = f (t1 ). Then the two endpoints have the same height and the slope of the line connecting the endpoints is zero. What can we say about the slope between the endpoints? It shouldn’t take 134 Chapter 6 Applications of the Derivative much experimentation before you are convinced of the truth of this statement: Somewhere between t0 and t1 the slope is exactly zero, that is, somewhere between t0 and t1 the slope is equal to the slope of the line between the endpoints. This suggests that perhaps the same is true even if the endpoints are at different heights, and again a bit of experimentation will probably convince you that this is so. But we can do better than “experimentation”—we can prove that this is so. We start with the simplified version: THEOREM 6.25 Rolle’s Theorem Suppose that f (x) has a derivative on the interval (a, b), is continuous on the interval [a, b], and f (a) = f (b). Then at some value c ∈ (a, b), f 0 (c) = 0. Proof. We know that f (x) has a maximum and minimum value on [a, b] (because it is continuous), and we also know that the maximum and minimum must occur at an endpoint, at a point at which the derivative is zero, or at a point where the derivative is undefined. Since the derivative is never undefined, that possibility is removed. If the maximum or minimum occurs at a point c, other than an endpoint, where f 0 (c) = 0, then we have found the point we seek. Otherwise, the maximum and minimum both occur at an endpoint, and since the endpoints have the same height, the maximum and minimum are the same. This means that f (x) = f (a) = f (b) at every x ∈ [a, b], so the function is a horizontal line, and it has derivative zero everywhere in (a, b). Then we may choose any c at all to get f 0 (c) = 0. Perhaps remarkably, this special case is all we need to prove the more general one as well. THEOREM 6.26 Mean Value Theorem Suppose that f (x) has a derivative on the interval (a, b) and is continuous on the interval [a, b]. Then at some value c ∈ (a, b), f (b) − f (a) f 0 (c) = . b−a f (b) − f (a) , and consider a new function g(x) = f (x)−m(x−a)−f (a). b−a We know that g(x) has a derivative everywhere, since g 0 (x) = f 0 (x) − m. We can compute g(a) = f (a) − m(a − a) − f (a) = 0 and Proof. Let m = g(b) = f (b) − m(b − a) − f (a) = f (b) − f (b) − f (a) (b − a) − f (a) b−a = f (b) − (f (b) − f (a)) − f (a) = 0. 6.5 The Mean Value Theorem 135 So the height of g(x) is the same at both endpoints. This means, by Rolle’s Theorem, that at some c, g 0 (c) = 0. But we know that g 0 (c) = f 0 (c) − m, so 0 = f 0 (c) − m = f 0 (c) − f (b) − f (a) , b−a which turns into f 0 (c) = f (b) − f (a) , b−a exactly what we want. Returning to the original formulation of question (2), we see that if f (t) gives the position of your car at time t, then the Mean Value Theorem says that at some time c, f 0 (c) = 70, that is, at some time you must have been traveling at exactly your average speed for the trip, and that indeed you exceeded the speed limit. Now let’s return to question (1). Suppose, for example, that two functions are known to have derivative equal to 5 everywhere, f 0 (x) = g 0 (x) = 5. It is easy to find such functions: 5x, 5x + 47, 5x − 132, etc. Are there other, more complicated, examples? No—the only functions that work are the “obvious” ones, namely, 5x plus some constant. How can we see that this is true? Although “5” is a very simple derivative, let’s look at an even simpler one. Suppose that f 0 (x) = g 0 (x) = 0. Again we can find examples: f (x) = 0, f (x) = 47, f (x) = −511 all have f 0 (x) = 0. Are there non-constant functions f with derivative 0? No, and here’s why: Suppose that f (x) is not a constant function. This means that there are two points on the function with different heights, say f (a) 6= f (b). The Mean Value Theorem tells us that at some point c, f 0 (c) = (f (b) − f (a))/(b − a) 6= 0. So any non-constant function does not have a derivative that is zero everywhere; this is the same as saying that the only functions with zero derivative are the constant functions. Let’s go back to the slightly less easy example: suppose that f 0 (x) = g 0 (x) = 5. Then (f (x) − g(x))0 = f 0 (x) − g 0 (x) = 5 − 5 = 0. So using what we discovered in the previous paragraph, we know that f (x) − g(x) = k, for some constant k. So any two functions with derivative 5 must differ by a constant; since 5x is known to work, the only other examples must look like 5x + k. Now we can extend this to more complicated functions, without any extra work. Suppose that f 0 (x) = g 0 (x). Then as before (f (x) − g(x))0 = f 0 (x) − g 0 (x) = 0, so f (x) − g(x) = k. Again this means that if we find just a single function g(x) with a certain derivative, then every other function with the same derivative must be of the form g(x)+k. 136 Chapter 6 Applications of the Derivative EXAMPLE 6.27 Describe all functions that have derivative 5x − 3. It’s easy to find one: g(x) = (5/2)x2 − 3x has g 0 (x) = 5x − 3. The only other functions with the same derivative are therefore of the form f (x) = (5/2)x2 − 3x + k. Alternately, though not obviously, you might have first noticed that g(x) = (5/2)x2 − 3x + 47 has g 0 (x) = 5x − 3. Then every other function with the same derivative must have the form f (x) = (5/2)x2 − 3x + 47 + k. This looks different, but it really isn’t. The functions of the form f (x) = (5/2)x2 − 3x + k are exactly the same as the ones of the form f (x) = (5/2)x2 − 3x + 47 + k. For example, (5/2)x2 − 3x + 10 is the same as (5/2)x2 − 3x + 47 + (−37), and the first is of the first “form” while the second has the second form. This is worth calling a theorem: THEOREM 6.28 If f 0 (x) = g 0 (x) for every x ∈ (a, b), then for some constant k, f (x) = g(x) + k on the interval (a, b). EXAMPLE 6.29 Describe all functions with derivative sin x + ex . One such function is − cos x + ex , so all such functions have the form − cos x + ex + k. Exercises 1. Let f (x) = x2 . Find a value c ∈ (−1, 2) so that f 0 (c) equals the slope between the endpoints of f (x) on [−1, 2]. ⇒ 2. Verify that f (x) = x/(x + 2) satisfies the hypotheses of the Mean Value Theorem on the interval [1, 4] and then find all of the values, c, that satisfy the conclusion of the theorem. ⇒ 3. Verify that f (x) = 3x/(x + 7) satisfies the hypotheses of the Mean Value Theorem on the interval [−2, 6] and then find all of the values, c, that satisfy the conclusion of the theorem. 4. Let f (x) = tan x. Show that f (π) = f (2π) = 0 but there is no number c ∈ (0, 2π) such that f 0 (c) = 0. Why does this not contradict Rolle’s theorem? 5. Let f (x) = (x − 3)−2 . Show that there is no value c in the interval (1, 4) such that f 0 (c) = (f (4) − f (1))/(4 − 1). Why is this not a contradiction of the Mean Value Theorem? 6. Describe all functions with derivative x2 + 47x − 5. ⇒ 1 7. Describe all functions with derivative . ⇒ 1 + x2 1 8. Describe all functions with derivative x3 − . ⇒ x 9. Describe all functions with derivative sin(2x). ⇒ 10. Show that the equation 6x4 − 7x + 1 = 0 does not have more than two distinct real roots. 6.5 The Mean Value Theorem 137 11. Let f be differentiable on R. Suppose that f 0 (x) 6= 0 for every x. Prove that f has at most one root. 12. Prove that for all real x and y | cos x − cos y| ≤ |x − y|. State and prove an analogous result involving sine. √ 13. Show that 1 + x > 1 + (x/2) if −1 < x < 1. 7 Integration 7.1 Two examples Up to now we have been concerned with extracting information about how a function changes from the function itself. Given knowledge about an object’s position, for example, we want to know the object’s speed. Given information about the height of a curve we want to know its slope. We now consider problems that are, whether obviously or not, the reverse of such problems. EXAMPLE 7.1 An object moves in a straight line so that its speed at time t is given by v(t) = 3t in, say, cm/sec. If the object is at position 10 on the straight line when t = 0, where is the object at any time t? There are two reasonable ways to approach this problem. If s(t) is the position of the object at time t, we know that s0 (t) = v(t). Because of our knowledge of derivatives, we know therefore that s(t) = 3t2 /2+ k, and because s(0) = 10 we easily discover that k = 10, so s(t) = 3t2 /2 + 10. For example, at t = 1 the object is at position 3/2 + 10 = 11.5. This is certainly the easiest way to deal with this problem. Not all similar problems are so easy, as we will see; the second approach to the problem is more difficult but also more general. We start by considering how we might approximate a solution. We know that at t = 0 the object is at position 10. How might we approximate its position at, say, t = 1? We know that the speed of the object at time t = 0 is 0; if its speed were constant then in the first second the object would not move and its position would still be 10 when t = 1. In fact, the object will not be too far from 10 at t = 1, but certainly we can do better. Let’s 139 140 Chapter 7 Integration look at the times 0.1, 0.2, 0.3, . . . , 1.0, and try approximating the location of the object at each, by supposing that during each tenth of a second the object is going at a constant speed. Since the object initially has speed 0, we again suppose it maintains this speed, but only for a tenth of second; during that time the object would not move. During the tenth of a second from t = 0.1 to t = 0.2, we suppose that the object is traveling at 0.3 cm/sec, namely, its actual speed at t = 0.1. In this case the object would travel (0.3)(0.1) = 0.03 centimeters: 0.3 cm/sec times 0.1 seconds. Similarly, between t = 0.2 and t = 0.3 the object would travel (0.6)(0.1) = 0.06 centimeters. Continuing, we get as an approximation that the object travels (0.0)(0.1) + (0.3)(0.1) + (0.6)(0.1) + · · · + (2.7)(0.1) = 1.35 centimeters, ending up at position 11.35. This is a better approximation than 10, certainly, but is still just an approximation. (We know in fact that the object ends up at position 11.5, because we’ve already done the problem using the first approach.) Presumably, we will get a better approximation if we divide the time into one hundred intervals of a hundredth of a second each, and repeat the process: (0.0)(0.01) + (0.03)(0.01) + (0.06)(0.01) + · · · + (2.97)(0.01) = 1.485. We thus approximate the position as 11.485. Since we know the exact answer, we can see that this is much closer, but if we did not already know the answer, we wouldn’t really know how close. We can keep this up, but we’ll never really know the exact answer if we simply compute more and more examples. Let’s instead look at a “typical” approximation. Suppose we divide the time into n equal intervals, and imagine that on each of these the object travels at a constant speed. Over the first time interval we approximate the distance traveled as (0.0)(1/n) = 0, as before. During the second time interval, from t = 1/n to t = 2/n, the object travels approximately 3(1/n)(1/n) = 3/n2 centimeters. During time interval number i, the object travels approximately (3(i − 1)/n)(1/n) = 3(i − 1)/n2 centimeters. Adding these up as before, we approximate the distance traveled as (0) 1 1 1 1 1 + 3 2 + 3(2) 2 + 3(3) 2 + · · · + 3(n − 1) 2 n n n n n centimeters. What can we say about this? At first it looks rather less useful than the concrete calculations we’ve already done. But in fact a bit of algebra reveals it to be much more useful. We can factor out a 3 and 1/n2 to get 3 (0 + 1 + 2 + 3 + · · · + (n − 1)), n2 7.1 Two examples 141 that is, 3/n2 times the sum of the first n − 1 positive integers. Now we make use of a fact you may have run across before: 1 + 2 + 3 + ··· + k = k(k + 1) . 2 In our case we’re interested in k = n − 1, so 1 + 2 + 3 + · · · + (n − 1) = (n − 1)(n) n2 − n = . 2 2 This simplifies the approximate distance traveled to 3 n2 − n 3 n2 − n 3 = = n2 2 2 n2 2 µ n2 n − n2 n2 ¶ 3 = 2 µ ¶ 1 1− . n Now this is quite easy to understand: as n gets larger and larger this approximation gets closer and closer to (3/2)(1 − 0) = 3/2, so that 3/2 is the exact distance traveled during one second, and the final position is 11.5. So for t = 1, at least, this rather cumbersome approach gives the same answer as the first approach. But really there’s nothing special about t = 1; let’s just call it t instead. In this case the approximate distance traveled during time interval number i is 3(i − 1)(t/n)(t/n) = 3(i − 1)t2 /n2 and the total distance traveled is approximately (0) t t2 t2 t2 t2 + 3(1) 2 + 3(2) 2 + 3(3) 2 + · · · + 3(n − 1) 2 . n n n n n As before we can simplify this to µ ¶ 3t2 3t2 n2 − n 3 2 1 (0 + 1 + 2 + · · · + (n − 1)) = 2 = t 1− . n2 n 2 2 n In the limit, as n gets larger, this gets closer and closer to (3/2)t2 and the approximated position of the object gets closer and closer to (3/2)t2 + 10, so the actual position is (3/2)t2 + 10, exactly the answer given by the first approach to the problem. EXAMPLE 7.2 Find the area under the curve y = 3x between x = 0 and any positive value x. There is here no obvious analogue to the first approach in the previous example, but the second approach works fine. (Because the function y = 3x is so simple, there is another approach that works here, but it is even more limited in potential application than is approach number one.) How might we approximate the desired area? We know 142 Chapter 7 Integration how to compute areas of rectangles, so we approximate the area by rectangles. Jumping straight to the general case, suppose we divide the interval between 0 and x into n equal subintervals, and use a rectangle above each subinterval to approximate the area under the curve. There are many ways we might do this, but let’s use the height of the curve at the left endpoint of the subinterval as the height of the rectangle, as in figure 7.1. The height of rectangle number i is then 3(i − 1)(x/n), the width is x/n, and the area is 3(i − 1)(x2 /n2 ). The total area of the rectangles is (0) x x2 x2 x2 x2 + 3(1) 2 + 3(2) 2 + 3(3) 2 + · · · + 3(n − 1) 2 . n n n n n By factoring out 3x2 /n2 this simplifies to µ ¶ 1 3x2 3x2 n2 − n 3 2 (0 + 1 + 2 + · · · + (n − 1)) = 2 = x 1− . n2 n 2 2 n As n gets larger this gets closer and closer to 3x2 /2, which must therefore be the true area under the curve. .... .... .... .... . . . .... .... .... ..... . . . ..... .... .... .... . . . .... .... .... ..... . . . ..... .... .... .... . . . .... .... ..... .... . . . ..... .... .... .... . . . .... .... ..... .... . . . ..... .... .... .... . . . .... ..... .... .... . . . ..... ..... ..... ... Figure 7.1 Approximating the area under y = 3t with rectangles. What you will have noticed, of course, is that while the problem in the second example appears to be much different than the problem in the first example, and while the easy approach to problem one does not appear to apply to problem two, the “approximation” approach works in both, and moreover the calculations are identical. As we will see, there are many, many problems that appear much different on the surface but that turn out to 7.2 The Fundamental Theorem of Calculus 143 be the same as these problems, in the sense that when we try to approximate solutions we end up with mathematics that looks like the two examples, though of course the function involved will not always be so simple. Even better, we now see that while the second problem did not appear to be amenable to approach one, it can in fact be solved in the same way. The reasoning is this: we know that problem one can be solved easily by finding a function whose derivative is 3t. We also know that mathematically the two problems are the same, because both can be solved by taking a limit of a sum, and the sums are identical. Therefore, we don’t really need to compute the limit of either sum because we know that we will get the same answer by computing a function with the derivative 3t or, which is the same thing, 3x. It’s true that the first problem had the added complication of the “10”, and we certainly need to be able to deal with such minor variations, but that turns out to be quite simple. The lesson then is this: whenever we can solve a problem by taking the limit of a sum of a certain form, we can instead of computing the (often nasty) limit find a new function with a certain derivative. Exercises 1. Suppose an object moves in a straight line so that its speed at time t is given by v(t) = 2t + 2, and that at t = 1 the object is at position 5. Find the position of the object at t = 2. ⇒ 2. Suppose an object moves in a straight line so that its speed at time t is given by v(t) = t2 + 2, and that at t = 0 the object is at position 5. Find the position of the object at t = 2. ⇒ 3. By a method similar to that in example 7.2, find the area under y = 2x between x = 0 and any positive value for x. ⇒ 4. By a method similar to that in example 7.2, find the area under y = 4x between x = 0 and any positive value for x. ⇒ 5. By a method similar to that in example 7.2, find the area under y = 4x between x = 2 and any positive value for x bigger than 2. ⇒ 6. By a method similar to that in example 7.2, find the area under y = 4x between any two positive values for x, say a < b. ⇒ 7. Let f (x) = x2 + 3x + 2. Approximate the area under the curve between x = 0 and x = 2 using 4 rectangles and also using 8 rectangles. ⇒ 8. Let f (x) = x2 − 2x + 3. Approximate the area under the curve between x = 1 and x = 3 using 4 rectangles. ⇒ 7.2 The Fundamental Theorem of Calculus Let’s recast the first example from the previous section. Suppose that the speed of the object is 3t at time t. How far does the object travel between time t = a and time t = b? We are no longer assuming that we know where the object is at time t = 0 or at any other 144 Chapter 7 Integration time. It is certainly true that it is somewhere, so let’s suppose that at t = 0 the position is k. Then just as in the example, we know that the position of the object at any time is 3t2 /2+k. This means that at time t = a the position is 3a2 /2 + k and at time t = b the position is 3b2 /2 + k. Therefore the change in position is 3b2 /2 + k − (3a2 /2 + k) = 3b2 /2 − 3a2 /2. Notice that the k drops out; this means that it doesn’t matter that we don’t know k, it doesn’t even matter if we use the wrong k, we get the correct answer. In other words, to find the change in position between time a and time b we can use any antiderivative of the speed function 3t—it need not be the one antiderivative that actually gives the location of the object. What about the second approach to this problem, in the new form? We now want to approximate the change in position between time a and time b. We take the interval of time between a and b, divide it into n subintervals, and approximate the distance traveled during each. The starting time of subinterval number i is now a + (i − 1)(b − a)/n, which we abbreviate as ti−1 , so that t0 = a, t1 = a + (b − a)/n, and so on. The speed of the object is f (t) = 3t, and each subinterval is (b − a)/n = ∆t seconds long. The distance traveled during subinterval number i is approximately f (ti−1 )∆t, and the total change in distance is approximately f (t0 )∆t + f (t1 )∆t + · · · + f (tn−1 )∆t. The exact change in position is the limit of this sum as n goes to infinity. We abbreviate this sum using sigma notation: n−1 X f (ti )∆t = f (t0 )∆t + f (t1 )∆t + · · · + f (tn−1 )∆t. i=0 The notation on the left side of the equal sign uses a large capital sigma, a Greek letter, and the left side is an abbreviation for the right side. The answer we seek is lim n→∞ n−1 X f (ti )∆t. i=0 Since this must be the same as the answer we have already obtained, we know that lim n→∞ n−1 X i=0 f (ti )∆t = 3b2 3a2 − . 2 2 The significance of 3t2 /2, into which we substitute t = b and t = a, is of course that it is a function whose derivative is f (t). As we have discussed, by the time we know that we 7.2 The Fundamental Theorem of Calculus 145 want to compute lim n−1 X n→∞ f (ti )∆t, i=0 it no longer matters what f (t) stands for—it could be a speed, or the height of a curve, or something else entirely. We know that the limit can be computed by finding any function with derivative f (t), substituting a and b, and subtracting. We summarize this in a theorem. First, we introduce some new notation and terms. We write Z b n−1 X f (t) dt = lim f (ti )∆t n→∞ a i=0 if the limit exists. That is, the left hand side means, or is an abbreviation for, the right R hand side. The symbol is called an integral sign, and the whole expression is read as “the integral of f (t) from a to b.” What we have learned is that this integral can be computed by finding a function, say F (t), with the property that F 0 (t) = f (t), and then computing F (b) − F (a). The function F (t) is called an antiderivative of f (t). Now the theorem: THEOREM 7.3 Fundamental Theorem of Calculus Suppose that f (x) is continuous on the interval [a, b]. If F (x) is any antiderivative of f (x), then Z b f (x) dx = F (b) − F (a). a Let’s rewrite this slightly: Z x f (t) dt = F (x) − F (a). a We’ve replaced the variable x by t and b by x. These are just different names for quantities, so the substitution doesn’t change the meaning. It does make it easier to think of the two sides of the equation as functions. The expression Z x f (t) dt a is a function: plug in a value for x, get out some other value. The expression F (x) − F (a) is of course also a function, and it has a nice property: d (F (x) − F (a)) = F 0 (x) = f (x), dx 146 Chapter 7 Integration since F (a) is a constant and has derivative zero. In other words, by shifting our point of view slightly, we see that the odd looking function Z x G(x) = f (t) dt a has a derivative, and that in fact G0 (x) = f (x). This is really just a restatement of the Fundamental Theorem of Calculus, and indeed is often called the Fundamental Theorem of Calculus. To avoid confusion, some people call the two versions of the theorem “The Fundamental Theorem of Calculus, part I” and “The Fundamental Theorem of Calculus, part II”, although unfortunately there is no universal agreement as to which is part I and which part II. Since it really is the same theorem, differently stated, some people simply call them both “The Fundamental Theorem of Calculus.” THEOREM 7.4 Fundamental Theorem of Calculus tinuous on the interval [a, b] and let Z Suppose that f (x) is con- x G(x) = f (t) dt. a Then G0 (x) = f (x). We have not really proved the Fundamental Theorem. In a nutshell, we gave the following argument to justify it: Suppose we want to know the value of Z b f (t) dt = lim a n→∞ n−1 X f (ti )∆t. i=0 We can interpret the right hand side as the distance traveled by an object whose speed is given by f (t). We know another way to compute the answer to such a problem: find the position of the object by finding an antiderivative of f (t), then substitute t = a and t = b and subtract to find the distance traveled. This must be the answer to the original problem as well, even if f (t) does not represent a speed. What’s wrong with this? In some sense, nothing. As a practical matter it is a very convincing arguement, because our understanding of the relationship between speed and distance seems to be quite solid. From the point of view of mathematics, however, it is unsatisfactory to justify a purely mathematical relationship by appealing to our understanding of the physical universe, which could, however unlikely it is in this case, be wrong. 7.2 The Fundamental Theorem of Calculus 147 A complete proof is a bit too involved to include here, but we will indicate how it goes. First, if we can prove the second version of the Fundamental Theorem, theorem 7.4, then we can prove the first version from that: Proof of Theorem 7.3. We know from theorem 7.4 that Z x G(x) = f (t) dt a is an antiderivative of f (x), and therefore any antiderivative F (x) of f (x) is of the form F (x) = G(x) + k. Then F (b) − F (a) = G(b) + k − (G(a) + k) = G(b) − G(a) Z Z b f (t) dt − = a It is not hard to see that Ra a a f (t) dt. a f (t) dt = 0, so this means that Z b F (b) − F (a) = f (t) dt, a which is exactly what theorem 7.3 says. So the real job is to prove theorem 7.4. We will sketch the proof, using some facts that we do not prove. First, the following identity is true of integrals: Z Z b f (t) dt = a Z c f (t) dt + a b f (t) dt. c This can be proved directly from the definition of the integral, that is, using the limits of sums. It is quite easy to see that it must be true by thinking of either of the two applications of integrals that we have seen. It turns out that the identity is true no matter what c is, but it is easiest to think about the meaning when a ≤ c ≤ b. First, if f (t) represents a speed, then we know that the three integrals represent the distance traveled between time a and time b; the distance traveled between time a and time c; and the distance traveled between time c and time b. Clearly the sum of the latter two is equal to the first of these. Second, if f (t) represents the height of a curve, the three integrals represent the area under the curve between a and b; the area under the curve between a and c; and the area under the curve between c and b. Again it is clear from the geometry that the first is equal to the sum of the second and third. 148 Chapter 7 Integration Proof sketch for Theorem 7.4. We want to compute G0 (x), so we start with the definition of the derivative in terms of a limit: G(x + ∆x) − G(x) ∆x→0 ∆x ! ÃZ Z x x+∆x 1 = lim f (t) dt − f (t) dt ∆x→0 ∆x a a ! ÃZ Z x+∆x Z x x 1 = lim f (t) dt + f (t) dt − f (t) dt ∆x→0 ∆x a x a Z x+∆x 1 = lim f (t) dt. ∆x→0 ∆x x G0 (x) = lim Now we need to know something about Z x+∆x f (t) dt x when ∆x is small; in fact, it is very close to ∆xf (x), but we will not prove this. Once again, it is easy to believe this is true by thinking of our two applications: The integral Z x+∆x f (t) dt x can be interpreted as the distance traveled by an object over a very short interval of time. Over a sufficiently short period of time, the speed of the object will not change very much, so the distance traveled will be approximately the length of time multiplied by the speed at the beginning of the interval, namely, ∆xf (x). Alternately, the integral may be interpreted as the area under the curve between x and x + ∆x. When ∆x is very small, this will be very close to the area of the rectangle with base ∆x and height f (x); again this is ∆xf (x). If we accept this, we may proceed: 1 lim ∆x→0 ∆x Z x+∆x ∆xf (x) = f (x), ∆x→0 ∆x f (t) dt = lim x which is what we wanted to show. It is still true that we are depending on an interpretation of the integral to justify the argument, but we have isolated this part of the argument into two facts that are not too 7.2 The Fundamental Theorem of Calculus 149 hard to prove. Once the last reference to interpretation has been removed from the proofs of these facts, we will have a real proof of the Fundamental Theorem. Now we know that to solve certain kinds of problems, those that lead to a sum of a certain form, we “merely” find an antiderivative and substitute two values and subtract. Unfortunately, finding antiderivatives can be quite difficult. While there are a small number of rules that allow us to compute the derivative of any common function, there are no such rules for antiderivatives. There are some techniques that frequently prove useful, but we will never be able to reduce the problem to a completely mechanical process. Because of the close relationship between an integral and an antiderivative, the integral sign is also used to mean “antiderivative”. You can tell which is intended by whether the limits of integration are included: Z 2 x2 dx 1 is an ordinary integral, also called a definite integral, because it has a definite value, namely Z 2 13 7 23 x2 dx = − = . 3 3 3 1 We use Z x2 dx to denote the antiderivative of x2 , also called an indefinite integral. So this is evaluated as Z x3 x2 dx = + C. 3 It is customary to include the constant C to indicate that there are really an infinite number of antiderivatives. We do not need this C to compute definite integrals, but in other circumstances we will need to remember that the C is there, so it is best to get into the habit of writing the C. When we compute a definite integral, we first find an antiderivative and then substitute. It is convenient to first display the antiderivative and then do the substitution; we need a notation indicating that the substitution is yet to be done. A typical solution would look like this: Z 1 2 ¯2 x3 ¯¯ 23 13 7 x dx = − = . = ¯ 3 1 3 3 3 2 The vertical line with subscript and superscript is used to indicate the operation “substitute and subtract” that is needed to finish the evaluation. 150 Chapter 7 Integration Exercises Find the antiderivatives of the functions: √ 1. 8 x ⇒ 6. (5x + 1)2 ⇒ 2. 3t2 + 1 ⇒ √ 3. 4/ x ⇒ 7. (x − 6)2 ⇒ 8. x3/2 ⇒ 2 9. √ ⇒ x x 10. |2t − 4| ⇒ 2 4. 2/z ⇒ 5. 7s−1 ⇒ Compute the values of the integrals: Z Z 4 2 t + 3t dt ⇒ 11. Z 1 Z π 12. sin t dt ⇒ Z 13. 1 1 dx ⇒ x Z 3 2 x5 dx ⇒ 1 x 17. Find the derivative of G(x) = x3 dx ⇒ 0 16. Z ex dx ⇒ 0 15. 0 10 5 14. 2 t − 3t dt ⇒ Z 1 x2 18. Find the derivative of G(x) = Z1 x 19. Find the derivative of G(x) = Z t2 − 3t dt ⇒ 2 et dt ⇒ 1 x2 20. Find the derivative of G(x) = 2 et dt ⇒ 1 7.3 Some Properties of Integrals Suppose an object moves so that its speed, or more properly velocity, is given by v(t) = −t2 +5t, as shown in figure 7.2. Let’s examine the motion of this object carefully. We know that the velocity is the derivative of position, so position is given by s(t) = −t3 /3 + 5t2 /2 + C. Let’s suppose that at time t = 0 the object is at position 0, so s(t) = −t3 /3 + 5t2 /2; this function is also pictured in figure 7.2. Between t = 0 and t = 5 the velocity is positive, so the object moves away from the starting point, until it is a bit past position 20. Then the velocity becomes negative and the object moves back toward its starting point. The position of the object at t = 5 is exactly s(5) = 125/6, and at t = 6 it is s(6) = 18. The total distance traveled by the object is therefore 125/6 + (125/6 − 18) = 71/3 ≈ 23.7. 7.3 6 4 2 0 −2 −4 −6 .................................. ........ ...... ...... ...... ..... ..... . . . . ..... .. . . . . ..... ... . . ..... . .. . . .... . .. ... . . . ... . . . ... . . ... . . . ... . . . ... . . . ... . . ... . . . ... . . . ... . . . ... . ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 1 2 3 4 5 6 20 15 10 5 0 .......................... ......... ...... ...... ..... ...... ..... . . . . .. .. . . . . .. . . . . ... . . . .. . . . ..... .... .... .... . . . ... ..... ..... ... . . . .... .... ..... .... . . . .... .... ..... .... . . . . ..... ..... ..... ..... . . . . . ..... ....... .............. 1 Figure 7.2 151 Some Properties of Integrals 2 3 4 5 6 The velocity of an object and its position. As we have seen, we can also compute distance traveled with an integral; let’s try it. ¯6 Z 6 Z 6 5 2 ¯¯ −t3 2 v(t) dt = −t + 5t dt = + t ¯ = 18. 3 2 0 0 0 What went wrong? Well, nothing really, except that it’s not really true after all that “we can also compute distance traveled with an integral”. Instead, as you might guess from this example, the integral actually computes the net distance traveled, that is, the difference between the starting and ending point. As we have already seen, Z 6 Z 5 Z 6 v(t) dt = v(t) dt + v(t) dt. 0 0 5 Computing the two integrals on the right (do it!) gives 125/6 and −17/6, and the sum of these is indeed 18. But what does that negative sign mean? It means precisely what you might think: it means that the object moves backwards. To get the total distance traveled we can add 125/6 + 17/6 = 71/3, the same answer we got before. Remember that we can also interpret an integral as measuring an area, but now we see that this too is a little more complicated that we have suspected. The area under the curve v(t) from 0 to 5 is given by Z 5 125 v(t) dt = , 6 0 and the “area” from 5 to 6 is Z 6 v(t) dt = − 5 17 . 6 152 Chapter 7 Integration In other words, the area between the x-axis and the curve, but under the x-axis, “counts as negative area”. So the integral Z 6 v(t) dt = 18 0 measures “net area”, the area above the axis minus the (positive) area below the axis. If we recall that the integral is the limit of a certain kind of sum, this behavior is not surprising. Recall the sort of sum involved: n−1 X v(ti )∆t. i=0 In each term v(t)∆t the ∆t is positive, but if v(ti ) is negative then the term is negative. If over an entire interval, like 5 to 6, the function is always negative, then the entire sum is negative. In terms of area, v(t)∆t is then a negative height times a positive width, giving a negative rectangle “area”. So now we see that when evaluating Z 6 v(t) dt = − 5 17 6 by finding an antiderivative, substituting, and subtracting, we get a surprising answer, but one that turns out to make sense. Let’s now try something a bit different: Z 5 6 ¯5 −t3 5 2 ¯¯ 5 −63 5 17 −53 v(t) dt = + t ¯ = + 52 − − 62 = . 3 2 6 3 2 3 2 6 Here we simply interchanged the limits 5 and 6, so of course when we substitute and subtract we’re subtracting in the opposite order and we end up multiplying the answer by −1. This too makes sense in terms of the underlying sum, though it takes a bit more thought. Recall that in the sum n−1 X v(ti )∆t, i=0 the ∆t is the “length” of each little subinterval, but more precisely we could say that ∆t = ti+1 − ti , the difference between two endpoints of a subinterval. We have until now 7.3 Some Properties of Integrals 153 assumed that we were working left to right, but could as well number the subintervals from right to left, so that t0 = b and tn = a. Then ∆t = ti+1 − ti is negative and in Z n−1 X 5 v(t) dt = 6 v(ti )∆t, i=0 the values v(ti ) are negative but also ∆t is negative, so all terms are positive again. On the other hand, in Z 0 n−1 X v(t) dt = v(ti )∆t, 5 i=0 the values v(ti ) are positive but ∆t is negative,and we get a negative result: Z 5 0 ¯0 5 2 ¯¯ −53 5 125 −t3 + t ¯ =0− − 52 = − . v(t) dt = 3 2 5 3 2 6 Finally we note one simple property of integrals: Z Z b f (x) + g(x) dx = a Z b b f (x) dx + g(x) dx. a a This is easy to understand once you recall that (F (x) + G(x))0 = F 0 (x) + G0 (x). Hence, if F 0 (x) = f (x) and G0 (x) = g(x), then Z b b f (x) + g(x) dx = (F (x) + G(x))|a a = F (b) + G(b) − F (a) − G(a) = F (b) − F (a) + G(b) − G(a) b b = F (x)|a + G(x)|a Z b Z b = f (x) dx + g(x) dx. a a In summary, we will frequently use these properties of integrals: Z b Z c Z b f (x) dx = f (x) dx + f (x) dx Z a a b c Z b f (x) + g(x) dx = a Z f (x) dx + a b Z f (x) dx = − a Z g(x) dx a a f (x) dx b b 154 Chapter 7 Integration and if a < b and f (x) ≤ 0 on [a, b] then Z b f (x) dx ≤ 0 a and in fact Z Z b b f (x) dx = − a |f (x)| dx. a Exercises 1. An object moves so that its velocity at time t is v(t) = −9.8t + 20 m/s. Describe the motion of the object between t = 0 and t = 5, find the total distance traveled by the object during that time, and find the net distance traveled. ⇒ 2. An object moves so that its velocity at time t is v(t) = sin t. Set up and evaluate a single definite integral to compute the net distance traveled between t = 0 and t = 2π. ⇒ 3. An object moves so that its velocity at time t is v(t) = 1 + 2 sin t m/s. Find the net distance traveled by the object between t = 0 and t = 2π, and find the total distance traveled during the same period. ⇒ 4. Consider the function f (x) = (x + 2)(x + 1)(x − 1)(x − 2) on [−2, 2]. Find the total area between the curve and the x-axis (measuring all area as positive). ⇒ 5. Consider the function f (x) = x2 − 3x + 2 on [0, 4]. Find the total area between the curve and the x-axis (measuring all area as positive). ⇒ 6. Evaluate the three integrals: Z 3 A= −x2 + 9 dx 0 and verify that A = B + C. ⇒ Z 4 B= 0 Z −x2 + 9 dx 3 C= 4 −x2 + 9 dx, 8 Techniques of Integration Over the next few sections we examine some techniques that are frequently successful when seeking antiderivatives of functions. Sometimes this is a simple problem, since it will be apparent that the function you wish to integrate is a derivative in some straightforward way. For example, faced with Z x10 dx you realize immediately that the derivative of x11 will supply an x10 : (x11 )0 = 11x10 . We don’t want the “11”, but constants are easy to alter, because differentiation “ignores” them in certain circumstances, so d 1 11 1 x = 11x10 = x10 . dx 11 11 From our knowledge of derivatives, we can immediately write down a number of antiderivatives. Here is a list of those most often used: Z xn+1 xn dx = + C, if n 6= −1 n+1 Z x−1 dx = ln |x| + C Z ex dx = ex + C 155 156 Chapter 8 Techniques of Integration Z sin x dx = − cos x + C Z cos x dx = sin x + C Z sec2 x dx = tan x + C Z sec x tan x dx = sec x + C Z 1 dx = arctan x + C 1 + x2 Z 1 √ dx = arcsin x + C 1 − x2 8.1 Substitution Needless to say, most problems we encounter will not be so simple. Here’s a slightly more complicated example: find Z 2x cos(x2 ) dx. This is not a “simple” derivative, but a little thought reveals that it must have come from an application of the chain rule. Multiplied on the “outside” is 2x, which is the derivative of the “inside” function x2 . Checking: d d sin(x2 ) = cos(x2 ) x2 = 2x cos(x2 ), dx dx so Z 2x cos(x2 ) dx = sin(x2 ) + C. Even when the chain rule has “produced” a certain derivative, it is not always easy to see. Consider this problem: Z p x3 1 − x2 dx. √ There are two factors in this expression, x3 and 1 − x2 , but it is not apparent that the chain rule is involved. Some clever rearrangement reveals that it is: Z x 3 Z p 1− x2 dx = µ ¶ p 1 (−2x) − (1 − (1 − x2 )) 1 − x2 dx. 2 8.1 Substitution 157 This looks messy, but we do now have something that looks like the result of the chain √ rule: the function 1 − x2 has been substituted into −(1/2)(1 − x) x, and the derivative of 1 − x2 , −2x, multiplied on the outside. If we can find a function F (x) whose derivative √ is −(1/2)(1 − x) x we’ll be done, since then µ ¶ p d 1 2 0 2 F (1 − x ) = −2xF (1 − x ) = (−2x) − (1 − (1 − x2 )) 1 − x2 dx 2 p = x3 1 − x2 But this isn’t hard: Z √ 1 − (1 − x) x dx = 2 Z 1 − (x1/2 − x3/2 ) dx 2 µ ¶ 1 2 3/2 2 5/2 x − x +C =− 2 3 5 µ ¶ 1 1 = x− x3/2 + C. 5 3 (8.1) So finally we have Z µ ¶ p 1 1 2 2 (1 − x ) − (1 − x2 )3/2 + C. x 1 − x dx = 5 3 3 So we succeeded, but it required a clever first step, rewriting the original function so that it looked like the result of using the chain rule. Fortunately, there is a technique that makes such problems simpler, without requiring cleverness to rewrite a function in just the right way. It does sometimes not work, or may require more than one attempt, but the idea is simple: guess at the most likely candidate for the “inside function”, then do some algebra to see what this requires the rest of the function to look like. One frequently good guess is any complicated expression inside a square root, so we start by trying u = 1 − x2 . We use a new variable, u, for convenience in the manipulations that follow. Now we know that the chain rule will multiply by the derivative of this inner function: du = −2x, dx so we need to rewrite the original function to include this: Z 3 x p Z 1− x2 = √ −2x x u dx = −2x 3 Z x2 √ du u dx. −2 dx 158 Chapter 8 Techniques of Integration Recall that one benefit of the Leibniz notation is that it turns out that what looks like ordinary arithmetic gives the correct answer, even if something more complicated is going on. For example, in Leibniz notation the chain rule is dy dy dt = . dx dt dx The same is true of our current expression: Z 2 Z 2 x √ x √ du u dx = u du. −2 dx −2 Now we’re almost there: since u = 1 − x2 , x2 = 1 − u and the integral is Z √ 1 − (1 − u) u du. 2 It’s no coincidence that this is exactly the integral we computed in (8.1), we have simply renamed the variable u to make the calculations less confusing. Just as before: µ ¶ Z √ 1 1 1 − (1 − u) u du = u− u3/2 + C. 2 5 3 Then since u = 1 − x2 : µ ¶ Z p 1 1 3 2 2 x 1 − x dx = (1 − x ) − (1 − x2 )3/2 + C. 5 3 To summarize: if we suspect that a given function is the derivative of another via the chain rule, we let u denote a likely candidate for the inner function, then translate the given function so that it is written entirely in terms of u, with no x remaining in the expression. If we can integrate this new function of u, then the antiderivative of the original function is obtained by replacing u by the equivalent expression in x. Even in simple cases you may prefer to use this mechanical procedure, since it often helps to avoid silly mistakes. For example, consider again this simple problem: Z 2x cos(x2 ) dx. Let u = x2 , then du/dx = 2x or du = 2x dx. Since we have exactly 2x dx in the original integral, we can replace it by du: Z Z 2 2x cos(x ) dx = cos u du = sin u + C = sin(x2 ) + C. 8.1 Substitution 159 This is not the only way to do the algebra, and typically there are many paths to the correct answer. Another possibility, for example, is: Since du/dx = 2x, dx = du/2x, and then the integral becomes Z Z 2 2x cos(x ) dx = du 2x cos u = 2x Z cos u du. The important thing to remember is that you must eliminate all instances of the original variable x. Z EXAMPLE 8.1 Evaluate (ax + b)n dx, assuming that a and b are constants, a 6= 0, and n is a positive integer. We let u = ax + b so du = a dx or dx = du/a. Then Z Z n (ax + b) dx = 1 n 1 1 u du = un+1 + C = (ax + b)n+1 + C. a a(n + 1) a(n + 1) Z EXAMPLE 8.2 Evaluate sin(ax + b) dx, assuming that a and b are constants and a 6= 0. Again we let u = ax + b so du = a dx or dx = du/a. Then Z Z 1 1 1 sin u du = (− cos u) + C = − cos(ax + b) + C. a a a sin(ax + b) dx = Z EXAMPLE 8.3 Then x sin(x2 ) dx. Let u = x2 so du = 2x dx or x dx = du/2. Evaluate Z Z 1 1 1 sin u du = (− cos u) + C = − cos(x2 ) + C. 2 2 2 2 x sin(x ) dx = Z EXAMPLE 8.4 Evaluate cos(πt) dt. Let u = sin(πt) so du = π cos(πt) dt or sin2 (πt) du/π = cos(πt) dt. Then Z cos(πt) dt = sin2 (πt) Z 1 1 du = π u2 Z 1 −2 1 u−1 1 1 u du = +C =− +C =− + C. π π −1 uπ π sin(πt) 160 Chapter 8 Techniques of Integration Exercises Find the antiderivatives. Z 1. (1 − t)9 dt ⇒ Z 2. (x2 + 1)2 dx ⇒ Z 3. x(x2 + 1)100 dx ⇒ Z 1 √ 4. dt ⇒ 3 1 − 5t Z 5. sin3 x cos x dx ⇒ Z p 6. x 100 − x2 dx ⇒ Z x2 √ 7. dx ⇒ 1 − x3 Z “ ” 8. cos(πt) cos sin(πt) dt ⇒ Z sin x 9. dx ⇒ cos3 x Z 10. tan x dx ⇒ Z π 11. sin5 (3x) cos(3x) dx ⇒ Z0 sec2 x tan x dx ⇒ 12. Z √ 13. π/2 x sec2 (x2 ) tan(x2 ) dx ⇒ Z0 14. 15. 16. 17. 18. sin(tan x) dx ⇒ cos2 x Z 4 1 dx ⇒ (3x − 7)2 3 Z π/6 (cos2 x − sin2 x) dx ⇒ Z0 6x dx ⇒ (x2 − 7)1/9 Z 1 (2x3 − 1)(x4 − 2x)6 dx ⇒ Z −1 1 19. Z sin7 x dx ⇒ −1 f (x)f 0 (x) dx ⇒ 20. 8.2 Powers of sine and cosine Functions consisting of products of the sine and cosine can be integrated by using substitution and trigonometric identities. These can sometimes be tedious, but the technique is straightforward. Some examples will suffice to explain the approach. Z EXAMPLE 8.5 Z sin5 x dx. Rewrite the function: Evaluate Z 5 sin x dx = Z 4 sin x sin x dx = Z 2 2 sin x(sin x) dx = sin x(1 − cos2 x)2 dx. 8.2 Powers of sine and cosine 161 Now use u = cos x, du = − sin x dx: Z Z 2 2 sin x(1 − cos x) dx = −(1 − u2 )2 du Z = −(1 − 2u2 + u4 ) du 2 1 = −u + u3 − u5 + C 3 5 2 1 = − cos x + cos3 x − cos5 x + C. 3 5 Z EXAMPLE 8.6 sin6 x dx. Use sin2 x = (1 − cos(2x))/2 to rewrite the Evaluate function: Z Z 6 sin x dx = Z 2 3 (1 − cos 2x)3 dx 8 Z 1 = 1 − 3 cos 2x + 3 cos2 2x − cos3 2x dx. 8 (sin x) dx = Now we have four integrals to evaluate: Z 1 dx = x and Z 3 −3 cos 2x dx = − sin 2x 2 are easy. The cos3 2x integral is like the previous example: Z Z 3 − cos 2x dx = − cos 2x cos2 2x dx Z = − cos 2x(1 − sin2 2x) dx Z 1 = − (1 − u2 ) du 2 µ ¶ 1 u3 =− u− 2 3 µ ¶ 1 sin3 2x =− sin 2x − . 2 3 162 Chapter 8 Techniques of Integration And finally we use another trigonometric identity, cos2 x = (1 + cos(2x))/2: Z Z 2 3 cos 2x dx = 3 1 + cos 4x 3 dx = 2 2 µ ¶ sin 4x x+ . 4 So at long last we get Z x 3 1 sin x dx = − sin 2x − 8 16 16 6 µ ¶ µ ¶ sin3 2x 3 sin 4x sin 2x − + x+ + C. 3 16 4 Z EXAMPLE 8.7 Evaluate sin2 x cos2 x dx. Use the double angle formulas sin2 x = (1 − cos(2x))/2 and cos2 x = (1 + cos(2x))/2 to get: Z Z 1 1 − cos (2x) dx = sin2 (2x) dx 4 µ ¶ Z 1 1 sin(4x) x− + C. = 1 − cos(4x) dx = 8 8 4 1 sin x cos x dx = 4 2 2 Z 2 Exercises Find the antiderivatives. Z 1. sin2 x dx ⇒ Z 2. sin3 x dx ⇒ Z 3. sin4 x dx ⇒ Z 4. cos2 x sin3 x dx ⇒ Z 5. cos3 x dx ⇒ Z sin2 x cos2 x dx ⇒ 6. Z cos3 x sin2 x dx ⇒ 7. Z sin x(cos x)3/2 dx ⇒ 8. Z sec2 x csc2 x dx ⇒ 9. Z 10. tan3 x sec x dx ⇒ 8.3 Trigonometric Substitutions So far we have seen that it sometimes helps to replace a subexpression of a function by a single variable. Occasionally it can help to replace the original variable by something more complicated. This seems like a “reverse” substitution, but it is really no different in principle than ordinary substitution. 8.3 EXAMPLE 8.8 Evaluate Trigonometric Substitutions 163 Z p 1 − x2 dx. Let x = sin u so dx = cos u du. Then Z p Z p Z √ 2 2 1 − x dx = 1 − sin u cos u du = cos2 u cos u du. √ We would like to replace cos2 u by cos u, but this is valid only if cos u is positive, since √ cos2 u is positive. Consider again the substitution x = sin u. We could just as well think of this as u = arcsin x. If we do, then by the definition of the arcsine, −π/2 ≤ u ≤ π/2, so cos u ≥ 0. Then we continue: Z √ Z Z 1 + cos 2u u sin 2u 2 2 cos u cos u du = cos u du = du = + +C 2 2 4 = arcsin x sin(2 arcsin x) + + C. 2 4 This is a perfectly good answer, though the term sin(2 arcsin x) is a bit unpleasant. It is possible to simplify this. Using the identity sin 2x = 2 sin x cos x, we can write sin 2u = q p √ 2 sin u cos u = 2 sin(arcsin x) 1 − sin2 u = 2x 1 − sin2 (arcsin x) = 2x 1 − x2 . Then the full antiderivative is √ √ arcsin x 2x 1 − x2 arcsin x x 1 − x2 + = + + C. 2 4 2 2 This type of substitution is usually indicated when the function you wish to integrate contains a polynomial expression that might allow you to use the fundamental identity sin2 x + cos2 x = 1 in one of three forms: cos2 x = 1 − sin2 x sec2 x = 1 + tan2 x tan2 x = sec2 x − 1. If your function contains 1 − x2 , as in the example above, try x = sin u; if it contains 1 + x2 try x = tan u; and if it contains x2 − 1, try x = sec u. Sometimes you will need to try something a bit different to handle constants other than one. EXAMPLE 8.9 Evaluate Z p 4 − 9x2 dx. We start by rewriting this so that it looks more like the previous example: Z p Z p Z p 2 2 4 − 9x dx = 4(1 − (3x/2) ) dx = 2 1 − (3x/2)2 dx. 164 Chapter 8 Techniques of Integration Now let 3x/2 = sin u so (3/2) dx = cos u du or dx = (2/3) cos u du. Then Z p Z p Z 4 2 2 2 1 − (3x/2) dx = 2 1 − sin u(2/3) cos u du = cos2 u du 3 4u 4 sin 2u = + +C 6 12 2 arcsin(3x/2) 2 sin u cos u = + +C 3 3 2 arcsin(3x/2) 2 sin(arcsin(3x/2)) cos(arcsin(3x/2)) = + +C 3 3 p 2 arcsin(3x/2) 2(3x/2) 1 − (3x/2)2 = + +C 3 3 √ 2 arcsin(3x/2) x 4 − 9x2 + + C, = 3 2 using some of the work from example 8.8. EXAMPLE 8.10 Z p 1+ Evaluate x2 dx = Z p Z p 1 + x2 dx. Let x = tan u, dx = sec2 u du, so 2 2 1 + tan u sec u du = Z √ sec2 u sec2 u du. √ Since u = arctan(x), −π/2 ≤ u ≤ π/2 and sec u ≥ 0, so sec2 u = sec u. Then Z √ Z 2 2 sec u sec u du = sec3 u du. R R In problems of this type, two integrals come up frequently: sec3 u du and sec u du. Both have relatively nice expressions but they are a bit tricky to discover. R First we do sec u du: Z Z sec u + tan u sec u du = sec u du sec u + tan u Z sec2 u + sec u tan u = du. sec u + tan u Now let w = sec u + tan u, dw = sec u tan u + sec2 u du, exactly the numerator of the function we are integrating. Thus Z Z Z 1 sec2 u + sec u tan u du = dw = ln |w| + C sec u du = sec u + tan u w = ln | sec u + tan u| + C. 8.3 Now for R Trigonometric Substitutions 165 sec3 u du: sec3 u sec3 u sec3 u (tan2 u + 1) sec u + = + 2 2 2 2 sec3 u sec u tan2 u sec u sec3 u + sec u tan2 u sec u = + + = + . 2 2 2 2 2 sec3 u = We already know how to integrate sec u, so we just need the first quotient. This is “simply” a matter of recognizing the product rule in action: Z sec3 u + sec u tan2 u du = sec u tan u. So putting these together we get Z sec3 u du = sec u tan u ln | sec u + tan u| + + C, 2 2 and reverting to the original variable x: Z p 1 + x2 dx = sec u tan u ln | sec u + tan u| + +C 2 2 sec(arctan x) tan(arctan x) ln | sec(arctan x) + tan(arctan x)| + +C 2 2 √ √ x 1 + x2 ln | 1 + x2 + x| = + + C, 2 2 = using tan(arctan x) = x and sec(arctan x) = p 1 + tan2 (arctan x) = √ 1 + x2 . 166 Chapter 8 Techniques of Integration Exercises Find the antiderivatives. Z 1. csc x dx ⇒ Z 2. csc3 x dx ⇒ Z p 3. x2 − 1 dx ⇒ Z p 4. 9 + 4x2 dx ⇒ Z p 5. x 1 − x2 dx ⇒ Z p 6. x2 1 − x2 dx ⇒ Z 1 dx ⇒ 1 + x2 Z p 8. x2 + 2x dx ⇒ Z 1 9. dx ⇒ 2 x (1 + x2 ) Z x2 √ dx ⇒ 10. 4 − x2 √ Z x √ 11. dx ⇒ 1−x Z x3 √ 12. dx ⇒ 4x2 − 1 √ 7. 8.4 Integration by Parts We have already seen that recognizing the product rule can be useful, when we noticed that Z sec3 u + sec u tan2 u du = sec u tan u. As with substitution, we do not have to rely on insight or cleverness to discover such antiderivatives; there is a technique that will often help to uncover the product rule. Start with the product rule: d f (x)g(x) = f 0 (x)g(x) + f (x)g 0 (x). dx We can rewrite this as Z f (x)g(x) = and then Z 0 f (x)g 0 (x) dx, f (x)g(x) dx + Z Z 0 f (x)g (x) dx = f (x)g(x) − f 0 (x)g(x) dx. This may not seem particularly useful at first glance, but it turns out that in many cases we have an integral of the form Z f (x)g 0 (x) dx 8.4 but that Integration by Parts 167 Z f 0 (x)g(x) dx is easier. This technique for turning one integral into another is called integration by parts, and is usually written in more compact form. If we let u = f (x) and v = g(x) then du = f 0 (x) dx and dv = g 0 (x) dx and Z Z u dv = uv − v du. To use this technique we need to identify likely candidates for u = f (x) and dv = g 0 (x) dx. Z EXAMPLE 8.11 Evaluate x ln x dx. Let u = ln x so du = 1/x dx. Then we must let dv = x dx so v = x2 /2 and Z x2 ln x x ln x dx = − 2 Z x2 1 x2 ln x dx = − 2 x 2 Z x x2 ln x x2 dx = − + C. 2 2 4 Z EXAMPLE 8.12 Evaluate x sin x dx. Let u = x so du = dx. Then we must let dv = sin x dx so v = − cos x and Z Z x sin x dx = −x cos x − Z − cos x dx = −x cos x + cos x dx = −x cos x + sin x + C. Z EXAMPLE 8.13 Evaluate sec3 x dx. Of course we already know the answer to this, but we needed to be clever to discover it. Here we’ll use the new technique to discover the antiderivative. Let u = sec x and dv = sec2 x dx. Then du = sec x tan x and v = tan x and Z Z 3 sec x dx = sec x tan x − tan2 x sec x dx Z = sec x tan x − (sec2 x − 1) sec x dx Z Z 3 = sec x tan x − sec x dx + sec x dx. 168 Chapter 8 Techniques of Integration At first this looks useless—we’re right back to R sec3 x dx. But looking more closely: Z Z 3 sec x dx = sec x tan x − Z Z 3 sec x dx Z sec x dx = sec x tan x + Z 2 sec x dx + 3 sec x dx + Z 3 sec x dx Z 3 sec x dx = sec x tan x + sec x dx Z Z sec x tan x 1 3 sec x dx = + sec x dx 2 2 = sec x tan x ln | sec x + tan x| + + C. 2 2 Z Evaluate x2 sin x dx. Let u = x2 , dv = sin x dx; then du = 2x dx Z Z 2 2 and v = − cos x. Now x sin x dx = −x cos x + 2x cos x dx. This is better than the EXAMPLE 8.14 original integral, but we need to do integration by parts again. Let u = 2x, dv = cos x dx; then du = 2 and v = sin x, and Z Z 2 2 x sin x dx = −x cos x + 2x cos x dx Z 2 = −x cos x + 2x sin x − 2 sin x dx = −x2 cos x + 2x sin x + 2 cos x + C. Such repeated use of integration by parts is fairly common, but it can be a bit tedious to accomplish, and it is easy to make errors, especially sign errors involving the subtraction in the formula. There is a nice tabular method to accomplish the calculation that minimizes the chance for error and speeds up the whole process. We illustrate with the previous example. Here is the table: 8.4 sign u 2 − − Integration by Parts dv u x sin x x 2x − cos x 2 0 or 2 169 dv sin x −2x − cos x − sin x 2 − sin x cos x 0 cos x To form the first table, we start with u at the top of the second column and repeatedly compute the derivative; starting with dv at the top of the third column, we repeatedly compute the antiderivative. In the first column, we place a “−” in every second row. To form the second table we combine the first and second columns by ignoring the boundary; if you do this by hand, you may simply start with two columns and add a “−” to every second row. To compute with this second table we begin at the top. Multiply the first entry in column u by the second entry in column dv to get −x2 cos x, and add this to the integral of the product of the second entry in column u and second entry in column dv. This gives: Z 2 −x cos x + 2x cos x dx, or exactly the result of the first application of integration by parts. Since this integral is not yet easy, we return to the table. Now we multiply twice on the diagonal, (x2 )(− cos x) and (−2x)(− sin x) and then once straight across, (2)(− sin x), and combine these as Z 2 −x cos x + 2x sin x − 2 sin x dx, giving the same result as the second application of integration by parts. While this integral is easy, we may return yet once more to the table. Now multiply three times on the diagonal to get (x2 )(− cos x), (−2x)(− sin x), and (2)(cos x), and once straight across, (0)(cos x). We combine these as before to get Z 2 −x cos x + 2x sin x + 2 cos x + 0 dx = −x2 cos x + 2x sin x + 2 cos x + C. Typically we would fill in the table one line at a time, until the “straight across” multiplication gives an easy integral. If we can see that the u column will eventually become zero, we can instead fill in the whole table; computing the products as indicated will then give the entire integral, including the “+C”, as above. 170 Chapter 8 Techniques of Integration Exercises Find the antiderivatives. Z 1. x cos x dx ⇒ Z 2. x2 cos x dx ⇒ Z 3. xex dx ⇒ Z 2 4. xex dx ⇒ Z 5. sin2 x dx ⇒ Z 6. ln x dx ⇒ Z 7. x arctan x dx ⇒ Z x2 sin x dx ⇒ 8. Z x sin2 x dx ⇒ 9. Z 10. x sin x cos x dx ⇒ Z 11. Z 12. √ arctan( x) dx ⇒ √ sin( x) dx ⇒ Z 13. sec2 x csc2 x dx ⇒ 8.5 Rational Functions A rational function is a fraction with polynomials in the numerator and denominator. For example, x3 1 x2 + 1 , , , x2 + x − 6 (x − 3)2 x2 − 1 are all rational functions of x. There is a general technique called “partial fractions” that, in principle, allows us to integrate any rational function. The algebraic steps in that technique are rather cumbersome if the polynomial in the denominator has degree more than 2, and the technique requires that we factor the denominator, something that is not always possible. However, in practice one does not often run across rational functions with high degree polynomials in the denominator for which one has to find the antiderivative function. So we shall explain how to find the antiderivative of a rational function only when the denominator is a quadratic polynomial ax2 + bx + c. We should mention a special type of rational function that we already know how to integrate: If the denominator has the form (ax + b)n , the substitution u = ax + b will always work. The denominator becomes un , and each x in the numerator is replaced by (u − b)/a, and dx = du/a. While it may be tedious to complete the integration if the numerator has high degree, it is merely a matter of algebra. 8.5 Z EXAMPLE 8.15 Z Find t3 1 dx = 5 (3 − 2t) −2 = 1 16 1 = 16 1 = 16 =− Z Z 171 Rational Functions t3 dx. Using the substitution u = 3 − 2t we get (3 − 2t)5 ³ u−3 −2 ´3 u5 1 du = 16 Z u3 − 9u2 + 27u − 27 du u5 u−2 − 9u−3 + 27u−4 − 27u−5 du µ µ 9u−2 27u−3 27u−4 u−1 − + − −1 −2 −3 −4 ¶ (3 − 2t)−1 9(3 − 2t)−2 27(3 − 2t)−3 27(3 − 2t)−4 − + − −1 −2 −3 −4 ¶ 1 9 9 27 + − + 2 3 16(3 − 2t) 32(3 − 2t) 16(3 − 2t) 64(3 − 2t)4 We now proceed to the case when the denominator is a quadratic polynomial. We can always factor out the coefficient of x2 and put it outside the integral, so we can assume that the denominator has the form x2 + bx + c. There are three possible cases, depending on how the quadratic factors: either x2 + bx + c = (x − r)(x − s), x2 + bx + c = (x − r)2 , or it doesn’t factor. We can use the quadratic formula to decide which of these we have, and to factor the quadratic if it is possible. EXAMPLE 8.16 Determine whether x2 + x + 1 factors, and factor it if possible. The quadratic formula tells us that x2 + x + 1 = 0 when x= −1 ± √ 2 1−4 . Since there is no square root of −3, this quadratic does not factor. EXAMPLE 8.17 Determine whether x2 − x − 1 factors, and factor it if possible. The quadratic formula tells us that x2 − x − 1 = 0 when x= Therefore 1± Ã x2 − x − 1 = √ 1+4 1± 5 = . 2 2 √ √ !Ã √ ! 1+ 5 1− 5 x− x− . 2 2 172 Chapter 8 Techniques of Integration If x2 + bx + c = (x − r)2 then we have the special case we have already seen, that can be handled with a substitution. The other two cases require different approaches. If x2 + bx + c = (x − r)(x − s), we have an integral of the form Z p(x) dx (x − r)(x − s) where p(x) is a polynomial. The first step is to make sure that p(x) has degree less than 2. Z x3 dx in terms of an integral with a numerator (x − 2)(x + 3) that has degree less than 2. To do this we use “long division of polynomials” to discover that EXAMPLE 8.18 Rewrite x3 x3 7x − 6 7x − 6 = 2 =x−1+ 2 =x−1+ , (x − 2)(x + 3) x +x−6 x +x−6 (x − 2)(x + 3) so Z x3 dx = (x − 2)(x + 3) Z Z x − 1 dx + 7x − 6 dx. (x − 2)(x + 3) The first integral is easy, so only the second requires some work. Now consider the following simple algebra of fractions: A B A(x − s) + B(x − r) (A + B)x − As − Br + = = . x−r x−s (x − r)(x − s) (x − r)(x − s) That is, adding two fractions with constant numerator and denominators (x−r) and (x−s) produces a fraction with denominator (x − r)(x − s) and a polynomial of degree less than 2 for the numerator. We want to reverse this process: starting with a single fraction, we want to write it as a sum of two simpler fractions. An example should make it clear how to proceed. Z x3 7x − 6 dx. We start by writing (x − 2)(x + 3) (x − 2)(x + 3) as the sum of two fractions. We want to end up with EXAMPLE 8.19 Evaluate A B 7x − 6 = + . (x − 2)(x + 3) x−2 x+3 8.5 Rational Functions 173 If we go ahead and add the fractions on the right hand side we get 7x − 6 (A + B)x + 3A − 2B = . (x − 2)(x + 3) (x − 2)(x + 3) So all we need to do is find A and B so that 7x − 6 = (A + B)x + 3A − 2B, which is to say, we need 7 = A + B and −6 = 3A − 2B. This is a problem you’ve seen before: solve a system of two equations in two unknowns. There are many ways to proceed; here’s one: If 7 = A+B then B = 7−A and so −6 = 3A−2B = 3A−2(7−A) = 3A−14+2A = 5A−14. This is easy to solve for A: A = 8/5, and then B = 7 − A = 7 − 8/5 = 27/5. Thus Z Z 8 1 27 1 8 27 7x − 6 dx = + dx = ln |x − 2| + ln |x + 3| + C. (x − 2)(x + 3) 5x−2 5 x+3 5 5 The answer to the original problem is now Z Z Z x3 7x − 6 dx = x − 1 dx + dx (x − 2)(x + 3) (x − 2)(x + 3) = 8 27 x2 − x + ln |x − 2| + ln |x + 3| + C. 2 5 5 Now suppose that x2 + bx + c doesn’t factor. Again we can use long division to ensure that the numerator has degree less than 2. Now we can complete the square to turn the integral into a trigonometric substitution problem. Z EXAMPLE 8.20 Evaluate x2 x dx. We have seen that this quadratic does +x+1 not factor. We complete the square: 1 1 3 1 x + x + 1 = x + x + + 1 − = x2 + x + + = 4 4 4 4 2 2 Now factor out 3/4: 3 4 µ ¶2 1 3 x+ + . 2 4 Ã µ ! Ãµ ! ¶2 ¶2 3 4 1 2 1 √ x+ √ x+ +1 = +1 . 3 2 4 3 3 Now let 2 1 tan u = √ x + √ 3 3 2 sec2 u du = √ dx 3 √ 3 sec2 u du. dx = 2 174 Chapter 8 Techniques of Integration √ √ Now we can substitute in the original integral, using x = ( 3/2)(tan u − 1/ 3) in the numerator. Z √ √ √ ( 3/2)(tan u − 1/ 3) 3 sec2 u du 2 tan2 u + 1 √ √ Z √ 4 ( 3/2)(tan u − 1/ 3) 3 = sec2 u du 3 sec2 u 2 ¶√ Z √ µ 4 1 3 3 = tan u − √ du 3 2 2 3 Z 1 = tan u − √ du 3 u = − ln | cos u| − √ + C. 3 u = ln | sec u| − √ + C. 3 x 4 dx = 2 x +x+1 3 Z Finally, we can substitute µ u = arctan and p sec u = tan2 u + 1 = to get 2 1 √ x+ √ 3 3 sµ ¶ 2 1 √ x+ √ 3 3 ¶2 +1 ¯s ¯ ¯ µ ¯ ¶2 µ ¶ ¯ ¯ 2 1 2 1 1 ¯ ¯ √ x+ √ ln ¯ + 1¯ − √ arctan √ x + √ + C. 3 3 3 3 3 ¯ ¯ The details here are admittedly a bit unpleasant, but the whole process is fairly mechanical and “easy” in principle. Exercises Find the antiderivatives. Z 1 1. dx ⇒ 4 − x2 Z x4 2. dx ⇒ 4 − x2 Z 1 dx ⇒ + 10x + 25 Z x2 4. dx ⇒ 4 − x2 3. x2 8.5 Z x4 dx ⇒ 4 + x2 Z 1 6. dx ⇒ x2 + 10x + 29 Z x3 7. dx ⇒ 4 + x2 5. Z Rational Functions 1 dx ⇒ x2 + 10x + 21 Z 1 9. dx ⇒ 2x2 − x − 3 Z 1 10. dx ⇒ 2 x + 3x 8. 175 176 Chapter 8 Techniques of Integration 8.6 Additional exercises These problems require the techniques of this chapter, and are in no particular order. Some problems may be done in more than one way. Exercises Z Z (t + 4)3 dt ⇒ 1. Z t(t2 − 9)3/2 dt ⇒ 2. Z 2 2 (et + 16)tet dt ⇒ 3. Z 4. sin t cos 2t dt ⇒ Z tan t sec2 t dt ⇒ 5. Z 2t + 1 dt ⇒ +t+3 Z 1 7. dt ⇒ t(t2 − 4) Z 1 8. dt ⇒ (25 − t2 )3/2 Z cos 3t √ 9. dt ⇒ sin 3t Z 10. t sec2 t dt ⇒ Z et √ 11. dt ⇒ et + 1 Z 12. cos4 t dt ⇒ Z 1 13. dt ⇒ t2 + 3t Z 1 √ 14. dt ⇒ 2 t 1 + t2 6. t2 sec2 t dt ⇒ (1 + tan t)3 Z p 16. t3 t2 + 1 dt ⇒ Z 17. et sin t dt ⇒ Z √ 18. (t3/2 + 47)3 t dt ⇒ Z t3 19. dt ⇒ (2 − t2 )5/2 Z 1 dt ⇒ 20. t(9 + 4t2 ) Z arctan 2t 21. dt ⇒ 1 + 4t2 Z t 22. dt ⇒ t2 + 2t − 3 Z 23. sin3 t cos4 t dt ⇒ Z 1 24. dt ⇒ 2 t − 6t + 9 Z 1 25. dt ⇒ t(ln t)2 Z 26. t(ln t)2 dt ⇒ Z 27. t3 et dt ⇒ Z t+1 28. dt ⇒ 2 t +t−1 15. 9 Applications of Integration 9.1 Area between curves We have seen how integration can be used to find an area between a curve and the x-axis. With very little change we can find some areas between curves; indeed, the area between a curve and the x-axis may be interpreted as the area between the curve and a second “curve” with equation y = 0. In the simplest of cases, the idea is quite easy to understand. EXAMPLE 9.1 Find the area below f (x) = −x2 + 4x + 3 and above g(x) = −x3 + 7x2 − 10x + 5 over the interval 1 ≤ x ≤ 2. In figure 9.1 we show the two curves together, with the desired area shaded, then f alone with the area under f shaded, and then g alone with the area under g shaded. It is clear from the figure that the area we want is the area under f minus the area under g, which is to say Z Z 2 f (x) dx − 1 Z 2 g(x) dx = 1 2 f (x) − g(x) dx. 1 177 178 Chapter 9 Applications of Integration 10 5 .. ... ... . . .. .. ... ... . . . ... ... ... . . ....................................................... ........................................................ ... ............. ..... ..................................................................................... .. ................................................................................................................................................................................... ..... . . . . .... ........................................................... .. ..... ................................................................................................................................................................ . . ... . . . . . . . . . . . . . . . . . . ..... ........................................................................................... ... ....... . . . . . . . . . . . . . . . . ... ..... . ...................................................................................... ... ... . . . . . . . . . . . . . . ..... .. ... ............................................................................ ..... . .. .......................................................... . . . . . . . . . . . . . . . ..... .... .. .................................................................. . . . . . . . . . . . .... ... ..... .. ........................................................... . . . . . . . . . ...... ... .. ................................................... . . . . . . . . ... ... .. ........................................... . . . . . . . ... ... .. ................................. . ... ..... ..................................... .............. 10 10 ............................................................. ...... ........................................................ ...... ...................................................................................... ................................................................................................................................................................ . . . . .. ..................................................................................................................... . . . . ... ..................................................................................................................... . . . ................... .................................................................................................. .... ................... .... .................................................................................................. ................... .. .................................................................................................. ... ................... . . .................................................................................................. . . .............................................................................. . ................... .. ........................................................... 5 0 .......................................................... .................................................................................................. ................... .................................................................................................. ................... .................................................................................................. .............................................................................. ................... .................................................................................................. ................... .................................................................................................. ................... .................................................................................................. .............................................................................. 0 0 1 2 3 Figure 9.1 0 1 2 5 .. ... ... . . .. .. ... ... . . . ... ... ... . . . ... ... ... . . . ... ... .... ... ............... . . ... .............. ... .................. ... ................................ ... ........................................................ . . . ... ........ .............................................. ... ................................................................. ... ............................................................... ... .................................................................... . ... . ..... . . . ............................................................................................................................................................. .............................................................................. ....................................... .................................................................................................. .............................................................................. 0 3 0 1 2 3 Area between curves as a difference of areas. It doesn’t matter whether we compute the two integrals on the left and then subtract or compute the single integral on the right. In this case, the latter is perhaps a bit easier: Z Z 2 2 f (x) − g(x) dx = 1 −x2 + 4x + 3 − (−x3 + 7x2 − 10x + 5) dx 1 Z = 2 x3 − 8x2 + 14x − 2 dx 1 ¯2 ¯ x4 8x3 2 = − + 7x − 2x¯¯ 4 3 1 1 8 16 64 − + 28 − 4 − ( − + 7 − 2) 4 3 4 3 56 1 49 = 23 − − = . 3 4 12 = It is worth examining this problem a bit more. We have seen one way to look at it, by viewing the desired area as a big area minus a small area, which leads naturally to the difference between two integrals. But it is instructive to consider how we might find the desired area directly. We can approximate the area by dividing the area into thin sections and approximating the area of each section by a rectangle, as indicated in figure 9.2. The area of a typical rectangle is ∆x(f (xi ) − g(xi )), so the total area is approximately n−1 X i=0 (f (xi ) − g(xi ))∆x. 9.1 Area between curves 179 This is exactly the sort of sum that turns into an integral in the limit, namely the integral Z 2 f (x) − g(x) dx. 1 Of course, this is the integral we actually computed above, but we have now arrived at it directly rather than as a modification of the difference between two other integrals. In that example it really doesn’t matter which approach we take, but in some cases this second approach is better. 10 5 ...... ...... ...... . . . . . ...... ..... ...... ..... . . . . ..... ..... ..... ..... . . . . ..................................................................... ................................ ................... .... ................... ............... ..... .............. ....... ............ ..... . . . . . . . . . . ..... .... . . . . . . . . . . . . . . .. .... . . . . . . . . . ... . . . . .... ......... ..... ..... ......... ...... ..... ............... ...... ............ ...... . . . . ..... ...... ........ ....... ..... ...... ...... ...... ....... ...... . . . . . . . ...... ....... ........ ......... ........ .............. .......... .................................... 0 0 1 Figure 9.2 2 3 Area between curves. EXAMPLE 9.2 Find the area below f (x) = −x2 + 4x + 1 and above g(x) = −x3 + 7x2 − 10x + 3 over the interval 1 ≤ x ≤ 2; these are the same curves as before but lowered by 2. In figure 9.3 we show the two curves together. Note that the lower curve now dips below the x-axis. This makes it somewhat tricky to view the desired area as a big area minus a smaller area, but it is just as easy as before to think of approximating the area by rectangles. The height of a typical rectangle will still be f (xi ) − g(xi ), even if g(xi ) is negative. Thus the area is Z 2 Z 2 3 2 2 −x + 4x + 1 − (−x + 7x − 10x + 3) dx = 1 x3 − 8x2 + 14x − 2 dx. 1 This is of course the same integral as before, because the region between the curves is identical to the former region—it has just been moved down by 2. EXAMPLE 9.3 Find the area between f (x) = −x2 + 4x and g(x) = x2 − 6x + 5 over the interval 0 ≤ x ≤ 1; the curves are shown in figure 9.4. Generally we should interpret 180 Chapter 9 Applications of Integration 10 5 0 .. ..... .... .... . . . ..... .... .... .... . . . ... .... .... ... . . . ........................................................... ............................................................................................ ............. .. ....................................................................................................................... .......... ... .............................................................................................................................................. ..... ........................................................................................................................................................................................................................................................................................................................................................................ ........ . . . . . . . ....................................... . . ... . . . . ...................................................................................................................................................................................................... . . . . . . ... . .. ............................................................................................................................................................ . . . ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .............................................................................. .... .. ... .. ..................................................................................................................................................................................... ....... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... ... ...... ......................................................................................................................................................................... ...... . ... ... .......... ............................................................................................................................... ....... .. ........................................................................................................................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........................................................... .... ... ... .. .. ................................................................................................................................... . . . . . . . . . . . . . . . . . . . . . . ...... ...... ..... .... .. .................................................................................................................... ..... . . . . . . . . . . . . . . . . . . . ....... .... ................................................................................................... ... ..... . . .. .. ..................................................................... ..... . . . . . . . . . . . . ......... ...... .. ................................................................ ...... .... .. .. ............................. ... ....... ............................................................................ 0 1 Figure 9.3 2 3 Area between curves. “area” in the usual sense, as a necessarily positive quantity. Since the two curves cross, we need to compute two areas and add them. First we find the intersection point of the curves: −x2 + 4x = x2 − 6x + 5 0 = 2x2 − 10x + 5 √ √ 10 ± 100 − 40 5 ± 15 x= = . 4 2 √ The intersection point we want is x = a = (5 − 15)/2. Then the total area is Z a Z 2 1 2 x − 6x + 5 − (−x + 4x) dx + 0 −x2 + 4x − (x2 − 6x + 5) dx a Z a = Z 2 2x − 10x + 5 dx + 0 1 −2x2 + 10x − 5 dx a ¯a ¯1 ¯ ¯ 2x3 2x3 2 2 ¯ = − 5x + 5x¯ + − + 5x − 5x¯¯ 3 3 0 a √ 52 = − + 5 15, 3 after a bit of simplification. EXAMPLE 9.4 Find the area between f (x) = −x2 + 4x and g(x) = x2 − 6x + 5; the curves are shown in figure 9.5. Here we are not given a specific interval, so it must be the case that there is a “natural” region involved. Since the curves are both parabolas, the only reasonable interpretation is the region between the two intersection points, which we 9.1 5 4 3 2 1 0 ........ ...... .. . ... .. ................ .. . . ...... .. ............................. ... ............................ ..... . . . . . . .... .............................................. .. . . . . . . . . ... ... ...... ...................................................... . . . . . . . . . . .... .................................................................. .. ... .......................................................... ... . . . . . . . . . . . . . ..... .. ... .................................................................................... ... . . . . . . . . . . . . . . . . .... .............................................................................................. .. . . . . . . . . . . . . . . . . . . ....... ....... 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............................................................................................................................................................................................. ...... ..................................................................................................................... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .... ...... ................................................................................................................................................................................................................................... ......................................................................................................................................................................................................... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..... ........ ................................................................................................................................................................... ...................................................................................................................................................................................................................... . .. .................................................................................................... .. .. ............................................................................................................................................................................. . . . . ..................................................................................................... .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ .. .................................................................................................................................................................................................... ...... ............................................................................................................................................................................................................................................................. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ ... ................................................................................................................................................................................ ................................................................................................................................................ ....... .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........ .. ............................................................................................................................................................ ..................................................................................................................................... ....... .. . . . . . . . . . . . . . . . . . . . . . . . . . . . ...... .. ............................................................................................................................ . . ........................................................................................................................................... .... .. .. ...................................................................................................... ............................................................................................................... . . .. . . . . . . . . . . . . . . . . . . . . . ...... . ................................................................................................... .. ............................................................................................................... .. .. . . . . . . . . . . . . . . . . . ...... .. ........................................................................................... . . . ... ........................................................................................... .. .. . . . . . . . . . . . . . . ...... ............................................................................... . ............................................................................ .. . .................................................................. .. .. ................................................... .. . . . . . . . . . . ...... ...................................................... ... ................................................. ... . . ......................................... . . . . . ...... .. ... . ............................... ... ... ............................... . . . . ...... .. .. . ................... ................... . . ... .. ........... .... ... .... ... . 0 Figure 9.4 1 Area between curves that cross. found in the previous example: 5± If we let a = (5 − Z √ Area between curves 15)/2 and b = (5 + √ √ 2 15 . 15)/2, the total area is Z b b 2 2 −x + 4x − (x − 6x + 5) dx = −2x2 + 10x − 5 dx a a ¯b ¯ 2x3 2 =− + 5x − 5x¯¯ 3 a √ = 5 15. after a bit of simplification. 5 0 ... ... ... ... ........................................................ ... ............. .......... ... ......... ........ .... ........ ....... . . . ..... . . . ....... ..... ........... ...... .......... ...... ..... . ...... . . . ...... ... ...... . . . . ...... ..... .. . . . . ..... ..... .. . . . ..... . ..... .. . . ..... . . . ..... ..... ..... ..... ..... .... ..... ..... ..... ..... ..... ..... ..... . . . . . . ...... ..... . ...... ..... .......... ...... ........ ...... .............. ....... . . . . . . . ..... ....... ....... ........ .... ........ ......... ... ......... ........... ... ........... . ................... . . . . . . ... . . . . . . . . . ....................... ... ... ... 1 −5 Figure 9.5 2 3 4 Area bounded by two curves. 5 181 182 Chapter 9 Applications of Integration Exercises Find the area bounded by the curves. 1. y = x4 − x2 and y = x2 (the part to the right of the y-axis) ⇒ 2. x = y 3 and x = y 2 ⇒ 3. x = 1 − y 2 and y = −x − 1 ⇒ 4. x = 3y − y 2 and x + y = 3 ⇒ 5. y = cos(πx/2) and y = 1 − x2 (in the first quadrant) ⇒ 6. y = sin(πx/3) and y = x (in the first quadrant) ⇒ √ 7. y = x and y = x2 ⇒ √ √ 8. y = x and y = x + 1, 0 ≤ x ≤ 4 ⇒ 9. x = 0 and x = 25 − y 2 ⇒ 10. y = sin x cos x and y = sin x, 0 ≤ x ≤ π ⇒ 11. y = x3/2 and y = x2/3 ⇒ 12. y = x2 − 2x and y = x − 2 ⇒ 9.2 Distance, Velocity, Acceleration We next recall a general principle that will later be applied to distance-velocity-acceleration Rb problems, among other things. If F (u) is an anti-derivative of f (u), then a f (u) du = F (b) − F (a). Suppose that we want to let the upper limit of integration vary, i.e., we replace b by some variable x. We think of a as a fixed starting value x0 . In this new notation the last equation (after adding F (a) to both sides) becomes: Z x F (x) = F (x0 ) + f (u) du. x0 (Here u is the variable of integration, called a “dummy variable,” since it is not the variable in the function F (x). In general, it is not a good idea to use the same letter as a variable Rx of integration and as a limit of integration. That is, x0 f (x)dx is bad notation, and can lead to errors and confusion.) An important application of this principle occurs when we are interested in the position of an object at time t (say, on the x-axis) and we know its position at time t0 . Let s(t) denote the position of the object at time t (its distance from a reference point, such as the origin on the x-axis). Then the net change in position between t0 and t is s(t) − s(t0 ). Since s(t) is an anti-derivative of the velocity function v(t), we can write Z t s(t) = s(t0 ) + v(u)du. t0 9.2 Distance, Velocity, Acceleration 183 Similarly, since the velocity is an anti-derivative of the acceleration function a(t), we have Z t v(t) = v(t0 ) + a(u)du. t0 EXAMPLE 9.5 Suppose an object is acted upon by a constant force F . Find v(t) and s(t). By Newton’s law F = ma, so the acceleration is F/m, where m is the mass of the object. Then we first have Z t v(t) = v(t0 ) + t0 ¯t F F F ¯¯ du = v0 + u¯ = v0 + (t − t0 ), m m t0 m using the usual convention v0 = v(t0 ). Then Z tµ s(t) = s(t0 ) + t0 ¯t ¶ ¯ F F 2 ¯ (u − t0 ) )¯ v0 + (u − t0 ) du = s0 + (v0 u + m 2m t0 = s0 + v0 (t − t0 ) + F (t − t0 )2 . 2m For instance, when F/m = −g is the constant of gravitational acceleration, then this is the falling body formula (if we neglect air resistance) familiar from elementary physics: g s0 + v0 (t − t0 ) − (t − t0 )2 , 2 or in the common case that t0 = 0, g s0 + v 0 t − t 2 . 2 Recall that the integral of the velocity function gives the net distance traveled. If you want to know the total distance traveled, you must find out where the velocity function crosses the t-axis, integrate separately over the time intervals when v(t) is positive and when v(t) is negative, and add up the absolute values of the different integrals. For example, if an object is thrown straight upward at 19.6 m/sec, its velocity function is v(t) = −9.8t + 19.6, using g = 9.8 m/sec for the force of gravity. This is a straight line which is positive for t < 2 and negative for t > 2. The net distance traveled in the first 4 seconds is thus Z 4 (−9.8t + 19.6)dt = 0, 0 184 Chapter 9 Applications of Integration while the total distance traveled in the first 4 seconds is ¯Z 4 ¯ Z 2 ¯ ¯ ¯ (−9.8t + 19.6)dt + ¯ (−9.8t + 19.6)dt¯¯ = 19.6 + | − 19.6| = 39.2 0 2 meters, 19.6 meters up and 19.6 meters down. EXAMPLE 9.6 The acceleration of an object is given by a(t) = cos(πt), and its velocity at time t = 0 is 1/(2π). Find both the net and the total distance traveled in the first 1.5 seconds. We compute ¯t Z t ¯ ¢ 1 ¡1 1 1 + sin(πu)¯¯ = + sin(πt) . v(t) = v(0) + cos(πu)du = 2π π π 2 0 0 The net distance traveled is then ¶ Z 3/2 µ 1 1 s(3/2) − s(0) = + sin(πt) dt π 2 0 µ ¶¯3/2 ¯ 3 1 1 1 t = = − cos(πt) ¯¯ + 2 ≈ 0.340 meters. π 2 π 4π π 0 To find the total distance traveled, we need to know when (0.5 + sin(πt)) is positive and when it is negative. This function is 0 when sin(πt) is −0.5, i.e., when πt = 7π/6, 11π/6, etc. The value πt = 7π/6, i.e., t = 7/6, is the only value in the range 0 ≤ t ≤ 1.5. Since v(t) > 0 for t < 7/6 and v(t) < 0 for t > 7/6, the total distance traveled is ¶ ¶ ¯ Z 7/6 µ ¯Z 3/2 1 µ 1 1 1 ¯ ¯ + sin(πt) dt + ¯ + sin(πt) dt¯ π 2 2 0 7/6 π µ ¶ ¯ 1 1 1 ¯¯ 3 7 1 1 7 ¯ + cos(7π/6) + + ¯ − + cos(7π/6)¯ = π 12 π π π 4 12 π Ã ! √ √ 1 7 1 3 1 1 ¯¯ 3 7 1 3 ¯¯ = + + + ¯ − + .¯ ≈ 0.409 meters. π 12 π 2 π π 4 12 π 2 9.3 Volume 185 Exercises For each velocity function find both the net distance and the total distance traveled during the indicated time interval (graph v(t) to determine when it’s positive and when it’s negative): 1. v = cos(πt), 0 ≤ t ≤ 2.5 ⇒ 2. v = −9.8t + 49, 0 ≤ t ≤ 10 ⇒ 3. v = 3(t − 3)(t − 1), 0 ≤ t ≤ 5 ⇒ 4. v = sin(πt/3) − t, 0 ≤ t ≤ 1 ⇒ 5. An object is shot upwards from ground level with an initial velocity of 2 meters per second; it is subject only to the force of gravity (no air resistance). Find its maximum altitude and the time at which it hits the ground. ⇒ 6. An object is shot upwards from ground level with an initial velocity of 3 meters per second; it is subject only to the force of gravity (no air resistance). Find its maximum altitude and the time at which it hits the ground. ⇒ 7. An object is shot upwards from ground level with an initial velocity of 100 meters per second; it is subject only to the force of gravity (no air resistance). Find its maximum altitude and the time at which it hits the ground. ⇒ 8. An object moves along a straight line with acceleration given by a(t) = − cos(t), and s(0) = 1 and v(0) = 0. Find the maximum distance the object travels from zero, and find its maximum speed. Describe the motion of the object. ⇒ 9. An object moves along a straight line with acceleration given by a(t) = sin(πt). Assume that when t = 0, s(t) = v(t) = 0. Find s(t), v(t), and the maximum speed of the object. Describe the motion of the object. ⇒ 10. An object moves along a straight line with acceleration given by a(t) = 1 + sin(πt). Assume that when t = 0, s(t) = v(t) = 0. Find s(t) and v(t). ⇒ 11. An object moves along a straight line with acceleration given by a(t) = 1 − sin(πt). Assume that when t = 0, s(t) = v(t) = 0. Find s(t) and v(t). ⇒ 9.3 Volume We have seen how to compute certain areas by using integration; some volumes may also be computed by evaluating an integral. Generally, the volumes that we can compute this way have cross-sections that are easy to describe. EXAMPLE 9.7 Find the volume of a pyramid with a square base that is 20 meters tall and 20 meters on a side at the base. As with most of our applications of integration, we begin by asking how we might approximate the volume. Since we can easily compute the volume of a rectangular prism (that is, a “box”), we will use some boxes to approximate the volume of the pyramid, as shown in figure 9.6: on the left is a cross-sectional view, on the right is a 3D view of part of the pyramid with some of the boxes used to approximate the volume. 186 Chapter 9 Applications of Integration .. ..... .. ... ... ..... . . ... ... ... .. ... ... ... ... . ... . . . ... . . . ... .. . ... . . . ... . . . ... .. . ... . . ... . . . ... . .. ... . . . ... . . . ... . .. ... . . . ... . . . ... . .. ... . . ... . . . ... . . ... .. . . ... . . . ... . . ... .. . ... . . . . ... . . i ... .. . ... . . . ... . . . ... .. . ... . . . ... . . . ... .. . ... . . . ... . . . ... .. . ... . . ... . . ... .. . y → xi Figure 9.6 Volume of a pyramid approximated by rectangular prisms. (JA) Each box has volume of the form (2xi )(2xi )∆y. Unfortunately, there are two variables here; fortunately, we can write x in terms of y: x = 10 − y/2 or xi = 10 − yi /2. Then the total volume is approximately n−1 X 4(10 − yi /2)2 ∆y i=0 and in the limit we get the volume is the value of an integral: Z 20 Z 2 4(10 − y/2) dy = 0 0 20 ¯20 (20 − y)3 ¯¯ 203 8000 03 (20 − y) dy = − − − = . = − ¯ 3 3 3 3 0 2 As you may know, the volume of a pyramid is (1/3)(height)(area of base) = (1/3)(20)(400), which agrees with our answer. EXAMPLE 9.8 The base of a solid is the region between f (x) = x2 − 1 and g(x) = −x2 + 1, and its cross-sections perpendicular to the x-axis are equilateral triangles. See figure 9.7; the solid has been truncated to show a triangular cross-section above x = 1/2. Find the volume of the solid. A cross-section at a value xi on the x-axis is a triangle with base 2(1 − x2i ) and height √ 3(1 − x2i ), so the area of the cross-section is √ 1 (base)(height) = (1 − x2i ) 3(1 − x2i ), 2 9.3 Volume 187 1 ........ .............. .................... ....... ...... ...... ...... . . . . . ..... .... ..... ..... . ..... . . .. . ..... . . .. ..... . . . ... ... . ... . . . ... . . ... . . . ... . . . ... . . ... . . . ... . . ... . . . ... . . . ... . . . ... . . . ... . . . ... . . . ... ..... .. ... ... ... ... . ... . . ... ... ... ... ... ... ... . . . ... ... ... ... ... ... ... . . ... .. ... ... ... ... ..... .... ..... . . . ..... ..... ..... ..... ..... .... ...... ..... . . . ...... . . .... ....... ....... ............ ........................ −1 1 −1 Figure 9.7 ... ... ... ... ... ... ... ... ... ... ... ........ ........... ... ..... .... ... ..... ... ... ..... ... . . . . ... .. ... . . . . ..... . ... .................... ... ... ... ... ... ... Solid with equilateral triangles as cross-sections. (JA) Figure 9.8 A solid of rotation. and the volume of a thin “slab” is then √ (1 − x2i ) 3(1 − x2i )∆x. Thus the total volume is Z 1 −1 √ 3(1 − x2i )2 dx = 16 √ 3. 15 One easy way to get “nice” cross-sections is by rotating a plane figure around a line. For example, in figure 9.8 we see a plane region under a curve and between two vertical lines; then the result of rotating this around the x-axis; then a typical circular cross-section. 188 Chapter 9 Applications of Integration Of course a real “slice” of this figure will not have straight sides, but we can approximate the volume of the slice by a cylinder or disk with circular top and bottom and straight sides; the volume of this disk will have the form πr2 ∆x. As long as we can write r in terms of x we can compute the volume by an integral. EXAMPLE 9.9 Find the volume of a right circular cone with base radius 10 and height 20. (A right circular cone is one with a cicular base and with the tip of the cone directly over the center of the base.) We can view this cone as produced by the rotation of the line y = x/2 rotated about the x-axis, as indicated in figure 9.9. . ........ ....... ....... . . . . . . . ........ ....... ....... ....... . . . . . . . ....... ....... ........ ....... . . . . . . .. ....... ....... ....... ....... . . . . . . ... ....... ....... ....... 0 Figure 9.9 20 A region that generates a cone; approximating the volume by circular disks. (JA) At a particular point on the x-axis, say xi , the radius of the resulting cone is the y-coordinate of the corresponding point on the line, namely yi = xi /2. Thus the total volume is approximately n−1 X π(xi /2)2 dx i=0 and the exact volume is Z 20 π 0 π 203 2000π x2 dx = = . 4 4 3 3 Note that we can instead do the calculation with a generic height and radius: Z h 2 r πr2 h3 πr2 h π 2 x2 dx = 2 = , h h 3 3 0 9.3 Volume 189 giving us the usual formula for the volume of a cone. EXAMPLE 9.10 Find the volume of the object generated when the area between y = x2 and y = x is rotated around the x-axis. This solid has a “hole” in the middle; we can compute the volume by subtracting the volume of the hole from the volume enclosed by the outer surface of the solid. In figure 9.10 we show the region that is rotated, the resulting solid with the front half cut away, the cone that forms the outer surface, the horn-shaped hole, and a cross-section perpendicular to the x-axis. 1 0 .... ...... ....... .......... . . . . ..... .. ..... ... .... .. ..... ..... . . . . .. ..... ..... ..... ..... .... ... . . . . . . . .. ..... .... ..... .... ..... .... . ..... . . . . . . .... ..... ..... ..... ..... ...... ..... ...... . . . . . . . . . ..... ....... .................... 0 Figure 9.10 1 Solid with a hole, showing the outer cone and the shape to be removed to form the hole. (JA) We have already computed the volume of a cone; in this case it is π/3. At a particular value of x, say xi , the cross-section of the horn is a circle with radius x2i , so the volume of 190 Chapter 9 Applications of Integration the horn is Z 1 Z 2 2 π(x ) dx = 0 0 1 1 πx4 dx = π , 5 so the desired volume is π/3 − π/5 = 2π/15. As with the area between curves, there is an alternate approach that computes the desired volume “all at once” by approximating the volume of the actual solid. We can approximate the volume of a slice of the solid with a washer-shaped volume, as indicated in figure 9.10. The volume of such a washer is the area of the face times the thickness. The thickness, as usual, is ∆x, while the area of the face is the area of the outer circle minus the area of the inner circle, say πR2 − πr2 . In the present example, at a particular xi , the radius R is xi and r is x2i . Hence, the whole volume is µ µ 3 ¶¯1 ¶ Z 1 x5 ¯¯ 1 1 2π x 2 4 πx − πx dx = π =π − − = . ¯ 3 5 0 3 5 15 0 Of course, what we have done here is exactly the same calculation as before, except we have in effect recomputed the volume of the outer cone. Suppose the region between f (x) = x + 1 and g(x) = (x − 1)2 is rotated around the y-axis; see figure 9.11. It is possible, but inconvenient, to compute the volume of the resulting solid by the method we have used so far. The problem is that there are two “kinds” of typical rectangles: those that go from the line to the parabola and those that touch the parabola on both ends. To compute the volume using this approach, we need to break the problem into two parts and compute two integrals: Z 1 Z 4 65 27 8 √ 2 √ 2 √ π. π (1 + y) − (1 − y) dy + π (1 + y)2 − (y − 1)2 = π + π = 3 6 2 0 1 If instead we consider a typical vertical rectangle, but still rotate around the y-axis, we get a thin “shell” instead of a thin “washer”. If we add up the volume of such thin shells we will get an approximation to the true volume. What is the volume of such a shell? Consider the shell at xi . Imagine that we cut the shell vertically in one place and “unroll” it into a thin, flat sheet. This sheet will be almost a rectangular prism that is ∆x thick, f (xi ) − g(xi ) tall, and 2πxi wide (namely, the circumference of the shell before it was unrolled). The volume will then be approximately the volume of a rectangular prism with these dimensions: 2πxi (f (xi ) − g(xi ))∆x. If we add these up and take the limit as usual, we get the integral Z 3 Z 3 27 2πx(f (x) − g(x)) dx = 2πx(x + 1 − (x − 1)2 ) dx = π. 2 0 0 9.3 Volume 191 Not only does this accomplish the task with only one integral, the integral is somewhat easier than those in the previous calculation. Things are not always so neat, but it is often the case that one of the two methods will be simpler than the other, so it is worth considering both before starting to do calculations. 4 3 2 1 0 4 ...... ...... ..... ..... . . . . ..... .. .... .... ..... .. ..... . . . . ... .... ... ..... . . . . . .... ... ..... ... ..... ... ..... . . . . . . . ..... ... ..... ... .... ..... ... . . . . . . . . ..... ... ..... ... ..... ... .... . . . . . ... .. ... ... ... ... ... ... ... . .. ..... ..... ..... ....... ........... ........... 0 1 2 3 2 1 0 3 ...... ...... ..... ..... . . . . ..... .. .... .... ..... .. ..... . . . . ... .... ... ..... . . . . . .... ... ..... ... ..... ... ..... . . . . . . . ..... ... ..... ... .... ..... ... . . . . . . . . ..... ... ..... ... ..... ... .... . . . . . ... .. ... ... ... ... ... ... ... . .. ..... ..... ..... ....... ........... ........... 0 Figure 9.11 1 2 3 Computing volumes with “shells”. (JA) EXAMPLE 9.11 Suppose the area under y = −x2 + 1 between x = 0 and x = 1 is rotated around the x-axis. Find the volume by both methods. Z 1 8 Disk method: π. π(1 − x2 )2 dx = 15 0 Z 1 p 8 Shell method: 2πy 1 − y dy = π. 15 0 Exercises Z 1 1. Verify that π Z (1 + y)2 − (1 − Z 4 (1 + 1 √ 2 8 65 27 y) − (y − 1)2 = π + π= π. 3 6 2 27 π. 2πx(x + 1 − (x − 1)2 ) dx = 2 0 1 3. Verify that π(1 − x2 )2 dx = 0 Z √ 2 y) dy + π 0 3 2. Verify that Z √ 1 p 8 π. 15 8 π. 15 0 5. Use integration to find the volume of the solid obtained by revolving the region bounded by x + y = 2 and the x and y axes around the x-axis. ⇒ 4. Verify that 2πy 1 − y dy = 6. Find the volume of the solid obtained by revolving the region bounded by y = x − x2 and the x-axis around the x-axis. ⇒ 192 Chapter 9 Applications of Integration 7. Find the volume of the solid obtained by revolving the region bounded by y = y-axis, and the lines y = 1 and x = π/2 around the x-axis. ⇒ √ sin x, the 8. Let S be the region of the xy-plane bounded above by the curve x3 y = 64, below by the line y = 1, on the left by the line x = 2, and on the right by the line x = 4. Find the volume of the solid obtained by rotating S around (a) the x-axis, (b) the line y = 1, (c) the y-axis, (d) the line x = 2. ⇒ 9. The equation x2 /9 + y 2 /4 = 1 describes an ellipse. Find the volume of the solid obtained by rotating the ellipse around the x-axis and also around the y-axis. These solids are called ellipsoids; one is vaguely rugby-ball shaped, one is sort of flying-saucer shaped, or perhaps squished-beach-ball-shaped. ⇒ Figure 9.12 Ellipsoids. (JA) 10. Use integration to compute the volume of a sphere of radius r. You should of course get the well-known formula 4πr 3 /3. 11. A hemispheric bowl of radius r contains water to a depth h. Find the volume of water in the bowl. ⇒ 12. The base of a tetrahedron (a triangular pyramid) of height h is an equilateral triangle of side s. Its cross-sections perpendicular to an altitude are equilateral triangles. Express its volume V as an integral, and find a formula for V in terms of h and s. Verify that your answer is (1/3)(area of base)(height). 13. The base of a solid is the region between f (x) = cos x and g(x) = − cos x, −π/2 ≤ x ≤ π/2, and its cross-sections perpendicular to the x-axis are squares. Find the volume of the solid. ⇒ 9.4 Average value of a function The average of some finite set of values is a familiar concept. If, for example, the class scores on a quiz are 10, 9, 10, 8, 7, 5, 7, 6, 3, 2, 7, 8, then the average score is the sum of these numbers divided by the size of the class: average score = 10 + 9 + 10 + 8 + 7 + 5 + 7 + 6 + 3 + 2 + 7 + 8 82 = ≈ 6.83. 12 12 Suppose that between t = 0 and t = 1 the speed of an object is sin(πt). What is the average speed of the object over that time? The question sounds as if it must make sense, 9.4 Average value of a function 193 yet we can’t merely add up some number of speeds and divide, since the speed is changing continuously over the time interval. To make sense of “average” in this context, we fall back on the idea of approximation. Consider the speed of the object at tenth of a second intervals: sin 0, sin(0.1π), sin(0.2π), sin(0.3π),. . . , sin(0.9π). The average speed “should” be fairly close to the average of these ten speeds: 9 1 1 X sin(πi/10) ≈ 6.3 = 0.63. 10 i=0 10 Of course, if we compute more speeds at more times, the average of these speeds should be closer to the “real” average. If we take the average of n speeds at evenly spaced times, we get: n−1 1X sin(πi/n). n i=0 Here the individual times are ti = i/n, so rewriting slightly we have n−1 1X sin(πti ). n i=0 This is almost the sort of sum that we know turns into an integral; what’s apparently missing is ∆t—but in fact, ∆t = 1/n, the length of each subinterval. So rewriting again: n−1 X n−1 X 1 sin(πti ) = sin(πti )∆t. n i=0 i=0 Now this has exactly the right form, so that in the limit we get Z average speed = 0 1 ¯1 cos(πt) ¯¯ cos(π) cos(0) 2 sin(πt) dt = − + = ≈ 0.6366 ≈ 0.64. =− ¯ π π π π 0 It’s not entirely obvious from this one simple example how to compute such an average in general. Let’s look at a somewhat more complicated case. Suppose that the velocity of an object is 16t2 + 5 feet per second. What is the average velocity between t = 1 and t = 3? Again we set up an approximation to the average: n−1 1X 16t2i + 5, n i=0 194 Chapter 9 Applications of Integration where the values ti are evenly spaced times between 1 and 3. Once again we are “missing” ∆t, and this time 1/n is not the correct value. What is ∆t in general? It is the length of a subinterval; in this case we take the interval [1, 3] and divide it into n subintervals, so each has length (3 − 1)/n = 2/n = ∆t. Now with the usual “multiply and divide by the same thing” trick we can rewrite the sum: n−1 n−1 n−1 n−1 1X 1X 1X 1 X 3−1 2 2 2 2 16ti + 5 = (16ti + 5) = (16ti + 5) = (16t2i + 5)∆t. n i=0 3 − 1 i=0 n 2 i=0 n 2 i=0 In the limit this becomes 1 2 Z 3 16t2 + 5 dt = 1 1 446 223 = . 2 3 3 Does this seem reasonable? Let’s picture it: in figure 9.13 is the velocity function together with the horizontal line y = 223/3 ≈ 74.3. Certainly the height of the horizontal line looks at least plausible for the average height of the curve. 150 125 100 75 50 25 0 . ... .... ..... . . . ..... .... .... .... . . . ..... .... .... ..... . . . .... ..... .... ..... . . . . ..... ..... ..... ..... . . . . ..... ..... ..... ..... . . . . .... ..... ...... ...... . . . . . . ...... ..... ...... ...... . . . . . . ....... ...... ....... ....... . . . . . . ........ ......... .......... ........... . . . . . . . . . . . . .............. .................................... 0 1 Figure 9.13 2 3 Average velocity. Here’s another way to interpret “average” that may make our computation appear even more reasonable. The object of our example goes a certain distance between t = 1 and t = 3. If instead the object were to travel at the average speed over the same time, it would go the same distance. At an average speed of 223/3 feet per second for two seconds the object would go 446/3 feet. How far does it actually go? We know how to compute 9.5 this: Z Z 3 v(t) dt = 1 3 16t2 + 5 dt = 1 Work 195 446 . 3 So now we see that another interpretation of the calculation 1 2 Z 1 3 16t2 + 5 dt = 1 446 223 = 2 3 3 is: total distance traveled divided by the time in transit, namely, average speed. It may also be helpful to interpret the average geometrically. The area under the velocity curve is Z 3 446 16t2 + 5 dt = . 3 1 The area under y = 223/3 over the same interval [1, 3] is simply the area of a rectangle that is 2 by 223/3 with area 446/3. So the average height of a function is the height of the horizontal line that produces the same area over the given interval. Exercises 1. Find the average height of cos x over the intervals [0, π/2], [−π/2, π/2], and [0, 2π]. ⇒ 2. Find the average height of x2 over the interval [−2, 2]. ⇒ 3. Find the average height of 1/x2 over the interval [1, A]. ⇒ √ 4. Find the average height of 1 − x2 over the interval [−1, 1]. ⇒ 5. An object moves with velocity v(t) = −t2 + 1 feet per second between t = 0 and t = 2. Find the average velocity and the average speed of the object between t = 0 and t = 2. ⇒ 6. The observation deck on the 102nd floor of the Empire State Building is 1,224 feet above the ground. If a steel ball is dropped from the observation deck its velocity at time t is approximately v(t) = −32t feet per second. Find the average speed between the time it is dropped and the time it hits the ground, and find its speed when it hits the ground. ⇒ 9.5 Work A fundamental concept in classical physics is work: If an object is moved in a straight line against a force F for a distance s the work done is W = F s. EXAMPLE 9.12 How much work is done in lifting a 10 pound weight vertically a distance of 5 feet? The force due to gravity on a 10 pound weight is 10 pounds at the surface of the earth, and it does not change appreciably over 5 feet. The work done is W = 10 · 5 = 50 foot-pounds. 196 Chapter 9 Applications of Integration In reality few situations are so simple. The force might not be constant over the range of motion, as in the next example. EXAMPLE 9.13 How much work is done in lifting a 10 pound weight from the surface of the earth to an orbit 100 miles above the surface? Over 100 miles the force due to gravity does change significantly, so we need to take this into account. The force exerted on a 10 pound weight at a distance r from the center of the earth is F = k/r2 and by definition it is 10 when r is the radius of the earth (we assume the earth is a sphere). How can we approximate the work done? We divide the path from the surface to orbit into n small subpaths. On each subpath the force due to gravity is roughly constant, with value k/ri2 at distance ri . The work to raise the object from ri to ri+1 is thus approximately k/ri2 ∆r and the total work is approximately n−1 X i=0 or in the limit Z k ∆r, ri2 r1 W = r0 k dr, r2 where r0 is the radius of the earth and r1 is r0 plus 100 miles. The work is ¯r Z r1 k k ¯¯ 1 k k W = dr = − + . = − 2 r ¯r0 r1 r0 r0 r Using r0 = 20925525 feet we have r1 = 21453525. The force on the 10 pound weight at the surface of the earth is 10 pounds, so 10 = k/209255252 , giving k = 4378775965256250. Then k k 491052320000 − + = ≈ 5150052 foot-pounds. r1 r0 95349 Note that if we assume the force due to gravity is 10 pounds over the whole distance we would calculate the work as 10(r1 − r0 ) = 10 · 100 · 5280 = 5280000, somewhat higher since we don’t account for the weakening of the gravitational force. EXAMPLE 9.14 How much work is done in lifting a 10 kilogram object from the surface of the earth to a distance D from the center of the earth? This is the same problem as before in different units, and we are not specifying a value for D. As before Z D W = r0 ¯D k ¯¯ k k k dr = − ¯ = − + . 2 r r r0 D r0 9.5 Work 197 While “weight in pounds” is a measure of force, “weight in kilograms” is a measure of mass. To convert to force we need to use Newton’s law F = ma. At the surface of the earth the acceleration due to gravity is approximately 9.8 meters per second squared, so the force is F = 10 · 9.8 = 98. The units here are “kilogram-meters per second squared” or “kg m/s2 ”, also known as a Newton (N), so F = 98 N. The radius of the earth is approximately 6378.1 kilometers or 6378100 meters. Since the force due to gravity obeys an inverse square law, F = k/r2 and 98 = k/63781002 , k = 3.986655642 · 1015 and W =− k + 6.250538000 · 108 D Newton-meters. As D increases W of course gets larger, since the quantity being subtracted, −k/D, gets smaller. But note that the work W will never exceed 6.250538000 · 108 , and in fact will approach this value as D gets larger. In short, with a finite amount of work, namely 6.250538000 · 108 N-m, we can lift the 10 kilogram object as far as we wish from earth. Next is an example in which the force is constant, but there are many objects moving different distances. EXAMPLE 9.15 Suppose that a water tank is shaped like a right circular cone with the tip at the bottom, and has height 10 meters and radius 2 meters at the top. If the tank is full, how much work is required to pump all the water out over the top? Here we have a large number of atoms of water that must be lifted different distances to get to the top of the tank. Fortunately, we don’t really have to deal with individual atoms—we can consider all the atoms at a given depth together. To approximate the work, we can divide the water in the tank into horizontal sections, approximate the volume of water in a section by a thin disk, and compute the amount of work required to lift each disk to the top of the tank. As usual, we take the limit as the sections get thinner and thinner to get the total work. At depth h the circular cross-section through the tank has radius r = (10 − h)/5, by similar triangles, and area π(10 − h)2 /25. A section of the tank at depth h thus has volume approximately π(10 − h)2 /25∆h and so contains σπ(10 − h)2 /25∆h kilograms of water, where σ is the density of water in kilograms per cubic meter; σ ≈ 1000. The force due to gravity on this much water is 9.8σπ(10 − h)2 /25∆h, and finally, this section of water must be lifted a distance h, which requires h9.8σπ(10 − h)2 /25∆h Newton-meters of work. The total work is therefore 9.8σπ W = 25 Z 10 0 h(10 − h)2 dh = 980000 π ≈ 1026254 3 Newton-meters. 198 Chapter 9 Applications of Integration .................... 2 . .................... . ............................................................... ... . ... ... ... .. ... ... . ... .. ... ... ... .. ... .... . ... ... ... ... ... . . ... ... ... .. ... ... ... . . ... .. ... .. ... ... ... ... . ... .. ... .. ... ... ... .... ... . ... .. ... ... ... .... ... .. ... ... ... .. ... ... ...... ... ↑ | h | ↓ Figure 9.14 ↑ | | | | | 10 | | | | | ↓ Cross-section of a conical water tank. A spring has a “natural length,” its length if nothing is stretching or compressing it. If the spring is either stretched or compressed the spring provides an opposing force; according to Hooke’s Law the magnitude of this force is proportional to the distance the spring has been stretched or compressed: F = kx. The constant of proportionality, k, of course depends on the spring. Note that x here represents the change in length from the natural length. EXAMPLE 9.16 Suppose k = 5 for a given spring that has a natural length of 0.1 meters. Suppose a force is applied that compresses the spring to length 0.08. What is the magnitude of the force? Assuming that the constant k has appropriate dimensions (namely, kg/s2 ), the force is 5(0.1 − 0.08) = 5(0.02) = 0.1 Newtons. EXAMPLE 9.17 How much work is done in compressing the spring in the previous example from its natural length to 0.08 meters? From 0.08 meters to 0.05 meters? How much work is done to stretch the spring from 0.1 meters to 0.15 meters? We can approximate the work by dividing the distance that the spring is compressed into small subintervals. Then the force exerted by the spring is approximately constant over the subinterval, so the work required to compress the spring from xi to xi+1 is approximately 5(xi − 0.1)∆x. The total work is approximately n−1 X i=0 5(xi − 0.1)∆x 9.5 Work 199 and in the limit Z 0.08 W = 0.1 ¯0.08 5(0.08 − 0.1)2 5(0.1 − 0.1)2 1 5(x − 0.1)2 ¯¯ 5(x − 0.1) dx = = − = N-m. ¯ 2 2 2 1000 0.1 The other values we seek simply use different limits. To compress the spring from 0.08 meters to 0.05 meters takes Z 0.05 W = 0.08 ¯0.05 5(0.05 − 0.1)2 5(0.08 − 0.1)2 21 5x2 ¯¯ = − = 5(x − 0.1) dx = ¯ 2 0.08 2 2 4000 N-m and to stretch the spring from 0.1 meters to 0.15 meters requires Z 0.15 W = 0.1 ¯0.15 5x2 ¯¯ 5(0.15 − 0.1)2 5(0.1 − 0.1)2 1 5(x − 0.1) dx = = − = ¯ 2 0.1 2 2 160 N-m. Exercises 1. How much work is done in lifting a 100 kilogram weight from the surface of the earth to an orbit 35,786 kilometers above the surface of the earth? ⇒ 2. How much work is done in lifting a 100 kilogram weight from an orbit 1000 kilometers above the surface of the earth to an orbit 35,786 kilometers above the surface of the earth? ⇒ 3. A water tank has the shape of a cylinder with radius r = 1 meter and height 10 meters. If the depth of the water is 5 meters, how much work is required to pump all the water out the top of the tank? ⇒ 4. Suppose the tank of the previous problem is lying on its side, so that the circular ends are vertical, and that it has the same amount of water as before. How much work is required to pump the water out the top of the tank (which is now 2 meters above the bottom of the tank)? ⇒ 5. A water tank has the shape of the bottom half of a sphere with radius r = 1 meter. If the tank is full, how much work is required to pump all the water out the top of the tank? ⇒ 6. A spring has constant k = 10 kg/s2 . How much work is done in compressing it 1/10 meter from its natural length? ⇒ 7. A force of 2 Newtons will compress a spring from 1 meter (its natural length) to 0.8 meters. How much work is required to stretch the spring from 1.1 meters to 1.5 meters? ⇒ 8. A 20 meter long steel cable has density 2 kilograms per meter, and is hanging straight down. How much work is required to lift the entire cable to the height of its top end? ⇒ 9. The cable in the previous problem has a 100 kilogram bucket of concrete attached to its lower end. How much work is required to lift the entire cable and bucket to the height of its top end? ⇒ 200 Chapter 9 Applications of Integration 9.6 Center of Mass Suppose a beam is 10 meters long, and that there are three weights on the beam: a 10 kilogram weight 3 meters from the left end, a 5 kilogram weight 6 meters from the left end, and a 4 kilogram weight 8 meters from the left end. Where should a fulcrum be placed so that the beam balances? Let’s assign a scale to the beam, from 0 at the left end to 10 at the right, so that we can denote locations on the beam simply as x coordinates; the weights are at x = 3, x = 6, and x = 8. Suppose to begin with that the fulcrum is placed at x = 5. What will happen? Each weight applies a force to the beam that tends to rotate it around the fulcrum; this effect is measured by a quantity called torque, proportional to the mass times the distance from the fulcrum. Of course, weights on different sides of the fulcrum rotate the beam in opposite directions. We can distinguish this by using a signed distance in the formula for torque. So with the fulcrum at 5, the torques induced by the three weights will be proportional to (3 − 5)10 = −20, (6 − 5)5 = 5, and (8 − 5)4 = 12. For the beam to balance, the sum of the torques must be zero; since the sum is −20 + 5 + 12 = −3, the beam rotates counter-clockwise, and to get the beam to balance we need to move the fulcrum to the left. To calculate exactly where the fulcrum should be, we let x̄ denote the location of the fulcrum when the beam is in balance. The total torque on the beam is then (3 − x̄)10 + (6 − x̄)5 + (8 − x̄)4 = 92 − 19x̄. Since the beam balances at x̄ it must be that 92 − 19x̄ = 0 or x̄ = 92/19 ≈ 4.84, that is, the fulcrum should be placed at x = 92/19 to balance the beam. Now suppose that we have a beam with varying density—some portions of the beam contain more mass than other portions of the same size. We want to figure out where to put the fulcrum so that the beam balances. EXAMPLE 9.18 Suppose the beam is 10 meters long and that the density is 1 + x kilograms per meter at location x on the beam. To approximate the solution, we can think of the beam as a sequence of weights “on” a beam. For example, we can think of the portion of the beam between x = 0 and x = 1 as a weight sitting at x = 0, the portion between x = 1 and x = 2 as a weight sitting at x = 1, and so on. We then approximate the mass of the weights by assuming that each portion of the beam has constant density. So the mass of the first weight is approximated as m0 = (1 + 0)1 = 1 kilograms, namely, (1 + 0) kilograms per meter times 1 meter. The second weight is m1 = (1 + 1)1 = 2 kilograms, and so on to the tenth weight with m9 = (1 + 9)1 = 9 kilograms. So in this case the total torque is (0 − x̄)m0 + (1 − x̄)m1 + · · · + (9 − x̄)m9 = (0 − x̄)1 + (1 − x̄)2 + · · · + (9 − x̄)9. 9.6 Center of Mass 201 If we set this to zero and solve for x̄ we get x̄ = 6. In general, if we divide the beam into n portions, the mass of weight number i will be mi = (1 + xi )(xi+1 − xi ) = (1 + xi )∆x and the torque induced by weight number i will be (xi − x̄)mi = (xi − x̄)(1 + xi )∆x. The total torque is then (x0 − x̄)(1 + x0 )∆x + (x1 − x̄)(1 + x1 )∆x + · · · + (xn−1 − x̄)(1 + xn−1 )∆x = n−1 X xi (1 + xi )∆x − i=0 = n−1 X n−1 X x̄(1 + xi )∆x i=0 xi (1 + xi )∆x − x̄ i=0 n−1 X (1 + xi )∆x. i=0 If we set this equal to zero and solve for x̄ we get an approximation to the balance point of the beam: 0= n−1 X xi (1 + xi )∆x − x̄ i=0 x̄ n−1 X (1 + xi )∆x = i=0 n−1 X n−1 X (1 + xi )∆x i=0 xi (1 + xi )∆x i=0 n−1 X x̄ = xi (1 + xi )∆x i=0 n−1 X . (1 + xi )∆x i=0 The numerator of this fraction is called the moment of the system around zero, and the denominator has a very familiar interpretation. Consider one term of the sum in the denominator: (1 + xi )∆x. This is the density near xi times a short length, ∆x, which in other words is approximately the mass of the beam between xi and xi+1 . When we add these up we get approximately the mass of the beam. Now each of the sums in the fraction has the right form to turn into an integral, which in turn gives us the exact value of x̄: Z 10 x(1 + x) dx x̄ = Z0 . 10 (1 + x) dx 0 202 Chapter 9 Applications of Integration The numerator is the moment of the system: Z 10 Z 10 1150 x(1 + x) dx = x + x2 dx = , 3 0 0 and the denominator is the mass of the beam: Z 10 (1 + x) dx = 60, 0 and the balance point, officially called the center of mass, is x̄ = 115 1150 1 = ≈ 6.39. 3 60 18 It should be apparent that there was nothing special about the density function σ(x) = 1 + x or the length of the beam, or even that the left end of the beam is at the origin. In general, if the density of the beam is σ(x) and the beam covers the interval [a, b], the moment of the beam around zero is Z b M0 = xσ(x) dx a and the total mass of the beam is Z M= b σ(x) dx a and the center of mass is at x̄ = M0 . M EXAMPLE 9.19 Suppose a beam lies on the x-axis between 20 and 30, and has density function σ(x) = x − 19. Find the center of mass. This is the same as the previous example except that the beam has been moved. Note that the density at the left end is 20 − 19 = 1 and at the right end is 30 − 19 = 11, as before. Hence the center of mass must be at approximately 20 + 6.39 = 26.39. Let’s see how the calculation works out. ¯30 Z 30 Z 30 19x2 ¯¯ x3 4750 2 M0 = x(x − 19) dx = x − 19x dx = − = ¯ 3 2 20 3 20 20 ¯30 Z 30 ¯ x2 M= x − 19 dx = − 19x¯¯ = 60 2 20 20 4750 1 475 M0 = = ≈ 26.39. M 3 60 18 9.6 1 . ... ... ... . . . ... ... ... . (x̄, ȳ) . .. ... .. ... . . .. .... .... ..... . . . . .... ..... ...... ...... . . . . . . ....... ................. • 0 0 ............ .............................. ...... .............................. ........................ ...... .............................. ...... .............................. ...... .............................. ........................ ...... .............................. ...... .............................. ...... .............................. ...... .............................. ........................ ...... .............................. ...... .............................. ...... .............................. ........................ xi Figure 9.15 Center of Mass 203 mi ............................ ..... ............................ ..... 1 0 xi 1 Center of mass for a two dimensional plate. EXAMPLE 9.20 Suppose a flat plate of uniform density has the shape contained by y = x2 , y = 1, and x = 0, in the first quadrant. Find the center of mass. (Since the density is constant, the center of mass depends only on the shape of the plate, not the density, or in other words, this is a purely geometric quantity. In such a case the center of mass is called the centroid.) This is a two dimensional problem, but it can be solved as if it were two one dimensional problems: we need to find the x and y coordinates of the center of mass, x̄ and ȳ, and fortunately we can do these independently. Imagine looking at the plate edge on, from below the x-axis. The plate will appear to be a beam, and the mass of a short section of the “beam”, say between xi and xi+1 , is the mass of a strip of the plate between xi and xi+1 . See figure 9.15 showing the plate from above and as it appears edge on. Since the plate has uniform density we may as well assume that σ = 1. Then the mass of the plate between xi and xi+1 is approximately mi = σ(1 − x2i )∆x = (1 − x2i )∆x. Now we can compute the moment around the y-axis: Z 1 My = x(1 − x2 ) dx = 0 and the total mass Z M= 1 (1 − x2 ) dx = 0 and finally x̄ = 13 3 = . 42 8 2 3 1 4 204 Chapter 9 Applications of Integration Next we do the same thing to find ȳ. The mass of the plate between yi and yi+1 is √ approximately ni = y∆y, so Z 1 Mx = 0 and ȳ = 2 √ y y dy = 5 23 3 = , 52 5 since the total mass M is the same. The center of mass is shown in figure 9.15. EXAMPLE 9.21 Find the center of mass of a thin, uniform plate whose shape is the region between y = cos x and the x-axis between x = −π/2 and x = π/2. It is clear that x̄ = 0, but for practice let’s compute it anyway. We will need the total mass, so we compute it first: Z π/2 ¯π/2 ¯ M= cos x dx = sin x¯ = 2. −π/2 −π/2 The moment around the y-axis is Z ¯π/2 ¯ x cos x dx = cos x + x sin x¯ =0 π/2 My = −π/2 −π/2 and the moment around the x-axis is Z Mx = 0 1 ¯1 p ¯ 2 1 − y arcsin y π ¯ y · 2 arccos y dy = y 2 arccos y − + ¯ = . 2 2 ¯ 4 y 0 Thus x̄ = 0 , 2 ȳ = π ≈ 0.393. 8 9.7 Kinetic energy; improper integrals 205 Exercises 1. A beam 10 meters long has density σ(x) = x2 at distance x from the left end of the beam. Find the center of mass x̄. ⇒ 2. A beam 10 meters long has density σ(x) = sin(πx/10) at distance x from the left end of the beam. Find the center of mass x̄. ⇒ 3. A beam 4 meters long has density σ(x) = x3 at distance x from the left end of the beam. Find the center of mass x̄. ⇒ √ Z x 1 − x2 arcsin x 2 4. Verify that 2x arccos x dx = x arccos x − + + C. 2 2 5. A thin plate lies in the region between y = x2 and the x-axis between x = 1 and x = 2. Find the centroid. ⇒ 6. A thin plate fills the upper half of the unit circle x2 + y 2 = 1. Find the centroid. ⇒ 7. A thin plate lies in the region contained by y = x and y = x2 . Find the centroid. ⇒ 8. A thin plate lies in the region contained by y = 4 − x2 and the x-axis. Find the centroid. ⇒ 9. A thin plate lies in the region contained by y = x1/3 and the x-axis between x = 0 and x = 1. Find the centroid. ⇒ √ √ 10. A thin plate lies in the region contained by x + y = 1 and the axes in the first quadrant. Find the centroid. ⇒ 11. A thin plate lies in the region between the circle x2 + y 2 = 4 and the circle x2 + y 2 = 1, above the x-axis. Find the centroid. ⇒ 12. A thin plate lies in the region between the circle x2 + y 2 = 4 and the circle x2 + y 2 = 1 in the first quadrant. Find the centroid. ⇒ 9.7 Kinetic energy; improper integrals Recall example 9.14 in which we computed the work required to lift an object from the surface of the earth to some large distance D away. Since F = k/x2 we computed Z D k k k dx = − + . 2 D r0 r0 x We noticed that as D increases, k/D decreases to zero so that the amount of work increases to k/r0 . More precisely, Z D k k k k lim dx = lim − + = . 2 D→∞ r D→∞ x D r0 r0 0 We might reasonably describe this calculuation as computing the amount of work required to lift the object “to infinity,” and abbreviate the limit as Z D Z ∞ k k lim dx = dx. 2 2 D→∞ r x r0 x 0 206 Chapter 9 Applications of Integration Such an integral, with a limit of infinity, is called an improper integral. This is a bit unfortunate, since it’s not really “improper” to do this, nor is it really “an integral”—it is an abbreviation for the limit of a particular sort of integral. Nevertheless, we’re stuck with the term, and the operation itself is perfectly legitimate. It may at first seem odd that a finite amount of work is sufficient to lift an object to “infinity”, but sometimes surprising things are nevertheless true, and this is such a case. If the value of an improper integral is a finite number, as in this example, we say that the integral converges, and if not we say that the integral diverges. Here’s another way, perhaps even more surprising, to interpret this calculation. We know that one interpretation of Z D 1 dx x2 1 is the area under y = 1/x2 from x = 1 to x = D. Of course, as D increases this area increases. But since Z D 1 1 1 + , dx = − x2 D 1 1 while the area increases, it never exceeds 1, that is Z ∞ 1 1 dx = 1. x2 The area of the infinite region under y = 1/x2 from x = 1 to infinity is finite. Z ∞ 2 Consider a slightly different sort of improper integral: xe−x dx. There are two −∞ ways we might try to compute this. First, we could break it up into two more familiar integrals: Z ∞ Z 0 Z ∞ 2 −x2 −x2 xe dx = xe dx + xe−x dx. −∞ −∞ 0 Now we do these as before: Z 0 xe−x 2 −∞ and Z ∞ 0 ¯0 ¯ e 1 ¯ dx = lim − ¯ =− , D→∞ 2 ¯ 2 −x2 D xe−x 2 e−x dx = lim − D→∞ 2 2 ¯D ¯ 1 ¯ ¯ = , ¯ 2 0 9.7 so Z Kinetic energy; improper integrals 207 ∞ 2 1 1 xe−x dx = − + = 0. 2 2 −∞ Alternately, we might try Z ∞ −∞ Z 2 xe−x dx = lim D→∞ D xe−x 2 −D ¯D 2 2 ¯ e−D e e−D ¯ dx = lim − = lim − + = 0. ¯ D→∞ D→∞ 2 ¯ 2 2 −x2 −D So we get the same answer either way. This does not always happen; sometimes the second approach gives a finite number, while the first approach does not; the exercises provide Z ∞ examples. In general, we interpret the integral f (x) dx according to the first method: −∞ Z a Z ∞ both integrals f (x) dx and f (x) dx must converge for the original integral to −∞ a Z D converge. The second approach does turn out to be useful; when lim f (x) dx = L, D→∞ −D Z ∞ and L is finite, then L is called the Cauchy Principal Value of f (x) dx. −∞ Here’s a more concrete application of these ideas. We know that in general Z x1 W = F dx x0 is the work done against the force F in moving from x0 to x1 . In the case that F is the force of gravity exerted by the earth, it is customary to make F < 0 since the force is “downward.” This makes the work W negative when it should be positive, so typically the work in this case is defined as Z x1 W =− F dx. x0 Also, by Newton’s Law, F = ma(t). This means that Z x1 W =− ma(t) dx. x0 Unfortunately this integral is a bit problematic: a(t) is in terms of t, while the limits and the “dx” are in terms of x. But x and t are certainly related here: x = x(t) is the function that gives the position of the object at time t, so v = v(t) = dx/dt = x0 (t) is its velocity 208 Chapter 9 Applications of Integration and a(t) = v 0 (t) = x00 (t). We can use v = x0 (t) as a substitution to convert the integral from “dx” to “dv” in the usual way, with a bit of cleverness along the way: dv = x00 (t) dt = a(t) dt = a(t) dt dx dx dx dv = a(t) dx dt v dv = a(t) dx. Substituting in the integral: Z Z x1 W =− v1 ma(t) dx = − x0 v0 ¯v mv12 mv 2 ¯¯ 1 mv02 = − mv dv = − + . 2 ¯v0 2 2 You may recall seeing the expression mv 2 /2 in a physics course—it is called the kinetic energy of the object. We have shown here that the work done in moving the object from one place to another is the same as the change in kinetic energy. We know that the work required to move an object from the surface of the earth to infinity is Z ∞ k k W = dr = . 2 r0 r0 r At the surface of the earth the acceleration due to gravity is approximately 9.8 meters per second squared, so the force on an object of mass m is F = 9.8m. The radius of the earth is approximately 6378.1 kilometers or 6378100 meters. Since the force due to gravity obeys an inverse square law, F = k/r2 and 9.8m = k/63781002 , k = 398665564178000m and W = 62505380m. Now suppose that the initial velocity of the object, v0 , is just enough to get it to infinity, that is, just enough so that the object never slows to a stop, but so that its speed decreases to zero, i.e., so that v1 = 0. Then 62505380m = W = − so v0 = √ mv12 mv02 mv02 + = 2 2 2 125010760 ≈ 11181 meters per second, or about 40251 kilometers per hour. This speed is called the escape velocity. Notice that the mass of the object, m, cancelled out at the last step; the escape velocity is the same for all objects. Of course, it takes considerably more energy to get a large object up to 9.7 Kinetic energy; improper integrals 209 40251 kph than a small one, so it is certainly more difficult to get a large object into deep space than a small one. Also, note that while we have computed the escape velocity for the earth, this speed would not in fact get an object “to infinity” because of the large mass in our neighborhood called the sun. Escape velocity for the sun starting at the distance of the earth from the sun is nearly 4 times the escape velocity we have calculated. Exercises 1. Is the area under y = 1/x from 1 to infinity finite or infinite? If finite, compute the area. ⇒ 2. Is the area under y = 1/x3 from 1 to infinity finite or infinite? If finite, compute the area. ⇒ Z ∞ 3. Does Z0 ∞ 4. Does Z1 ∞ 5. Does Z x2 + 2x − 1 dx converge or diverge? If it converges, find the value. ⇒ √ 1/ x dx converge or diverge? If it converges, find the value. ⇒ e−x dx converge or diverge? If it converges, find the value. ⇒ 0 1/2 6. (2x − 1)−3 dx is an improper integral of a slightly different sort. Express it as a limit 0 and determine whether it converges or diverges; if it converges, find the value. ⇒ Z 1 √ 1/ x dx converge or diverge? If it converges, find the value. ⇒ 7. Does Z 0 π/2 8. Does Z sec2 x dx converge or diverge? If it converges, find the value. ⇒ 0 ∞ 9. Does −∞ ∞ Z 10. Does x2 dx converge or diverge? If it converges, find the value. ⇒ 4 + x6 x dx converge or diverge? If it converges, find the value. Also find the Cauchy −∞ Principal Value, if it exists. ⇒ Z ∞ sin x dx converge or diverge? If it converges, find the value. Also find the Cauchy 11. Does −∞ Principal Value, if it exists. ⇒ Z ∞ 12. Does cos x dx converge or diverge? If it converges, find the value. Also find the Cauchy −∞ Principal Value, if it exists. ⇒ 13. Suppose the curve y = 1/x is rotated around the x-axis generating a sort of funnel or horn shape, called Gabriel’s horn or Toricelli’s trumpet. Is the volume of this funnel from x = 1 to infinity finite or infinite? If finite, compute the volume. ⇒ 14. An officially sanctioned baseball must be between 142 and 149 grams. How much work, in Newton-meters, does it take to throw a ball at 80 miles per hour? At 90 mph? At 100.9 mph? (According to the Guinness Book of World Records, at http://www.baseballalmanac.com/recbooks/rb_guin.shtml, “The greatest reliably recorded speed at which a 210 Chapter 9 Applications of Integration baseball has been pitched is 100.9 mph by Lynn Nolan Ryan (California Angels) at Anaheim Stadium in California on August 20, 1974.”) ⇒ 9.8 Probability You perhaps have at least a rudimentary understanding of discrete probability, which measures the likelihood of an “event” when there are a finite number of possibilities. For example, when an ordinary six-sided die is rolled, the probability of getting any particular number is 1/6. In general, the probability of an event is the number of ways the event can happen divided by the number of ways that “anything” can happen. For a slightly more complicated example, consider the case of two six-sided dice. The dice are physically distinct, which means that rolling a 2–5 is different than rolling a 5–2; each is an equally likely event out of a total of 36 ways the dice can land, so each has a probability of 1/36. Most interesting events are not so simple. More interesting is the probability of rolling a certain sum out of the possibilities 2 through 12. It is clearly not true that all sums are equally likely: the only way to roll a 2 is to roll 1–1, while there are many ways to roll a 7. Because the number of possibilities is quite small, and because a pattern quickly becomes evident, it is easy to see that the probabilities of the various sums are: P (2) = P (12) = 1/36 P (3) = P (11) = 2/36 P (4) = P (10) = 3/36 P (5) = P (9) = 4/36 P (6) = P (8) = 5/36 P (7) = 6/36 Here we use P (n) to mean “the probability of rolling an n.” Since we have correctly accounted for all possibilities, the sum of all these probabilities is 36/36 = 1; the probability that the sum is one of 2 through 12 is 1, because there are no other possibilities. The study of probability is concerned with more difficult questions as well; for example, suppose the two dice are rolled many times. On the average, what sum will come up? In the language of probability, this average is called the expected value of the sum. This is at first a little misleading, as it does not tell us what to “expect” when the two dice are rolled, but what we expect the long term average will be. Suppose that two dice are rolled 36 million times. Based on the probabilities, we would expect about 1 million rolls to be 2, about 2 million to be 3, and so on, with a roll of 7 9.8 Probability 211 topping the list at about 6 million. The sum of all rolls would be 1 million times 2 plus 2 million times 3, and so on, and dividing by 36 million we would get the average: 1 36 · 106 106 2 · 106 6 · 106 106 + 3 + · · · + 7 + · · · + 12 =2 36 · 106 36 · 106 36 · 106 36 · 106 = 2P (2) + 3P (3) + · · · + 7P (7) + · · · + 12P (12) x̄ = (2 · 106 + 3(2 · 106 ) + · · · + 7(6 · 106 ) + · · · + 12 · 106 ) = 12 X iP (i) = 7. i=2 It is apparent that there is nothing special about the 36 million in this calculation. No 12 X matter what the number of rolls, once we simplify the average, we get the same iP (i). i=2 A variable, say X, that can take certain values, each with a corresponding probability, is called a random variable; in the example above, the random variable was the sum of the two dice. If the possible values for X are x1 , x2 ,. . . ,xn , then the expected value of the n X random variable is E(X) = xi P (xi ). i=1 In many applications of probability, the number of possible values of a random variable is very large, perhaps even infinite. To deal with the infinite case we need a different approach, and since there is a sum involved, it should not be wholly surprising that integration turns out to be a useful tool. It then turns out that even when the number of possibilities is large but finite, it is easier to pretend that the number is infinite. Suppose, for example, that a dart is thrown at a dart board. Since the dart board consists of a finite number of atoms, there are in some sense only a finite number of places for the dart to land, but it is easier to explore the probabilities involved by pretending that the dart can land on any point in the usual x-y plane. DEFINITION 9.22 Let f : R → R be a function. If f (x) ≥ 0 for every x and Z ∞ f (x) dx = 1 then f is a probability density function. −∞ We associate a probability density function with a random variable X by stipulating Z b that the probability that X is between a and b is f (x) dx. Because of the requirement a that the integral from −∞ to ∞ be 1, all probabilities are less than or equal to 1, and the probability that X takes on some value between −∞ and ∞ is 1, as it should be. 212 Chapter 9 Applications of Integration EXAMPLE 9.23 Consider again the two dice example; we can view it in a way more that resembles the probability density function approach. Consider a random variable X that takes on any real value with probabilities given by the probability density function in figure 9.16. The function f consists of just the top edges of the rectangles, with vertical sides drawn for clarity; the function is zero below 1.5 and above 12.5. The area of each rectangle is the probability of rolling the sum in the middle of the bottom of the rectangle, or Z n+1/2 f (x) dx. P (n) = n−1/2 The probability of rolling a 4, 5, or 6 is Z 13/2 P (n) = f (x) dx. 7/2 Of course, we could also compute probabilities that don’t make sense in the context of the dice, such as the probability that X is between 4 and 5.8. 6 5 4 3 2 1 1 2 3 Figure 9.16 4 5 6 7 8 9 10 11 12 A probability density function for two dice. As this example illustrates, we do not require that f be continuous, but we will assume that f has only finitely many discontinuities (possibly zero), and that the discontinuities are either jump discontinuities or removable (a “hole” in the function). This guarantees 9.8 Probability 213 that the cumulative distribution function, Z x F (x) = P (X ≤ x) = f (t)dt, −∞ is continuous. Hence, we say that X is a continuous random variable. Note that because the area does not decrease as x increases, the function F (x) is non-decreasing. The entire collection of probabilities for a random variable X, namely P (X ≤ x) for all x, is called a probability distribution. Suppose that a < b and EXAMPLE 9.24 ( f (x) = 1 b−a 0 if a ≤ x ≤ b otherwise. Then f (x) is the uniform probability density function on [a, b]. and the corresponding distribution is the uniform distribution on [a, b]. Z The next theorem is not hard to prove; if I is an interval [a, b], we write f (x) dx for I Z b f (x) dx. a THEOREM 9.25 Let I be an interval. If 0 ≤ f (x) ≤ g(x) for x ∈ I and f and g are Z Z Z integrable on I then 0 ≤ f (x) dx ≤ g(x) dx ≤ ∞. In particular, if f (x) dx diverges, I I I Z Z Z then so does g(x) dx while if g(x) dx converges, then so does f (x) dx. I I I Note that the theorem applies to infinite intervals, in which case the integrals are improper integrals. 2 EXAMPLE 9.26 Notice that 0 ≤ f (x) = e−x /2 ≤ e−x/2 for |x| > 1. It is easy to check Z ∞ Z ∞ 2 −x/2 that e dx < ∞, so by the theorem e−x /2 dx is finite. Since f is symmetric 1 1 Z −1 2 around the y-axis, e−x /2 dx is also finite, and so finally −∞ Z Z ∞ A= e −∞ −x2 /2 Z −1 dx = e −∞ −x2 /2 1 dx + Z −x2 /2 e −1 dx + 1 ∞ e−x 2 /2 dx 214 Chapter 9 Applications of Integration is finite and positive. If we let g(x) = f (x)/A, then g is a probability density function. It turns out to be very useful, and is called the standard normal probability density function or more informally the bell curve, giving rise to the standard normal distribution. See figure 9.17 for the graph of the bell curve. 0.5 .......... ............. .................... ........ ........ ....... ........ ....... . ......... . . . . . . ............ ... . . . . . . . . . . . ..................................... ....................... ............................................................ −4 −3 −2 −1 0 Figure 9.17 1 2 3 4 The bell curve. We have shown that A is some finite number without computing it; we cannot compute it with the techniques we have available. By using some techniques from multivariable √ calculus, we can show that A = 2π. EXAMPLE 9.27 The exponential distribution has probability density function n f (x) = 0 ce−cx x<0 x≥0 where c is a positive constant. DEFINITION 9.28 Let f be a probability density function. Since we are assuming Z x that the function F (x) = f (t) dt is continuous and ranges from 0 to 1 it follows by the −∞ intermediate value theorem that there is at least one number m such that F (m) = 1/2; m is called a median of the random variable X. Note that P (X ≤ m) = P (X ≥ m) = 1/2. EXAMPLE 9.29 The median of the uniform distribution on [a, b] is (a + b)/2. EXAMPLE 9.30 Let 0    1 f (x) = 0    1 0 x<0 0 ≤ x ≤ 1/2 1/2 < x < 1 1 ≤ x ≤ 3/2 3/2 < x. Any value in the interval [1/2, 1 qualifies as a median, although convention would usually take ‘the’ median to be 3/4. 9.8 Probability 215 An even function is one that is symmetric around the y axis. EXAMPLE 9.31 If f is an even probability density function then one of the medians is 0. If f (x) > 0 on [−δ, δ] for some δ > 0, then 0 is the only median. In particular, the median of the standard normal distribution is 0. DEFINITION 9.32 If a probability density function f has a global maximum at x = c then c is a mode of the random variable X. There need not be a single mode; for example, in the uniform distribution every number between a and b is a mode. The mode may not even exist, though it is a bit tricky to come up with an example. EXAMPLE 9.33 It is somewhat difficult to devise a continuous probability density function for which there is no mode, but easy if we give up continuity. Consider  0   x + 1  x < −1 −1 ≤ x < 0 f (x) = 0 x=0     −x + 1 0 < x ≤ 1 0 1 < x. Sketch this graph; it is then apparent that f has no global maximum and hence no mode. In practice, both the mode and the median are useful, but the expected value, also called the mean, is generally most useful. Following our discussion of discrete probability, the definition should not be surprising. DEFINITION 9.34 The mean of a continuous random variable X with probability Z ∞ density function f is µ = E(X) = xf (x) dx, provided the integral converges. −∞ When the mean exists it is unique, since it is the result of an explicit calculation. The mean does not always exist. Let us look more closely at the definition of the mean. It is in fact essentially identical to the definition of the center of mass of a one-dimensional beam. The probability density function f plays the role of the physical density function, but now the “beam” has infinite length. If we consider only a finite portion of the beam, say between a and b, then the 216 Chapter 9 Applications of Integration center of mass is Z b xf (x) dx a x̄ = Z . b f (x) dx a If we extend the beam to infinity, we get Z ∞ xf (x) dx Z ∞ −∞ x̄ = Z ∞ = xf (x) dx, −∞ f (x) dx −∞ R∞ because −∞ f (x) dx = 1. In the center of mass interpretation, this integral is the total mass of the beam, which is always 1 when f is a probability density function. EXAMPLE 9.35 The mean of the standard normal distribution is Z ∞ 2 e−x /2 x √ dx. 2π −∞ We compute the two halves: ¯0 e e 1 ¯ dx = lim − √ x √ ¯ = −√ D→−∞ 2π 2π ¯ 2π −∞ Z −x2 /2 ¯ −x2 /2 0 D and Z 0 ∞ ¯D 2 2 e−x /2 e−x /2 ¯¯ 1 x √ dx = lim − √ ¯ =√ . D→∞ 2π 2π ¯ 2π 0 The sum of these is 0, which is the mean. Suppose that f : R → R is the probability density function for the continuous random variable X, and that the mean µ, exists (and is finite). We would like to measure how far a “typical” value of X is from µ. One way to measure this distance is (X − µ)2 ; we square the difference so as to measure all distances as positive. The expected value of this quantity is Z ∞ V (X) = (x − µ)2 f (x) dx. −∞ This quantity is called the variance, and is the expected value of the squared distance to µ. The standard deviation, denoted σ, is the square root of the variance. By taking 9.8 217 Probability the square root we “correct for” the original square, so that the units of σ are the same as the units of X, and σ measures a “typical distance from µ” instead of a typical squared distance. The standard deviation is very useful in statistical analysis. EXAMPLE 9.36 We compute the standard deviation of the standard normal distrubution. The variance is Z ∞ 2 1 √ x2 e−x /2 dx. 2π −∞ To compute the antiderivative, use integration by parts, with u = x and dv = xe−x This gives Z Z 2 2 −x2 /2 −x2 /2 x e dx = −xe + e−x /2 dx. 2 /2 dx. We cannot do the new integral, but we know its value when the limits are −∞ to ∞, from our discussion of the standard normal distribution. Thus ¯∞ Z ∞ Z ∞ ¯ 2 1 1 1 1 √ 2 −x2 /2 −x2 /2 ¯ √ +√ x e dx = − √ xe e−x /2 dx = 0 + √ 2π = 1. ¯ 2π −∞ 2π 2π −∞ 2π −∞ The standard deviation is then √ 1 = 1. Here is a simple example of the use of these ideas. Suppose it is known that, in the long run, 1 out of every 100 computer memory chips produced by a certain manufacturing plant is defective. Suppose 1000 chips are selected at random and 15 of them are defective. This is more than the ‘expected’ number (10), but is it so many that we should suspect that something has gone wrong in the manufacturing process? We are interested in the probability that various numbers of defective chips arise; the probability distribution is discrete: there can only be a whole number of defective chips. But (under reasonable assumptions) the distribution is very close to a normal distribution. By methods more complicated than we can explore here, it turns out that the appropriate density function is µ ¶ 1 −(x − 10)2 f (x) = √ p exp , 2(1000)(.01)(.99) 2π 1000(.01)(.99) which is pictured in figure 9.18 (exp(x) = ex ). Now how do we measure how unlikely it is that under normal circumstances we would see 15 defective chips? We can’t compute the probability of exactly 15 defective chips, as Z 15.5 Z 15 f (x) dx ≈ 0.036; this means there f (x) dx = 0. We could compute this would be 15 14.5 is only a 3.6% chance that the number of defective chips is 15. (We cannot compute these 218 Chapter 9 Applications of Integration 0.15 0.10 0.05 ....................... ........ ...... ...... ...... ..... ...... . . . . .. ...... . . . . . ...... ... . . . . ...... .. . . . . ....... . .. . . . . ......... . . . .... . . ................ . . . . . . . . ............................................................ ....................... 0 Figure 9.18 5 10 15 20 25 Normal density function for the defective chips example. integrals exactly; computer software has been used to approximate the integral values in Z 10.5 this discussion.) But this is misleading: f (x) dx ≈ 0.126, which is larger, certainly, 9.5 but still small, even for the “most likely” outcome. The most useful question, in most circumstances, is this: how likely is it that the number of defective chips is “far from” the mean? For example, how likely, or unlikely, is it that the number of defective chips is different by 3 or more from the expected value of 10? This is the probability that the number of defective chips is less than 7 or larger than 13, namely Z Z 7 ∞ f (x) dx + f (x) dx ≈ 0.34. −∞ 13 So there is a 34% chance that this happens—not small at all. Hence the 15 defective chips does not appear to be cause for alarm. How about 20? Here we compute Z Z 0 ∞ f (x) dx + −∞ f (x) dx ≈ 0.0015. 20 So there is only a 0.15% chance that the number of defective chips is more than 10 away from the mean; this would typically be interpreted as too suspicious to ignore—it shouldn’t happen if the process is running normally. The big question, of course, is what level of improbability should trigger concern? It depends to some degree on the application, and in particular on the consequences of getting it wrong in one direction or the other. If we’re wrong, do we lose a little money? A lot of money? Do people die? In general, the standard choices are 5% and 1%. So what we should do is find the number of defective chips that has only, let us say, a 1% chance of occurring under normal circumstances, and use that as the relevant number. In other words, we want to know when Z Z 10−r ∞ f (x) dx < 0.01. f (x) dx + −∞ 10+r 9.8 Probability 219 A bit of trial and error shows that with r = 8 the probability is about 1.1%, and with r = 9 it is about 0.4%, so if the number of defective chips is 19 or more, or 1 or fewer, we should look for problems. If the number is high, we worry that the manufacturing process has a problem, or conceivably that the process that tests for defective chips is not working correctly. If the number is too low, the obvious culprit is that the testing procedure is broken, and is not detecting defective chips. It is sometimes convenient to view the situation from the point of view of what is “good” rather than what is “bad”. Again by trial and error, we can discover that Z 10+r f (x) dx ≈ 0.9908 10−r when r = 8.2. So there is just over a 99% chance that under normal operation the number of defective chips is between 2 and 18; in such circumstances we assume the process is working normally. Exercises 1. Compute R∞ 1 e−x/2 dx. 2. Show that the function  f (x) = 0 ce−cx x<0 x≥0 is a probability density function, where c is a positive constant. Compute the median, mode, and mean. 3. Find the mode of the standard normal distribution. 4. Let µ and σ be real numbers with σ > 0. Show that (x−µ)2 1 − e 2σ2 2πσ is a probability density function. You will not be able to compute this integral directly; use a substitution to convert the integral into the one from example 9.26. The function N is the probability density function of the normal distribution with mean µ and standard deviation σ. Show that the mean of the normal distribution is µ and the standard deviation is σ. N (x) = √ 5. Let ( f (x) = 1 2x2 0 |x| ≥ 1 |x| < 1 Show that f is a probability density function, and that the distribution has no mean. 6. Let ( f (x) = x 1 0 −1 ≤ x ≤ 1 1 b and when a < b. Compare to the area of a sphere. 9.11 Differential equations Many physical phenomena can be modeled using the language of calculus. For example, observational evidence suggests that the temperature of a cup of tea (or some other liquid) in a room of constant temperature will cool over time at a rate proportional to the difference between the room temperature and the temperature of the tea. In symbols, if t is the time variable, M is the room temperature, and f (t) is the temperature of the tea at time t then f 0 (t) = k(M − f (t)) where k > 0 is a constant which will depend on the kind of tea (or more generally the kind of liquid) but not on the room temperature or the temperature of the tea. This is Newton’s law of cooling and the equation that we just wrote down is an example of a differential equation. Ideally we would like to solve this equation, namely, find the function f (t) that describes the temperature over time, though this often turns out to be impossible, in which case various approximation techniques must be used. The use and solution of differential equations is an important field of mathematics; here we give a few examples to illustrate their use. Informally, a differential equation is an equation in which one or more of the derivatives of some function appear. Typically, a scientific theory will produce a differential equation (or a system of differential equations) that describes or governs some physical process, but the theory will not produce the desired function or functions directly. 228 Chapter 9 Applications of Integration Recall from section 6.2 that when the variable is time the derivative of a function y(t) is sometimes written as ẏ instead of y 0 ; this is quite common in the study of differential equations. DEFINITION 9.40 A first order differential equation is an equation of the form F (t, y, ẏ) = 0. A solution of a first order differential equation is a function f (t) that makes F (t, f (t), f 0 (t)) = 0 for every value of t. Here, F is a function of three variables which we label t, y, and ẏ. It is understood that ẏ will explicitly appear in the equation although t and y need not. The term “first order” means that the first derivative of y appears, but no higher order derivatives do. EXAMPLE 9.41 ẏ = t2 + 1 is a first order differential equation; F (t, y, ẏ) = ẏ − t2 − 1. All solutions to this equation are of the form t3 /3 + t + C. EXAMPLE 9.42 The equation from Newton’s law of cooling, ẏ = k(M − y) is a first order differential equation; F (t, y, ẏ) = k(M − y) − ẏ. DEFINITION 9.43 A first order initial value problem is a system of equations of the form F (t, y, ẏ) = 0, y(t0 ) = y0 . Here t0 is a fixed time and y0 is a number. A solution of an initial value problem is a solution f (t) of the differential equation that also satisfies the initial condition f (t0 ) = y0 . EXAMPLE 9.44 t3 /3 + t + 8/3. The initial value problem ẏ = t2 + 1, y(1) = 4 has solution f (t) = The general first order equation is rather too general, that is, we can’t describe methods that will work on them all, or even a large portion of them. We can make progess with specific kinds of differential equations. For example, much can be said about equations of the form ẏ = φ(t, y) where φ is a function of the two variables t and y. Under reasonable conditions on φ, such an equation has a solution and that the corresponding initial value problem will have a unique solution. The precise statement of the result is known as Picard’s existence and uniqueness theorem. This is still a bit to general for a first introduction. DEFINITION 9.45 A first order differential equation is separable if it can be written in the form ẏ = f (t)g(y). 9.11 Differential equations 229 Some examples of separable differential equations: ẏ = y 3 , ẏ = t3 , ẏ = et sin y, ẏ = t ln y, ẏ = y 1/3 , ẏ = f (t), ẏ = g(y). We already know how to solve a certain class of separable equations, in principle, namely those of the form ẏ = f (t) where f is a continuous function. Since f is continuous, f has an antiderivative F and y = F (t) + C is the general solution of equation the differential equation. Just as in this simple example, a typical differential equation has infinitely many solutions; the corresponding initial value problem will ideally (but certainly not always) have a single solution. Any solution without unknown constants is called a particular solution. We can in principle solve separable first order differential equations by the technique called separation of variables. Suppose that ẏ = f (t)g(y) where f and g are continuous functions. If g(a) = 0 for some a then y(t) = a is a constant solution of the equation, since in this case ẏ = 0 = f (t)g(a). For example, ẏ = y 2 − 1 has constant solutions y(t) = 1 and y(t) = −1 are both constant solutions. To find the nonconstant solutions, we note that the function 1/g(y) is continuous away from the roots of g. Hence, 1/g has an antiderivative G. Let F be an antiderivative of f . Now we write ẏ = f (t) g(y) Z Z ẏ dt = f (t) dt = F (t) + C. g(y) Now let u = y(t), so du = y 0 (t) dt = ẏ dt, so Z ẏ dt = g(y) Z 1 du = G(u) = G(y) g(u) and G(y) = F (t) + C. Now we solve this equation for y. Note that the substitution u = y(t) is trivial—it just renames y to u. In practice, we do not actually do this. Of course, there are a few places this ideal description could go wrong: we need to be able to find the antiderivatives G and F , and we need to solve the final equation for y. The upshot is that the solutions to the original differential equation are the constant solutions, if any, and all functions y that satisfy G(y) = F (t) + C. EXAMPLE 9.46 Consider the initial value problem ẏ = 2(25 − y), y(0) = 40. This is a particular instance of Newton’s law of cooling. In this case, f (t) = 1 and g(y) = 2(25 − y) (or we could choose to take f (t) = 2 and g(y) = (25 − y)). Since g(25) = 0, y(t) = 25 is a constant solution. This is not a solution to the initial value problem, because y(0) 6= 40. (The physical interpretation of the constant solution is that if a liquid is at room temperature, then the liquid will stay at room temperature.) 230 Chapter 9 Applications of Integration For the non-constant solutions we use separation of variables. Note that ẏ dt = dy. Now Z dy dt = dt ẏ = 2(25 − y) Z dt = 1 dt ẏ 2(25 − y) Z Z 1 1 dy = 1 dt 2 25 − y 1 ln |25 − y|(−1) = t + C0 2 ln |25 − y| = −2t + (−2)C0 = −2t + C |25 − y| = e−2t eC y − 25 = ±e−2t eC y = 25 ± e−2t eC = 25 + Ae−2t . Finally, using the initial value, 40 = y(0) = 25 + Ae0 15 = A, and so y = 25 + 15e+ −2t. Remember that y = 25 is also a solution. In the derivation, A = ±eC 6= 0, but if we allow A = 0 in the final solution, y = 25 + Ae−2t represents all solutions to the differential equation. EXAMPLE 9.47 Consider the differential equation ẏ = ky. When k > 0, this describes certain simple cases of population growth: it says that the change in the population y is proportional to the population. The underlying assumption is that each organism in the current population reproduces at a fixed rate, so the larger the population the more new organisms are produced. While this is too simple to model most real populations, it is useful in some cases over a limited time. When k < 0, the differential equation describes a quantity that decreases in proportion to the current value; this can be used to model radioactive decay. The constant solution is y(t) = 0; of course this will not be the solution to any interesting initial value problem. For the non-constant solutions, we proceed much as 9.11 before: Z 1 dy = y Differential equations 231 Z k dt ln |y| = kt + C |y| = ekt eC y = ±ekt eC y = Aekt . Again, if we allow A = 0 this includes the constant solution, and we can simply say that y = Aekt is the general solution. With an initial value we can easily solve for A to get the solution of the initial value problem. In particular, if the initial value is given for time t = 0, y(0) = y0 , then A = y0 and the solution is y = y0 ekt . Exercises 1. Which of the following equations are separable? a. ẏ = sin(ty) b. ẏ = et ey c. y ẏ = t d. ẏ = (t3 − t) sin−1 y e. ẏ = t2 ln y + 4t3 ln y 2. Solve ẏ = 1/(1 + t2 ). 3. Solve the initial value problem ẏ = tn with y(0) = 1. Do not exclude the case n = −1. 4. Solve ẏ = ln t. 5. Identify the constant solutions (if any) of ẏ = t sin y. 6. Identify the constant solutions (if any) of ẏ = tey . 7. Solve ẏ = t/y. 8. Solve ẏ = y 2 − 1. 9. Solve ẏ = t/(y 3 − 5). You may leave your solution in implicit form: that is, you may stop once you have done the integration, without solving for y. 10. Find a non-constant solution of the initial value problem ẏ = y 1/3 , y(0) = 0, using separation of variables. Note that the constant function y(t) = 0 also solves the initial value problem. Hence, an initial value problem need not have a unique solution. 11. Solve the equation for Newton’s law of cooling leaving M and k unknown. 12. After 10 minutes in Jean-Luc’s ready room, his tea has cooled to 40◦ Celsius. The room temperature is 25◦ Celsius. If k = 1, what was the initial temperature of the tea? 13. Solve the logistic equation ẏ = ky(M −y). (This is a somewhat more reasonable population model in most cases than the simpler ẏ = ky.) Sketch the graph of the solution to this equation when y(0) = 1/2. 232 Chapter 9 Applications of Integration 14. Suppose that ẏ = ky, y(0) = 1, and ẏ(0) = 3. What is k? Compute y(4). 15. A radioactive substance obeys the equation ẏ = ky where k < 0 and y is the mass of the substance at time t. Suppose that initially, the mass of the substance is M > 0. At what time does half of the mass remain? (This is known as the half life. Note that the half life depends on k but not on M .) 16. Bismuth-210 has a half life of five days. If there is initially 600 milligrams, how much is left after 6 days? When will there be only 2 milligrams left? 17. The half life of carbon-14 is 5730 years. If one starts with 100 milligrams of carbon-14, how much is left after 6000 years? How long do we have to wait before there is less than 2 milligrams? 18. A certain species of bacteria doubles its population every hour. The differential equation which models this phenomenon is ẏ = ky, where k > 0 and y is the population of bacteria at time t. What is k? 19. If a certain microbe doubles its population every 4 hours and after 5 hours the total population has mass 500 grams, what was the initial mass? 10 Sequences and Series Consider the following sum: 1 1 1 1 1 + + + + ··· + i + ··· 2 4 8 16 2 The dots at the end indicate that the sum goes on forever. Does this make sense? Can we assign a numerical value to an infinite sum? While at first it may seem difficult or impossible, we have certainly done something similar when we talked about one quantity getting “closer and closer” to a fixed quantity. Here we could ask whether, as we add more and more terms, the sum gets closer and closer to some fixed value. That is, look at 1 2 3 4 7 8 15 16 1 2 1 1 = + 2 4 1 1 1 = + + 2 4 8 1 1 1 1 = + + + 2 4 8 16 = and so on, and ask whether these values have a limit. It seems pretty clear that they do, namely 1. In fact, as we will see, it’s not hard to show that 1 1 2i − 1 1 1 1 1 + + + + ··· + i = =1− i i 2 4 8 16 2 2 2 233 234 Chapter 10 Sequences and Series and then 1 = 1 − 0 = 1. i→∞ 2i There is one place that you have long accepted this notion of infinite sum without really thinking of it as a sum: 3 3 3 3 1 0.33333̄ = + + + + ··· = , 10 100 1000 10000 3 for example, or 4 1 5 9 1 + + + + + · · · = π. 3.14159 . . . = 3 + 10 100 1000 10000 100000 Our first task, then, to investigate infinite sums, called series, is to investigate limits of sequences of numbers. That is, we officially call lim 1 − ∞ X 1 1 1 1 1 1 = + + + + · · · + + ··· i i 2 2 4 8 16 2 i=1 a series, while 2i − 1 1 3 7 15 , , , ,..., ,... 2 4 8 16 2i is a sequence, and ∞ X 1 2i − 1 = lim , i→∞ 2i 2i i=1 that is, the value of a series is the limit of a particular sequence. 10.1 Sequences While the idea of a sequence of numbers, a1 , a2 , a3 , . . . is straightforward, it is useful to think of a sequence as a function. We have up until now dealt with functions whose domains are the real numbers, or a subset of the real numbers, like f (x) = sin x. A sequence is a function with domain the natural numbers N = {1, 2, 3, . . .} or the non-negative integers, Z≥0 = {0, 1, 2, 3, . . .}. The range of the function is still allowed to be the real numbers; in symbols, we say that a sequence is a function f : N → R. Sequences are written in a few different ways, all equivalent; these all mean the same thing: a1 , a2 , a3 , . . . ∞ {an }n=1 ∞ {f (n)}n=1 {an | n ∈ N} {f (n) | n ∈ N} 10.1 Sequences 235 As with functions on the real numbers, we will most often encounter sequences that can be expressed by a formula. We have already seen the sequence ai = f (i) = 1 − 1/2i , and others are easy to come by: i i+1 1 f (n) = n 2 f (n) = sin(nπ/6) f (i) = f (i) = (i − 1)(i + 2) 2i Frequently these formulas will make sense if thought of either as functions with domain R or N, though occasionally one will make sense only for integer values. Faced with a sequence we are interested in the limit lim f (i) = lim ai . i→∞ i→∞ We already understand lim f (x) x→∞ when x is a real valued variable; now we simply want to restrict the “input” values to be integers. No real difference is required in the definition of limit, except that we specify, perhaps implicitly, that the variable is an integer. Compare this definition to definition 4.10. ∞ DEFINITION 10.1 Suppose that {an }n=1 is a sequence. We say that limn→∞ an = L if for every ² > 0 there is an N > 0 so that whenever n > N , |an −L| < ². If limn→∞ an = L we say that the sequence converges, otherwise it diverges. If f (i) defines a sequence, and f (x) makes sense, and limx→∞ f (x) = L, then it is clear that limi→∞ f (i) = L as well, but it is important to note that the converse of this statement is not true. For example, since limx→∞ (1/x) = 0, it is clear that also limi→∞ (1/i) = 0, that is, the numbers 1 1 1 1 1 1 , , , , , ,... 1 2 3 4 5 6 get closer and closer to 0. Consider this, however: Let f (n) = sin(nπ). This is the sequence sin(0π), sin(1π), sin(2π), sin(3π), . . . = 0, 0, 0, 0, . . . since sin(nπ) = 0 when n is an integer. Thus limn→∞ f (n) = 0. But limx→∞ f (x), when x is real, does not exist: as x gets bigger and bigger, the values sin(xπ) do not get closer 236 Chapter 10 Sequences and Series and closer to a single value, but take on all values between −1 and 1 over and over. In general, whenever you want to know limn→∞ f (n) you should first attempt to compute limx→∞ f (x), since if the latter exists it is also equal to the first limit. But if for some reason limx→∞ f (x) does not exist, it may still be true that limn→∞ f (n) exists, but that you’ll have to figure out another way to compute it. It is occasionally useful to think of the graph of a sequence. Since the function is defined only for integer values, the graph is just a sequence of dots. In figure 10.1 we see the graphs of two sequences and the graphs of the corresponding real functions. 5 4 3 2 1 0 f (n) = 1/n • • • • 0 • • • • • 5 • 5 4 3 2 1 0 .. .. .. .. .. .. ... ... ... .. ... ... ... .... ... ..... ....... ............. ....................................... .......................................................................... f (x) = 1/x 10 1 0 ...... ...... ...... ........ ... .... ... ... ... .... ... ... ... .... ... .... ... ..... ... .... . . . . ... . . . . . ... ... ... . ... ... ... ... ... ... ... ... .. ... ... ... ... ... ... ... . . . . ..... ... . ... ... ... . . . . . . . ... . ... ... .. ... ... . . .. . . ... ... ... . . . . . . ... . . ... . ... ... . . . ... . . . . . ... ... ... ... ... .. ... .... ... .. ... ... ... ... ... .. ... ... ... .. ... .. ... ... ...... ..... ..... .... f (n) = sin(nπ) 0 • • • • • • • • 1 2 3 4 5 6 7 8 −1 Figure 10.1 10 f (x) = sin(xπ) 1 • 5 0 −1 Graphs of sequences and their corresponding real functions. Not surprisingly, the properties of limits of real functions translate into properties of sequences quite easily. Theorem 2.7 about limits becomes THEOREM 10.2 Suppose that lim an = L and lim bn = M and k is some constant. n→∞ n→∞ Then lim kan = k lim an = kL n→∞ n→∞ lim (an + bn ) = lim an + lim bn = L + M n→∞ n→∞ n→∞ lim (an − bn ) = lim an − lim bn = L − M n→∞ n→∞ n→∞ lim (an bn ) = lim an · lim bn = LM n→∞ n→∞ n→∞ limn→∞ an L an = = , if M is not 0 n→∞ bn limx→a bn M lim 10.1 Sequences 237 Likewise the Squeeze Theorem (4.1) becomes THEOREM 10.3 Suppose that an ≤ bn ≤ cn for all n > N , for some N . If lim an = n→∞ lim cn = L, then lim bn = L. n→∞ n→∞ And a final useful fact: THEOREM 10.4 If lim |an | = 0 then lim an = 0. n→∞ n→∞ This says simply that if the size of an gets close to zero then in fact an gets close to zero. Here are some alternate notations for sequences that are frequently used: a0 , a1 , a2 , a3 , . . . = {ai | i = 0, 1, 2, 3 . . .} = {ai | i ∈ Z≥0 } = {ai }∞ i=0 ½ ¾∞ n EXAMPLE 10.5 Determine whether converges or diverges. If it conn + 1 n=0 verges, compute the limit. Since this makes sense for real numbers we consider lim x→∞ 1 x = lim 1 − = 1 − 0 = 1. x→∞ x+1 x+1 Thus the sequence converges to 1. ½ EXAMPLE 10.6 Determine whether ln n n ¾∞ converges or diverges. If it converges, n=1 compute the limit. We compute ln x 1/x = lim = 0, x→∞ x x→∞ 1 lim using L’Hôpital’s Rule. Thus the sequence converges to 0. EXAMPLE 10.7 Determine whether {(−1)n }∞ n=0 converges or diverges. If it converges, compute the limit. This does not make sense for all real exponents, but the sequence is easy to understand: it is 1, −1, 1, −1, 1 . . . 238 Chapter 10 Sequences and Series and clearly diverges. EXAMPLE 10.8 Determine whether {(−1/2)n }∞ n=0 converges or diverges. If it converges, compute the limit. We consider the sequence {|(−1/2)n |} = {(1/2)n }. Then µ ¶x 1 1 lim = lim x = 0, x→∞ 2 x→∞ 2 so by theorem 10.4 the sequence converges to 0. √ EXAMPLE 10.9 Determine whether {(sin n)/ n}∞ n=1 converges or diverges. If it con√ √ verges, compute the limit. Since | sin n| ≤ 1, 0 ≤ | sin n/ n| ≤ 1/ n and we can use the√ √ orem 10.3 with an = 0 and cn = 1/ n. Since lim an = lim cn = 0, lim sin n/ n = 0 n→∞ n→∞ n→∞ and the sequence converges to 0. EXAMPLE 10.10 A particularly common and useful sequence is {rn }∞ n=0 , for various values of r. Some are quite easy to understand: If r = 1 the sequence converges to 1 since every term is 1, and likewise if r = 0 the sequence converges to 0. If r = −1 this is the sequence of example 10.7 and diverges. If r > 1 or r < −1 the terms rn get large without limit, so the sequence diverges. If 0 < r < 1 then the sequence converges to 0. If −1 < r < 0 then |rn | = |r|n and 0 < |r| < 1, so the sequence converges. In summary, {rn } converges precisely when −1 < r ≤ 1 in which case lim rn = n n→∞ 0 if −1 < r < 1 1 if r = 1 Sometimes we will not be able to determine the limit of a sequence, but we need to know whether it converges. In some cases we can determine this without being able to compute the limit. A sequence is called increasing or sometimes strictly increasing if ai < ai+1 for all i. It is called non-decreasing or sometimes (unfortunately) increasing if ai ≤ ai+1 for all i. Similarly a sequence is decreasing if ai > ai+1 for all i and non-increasing if ai ≥ ai+1 for all i. If a sequence has any of these properties it is called monotonic. EXAMPLE 10.11 The sequence ½ 2i − 1 2i ¾∞ = i=1 1 3 7 15 , , , ,..., 2 4 8 16 10.1 is increasing, and ½ n+1 n ¾∞ = i=1 Sequences 239 2 3 4 5 , , , ,... 1 2 3 4 is decreasing. A sequence is bounded above if there is some number N such that an ≤ N for every n, and bounded below if there is some number N such that an ≥ N for every n. If a sequence is bounded above and bounded below it is bounded. If a sequence {an }∞ n=0 is increasing or non-decreasing it is bounded below (by a0 ), and if it is decreasing or nonincreasing it is bounded above (by a0 ). Finally, with all this new terminology we can state an important theorem. THEOREM 10.12 If a sequence is bounded and monotonic then it converges. We will not prove this; the proof appears in many calculus books. It is not hard to believe: suppose that a sequence is increasing and bounded, so each term is larger than the one before, yet never larger than some fixed value N . The terms must then get closer and closer to some value between a0 and N . It need not be N , since N may be a “toogenerous” upper bound, but rather the limit will be the smallest number that is above all of the terms ai . EXAMPLE 10.13 All of the terms (2i − 1)/2i are less than 2, and the sequence is increasing. As we have seen, the limit of the sequence is 1—1 is the smallest number that is bigger than all the terms in the sequence. Similarly, all of the terms (n + 1)/n are bigger than 1/2, and the limit is 1—1 is the largest number that is smaller than the terms of the sequence. We don’t actually need to know that a sequence is monotonic to apply this theorem— it is enough to know that the sequence is “eventually” monotonic, that is, that at some point it becomes increasing or decreasing. For example, the sequence 10, 9, 8, 15, 3, 21, 4, 3/4, 7/8, 15/16, 31/32, . . . is not increasing, because among the first few terms it is not. But starting with the term 3/4 it is increasing, so the theorem tells us that the sequence 3/4, 7/8, 15/16, 31/32, . . . converges. Since convergence depends only on what happens as n gets large, adding a few random terms at the beginning can’t turn a convergent sequence into a divergent one. 240 Chapter 10 Sequences and Series EXAMPLE 10.14 Show that {n1/n } converges. We first show that this sequence is decreasing, that is, that n1/n ≥ (n+1)1/(n+1) . Consider the real function f (x) = x1/x when x ≥ 1. We can compute the derivative, f 0 (x) = x1/x (1−ln x)/x2 , and note that when x ≥ 3 this is negative. Since the function has negative slope, n1/n ≥ (n + 1)1/(n+1) when n ≥ 3. Since all terms of the sequence are positive, the sequence is decreasing and bounded when n ≥ 3, and so the sequence converges. (As it happens, we can compute the limit in this case, but we know it converges even without knowing the limit; see exercise 1.) EXAMPLE 10.15 Show that {n!/nn } converges. Again we show that the sequence is decreasing, and since each term is positive the sequence converges. We can’t take the derivative this time, as x! doesn’t make sense for x real. But we note that if an+1 /an ≤ 1 then an+1 ≤ an , which is what we want to know. So we look at an+1 /an : (n + 1)! nn (n + 1)! an+1 nn n+1 = = = n+1 n+1 an (n + 1) n! n! (n + 1) n+1 µ n n+1 ¶n µ = n n+1 ¶n < 1. (Again it is possible to compute the limit; see exercise 2.) Exercises 1. Compute lim x1/x . ⇒ x→∞ n! 2. Use the squeeze theorem to show that lim n = 0. n→∞ n √ √ 3. Determine whether { n + 47 − n}∞ n=0 converges or diverges. If it converges, compute the limit. ⇒  2 ff∞ n +1 4. Determine whether converges or diverges. If it converges, compute the limit. (n + 1)2 n=0 ⇒ ff∞  n + 47 converges or diverges. If it converges, compute the 5. Determine whether √ n2 + 3n n=1 limit. ⇒  n ff∞ 2 6. Determine whether converges or diverges. ⇒ n! n=0 10.2 Series While much more can be said about sequences, we now turn to our principal interest, series. Recall that a series, roughly speaking, is the sum of a sequence: if {an | n ∈ Z≥0 } 10.2 Series 241 is a sequence then the associated series is ∞ X an = a0 + a1 + a2 + · · · i=0 Associated with a series is a second sequence, called the sequence of partial sums {sn | n ∈ Z≥0 }: n X sn = an . i=0 So s0 = a0 , s1 = a0 + a1 , s2 = a0 + a1 + a2 , ... A series converges if the sequence of partial sums converges, and otherwise the series diverges. EXAMPLE 10.16 n If an = kx , ∞ X an is called a geometric series. A typical partial n=0 sum is sn = k + kx + kx2 + kx3 + · · · + kxn = k(1 + x + x2 + x3 + · · · + xn ). We note that sn (1 − x) = k(1 + x + x2 + x3 + · · · + xn )(1 − x) = k(1 + x + x2 + x3 + · · · + xn )1 − k(1 + x + x2 + x3 + · · · + xn−1 + xn )x = k(1 + x + x2 + x3 + · · · + xn − x − x2 − x3 − · · · − xn − xn+1 ) = k(1 − xn+1 ) so sn (1 − x) = k(1 − xn+1 ) sn = k 1 − xn+1 . 1−x If |x| < 1, lim xn = 0 so n→∞ lim sn = lim k n→∞ n→∞ 1 1 − xn+1 =k . 1−x 1−x 242 Chapter 10 Sequences and Series So when |x| < 1 the geometric series converges to k/(1 − x). When, for example, k = 1 and x = 1/2: 1 − (1/2)n+1 2n+1 − 1 1 sn = = =2− n n 1 − 1/2 2 2 and ∞ X 1 1 = = 2. n 2 1 − 1/2 n=0 We began the chapter with the series ∞ X 1 , n 2 n=1 namely, the geometric series without the first term 1. Each partial sum of this series is 1 less than the corresponding partial sum for the geometric series, so of course the limit is also one less than the value of the geometric series, that is, ∞ X 1 = 1. 2n n=1 It is not hard to see that the following theorem follows from theorem 10.2. P P THEOREM 10.17 Suppose that an and bn are convergent sequences, and c is a constant. Then X X X 1. can is convergent and can = c an X X X X 2. (an + bn ) is convergent and (an + bn ) = an + bn . In general, the sequence of partial sums sn is harder to understand and analyze than the sequence of terms an , and it is difficult to determine whether series converge and if so to what. Sometimes things are relatively simple, starting with the following. THEOREM 10.18 Proof. Since P If P an converges then lim an = 0. n→∞ an converges, lim sn = L and lim sn−1 = L, because this really says n→∞ n→∞ the same thing but “renumbers” the terms. By theorem 10.2, lim (sn − sn−1 ) = lim sn − lim sn−1 = L − L = 0. n→∞ n→∞ n→∞ 10.2 Series 243 But sn − sn−1 = (a0 + a1 + a2 + · · · + an ) − (a0 + a1 + a2 + · · · + an−1 ) = an , so as desired lim an = 0. n→∞ This theorem presents an easy divergence test: if given a series P an the limit lim an n→∞ does not exist or has a value other than zero, the series diverges. Note well that the converse is not true: If lim an = 0 then the series does not necessarily converge. n→∞ EXAMPLE 10.19 We compute the limit: Show that ∞ X n diverges. n+1 n=1 n = 1 6= 0. n→∞ n + 1 lim Looking at the first few terms perhaps makes it clear that the series has no chance of converging: 1 2 3 4 + + + + ··· 2 3 4 5 will just get larger and larger; indeed, after a bit longer the series starts to look very much like · · · + 1 + 1 + 1 + 1 + · · ·, and of course if we add up enough 1’s we can make the sum as large as we desire. EXAMPLE 10.20 ∞ X 1 Show that diverges. n n=1 Here the theorem does not apply: lim 1/n = 0, so it looks like perhaps the series conn→∞ verges. Indeed, if you have the fortitude (or the software) to add up the first 1000 terms you will find that 1000 X 1 ≈ 7.49, n n=1 so it might be reasonable to speculate that the series converges to something in the neighborhood of 10. But in fact the partial sums do go to infinity; they just get big very, very slowly. Consider the following: 1+ 1 1 1 1 1 1 1 1 + + >1+ + + =1+ + 2 3 4 2 4 4 2 2 244 Chapter 10 Sequences and Series 1+ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + + + + + + >1+ + + + + + + =1+ + + 2 3 4 5 6 7 8 2 4 4 8 8 8 8 2 2 2 1+ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 + +···+ > 1+ + + + +···+ + +···+ = 1+ + + + 2 3 16 2 4 4 8 8 16 16 2 2 2 2 and so on. By swallowing up more and more terms we can always manage to add at least another 1/2 to the sum, and by adding enough of these we can make the partial sums as big as we like. In fact, it’s not hard to see from this pattern that 1+ 1 1 1 n + + ··· + n > 1 + , 2 3 2 2 so to make sure the sum is over 100, for example, we’d add up terms until we get to P around 1/2198 , that is, about 4 · 1059 terms. This series, (1/n), is called the harmonic series. Exercises ∞ X 1. Explain why 2. Explain why 3. Explain why n=1 ∞ X n=1 ∞ X n=1 4. Compute 5. Compute ∞ X n=0 ∞ X n=0 n2 diverges. ⇒ 2n2 + 1 5 diverges. ⇒ + 14 21/n 3 diverges. ⇒ n 3 4 − n. ⇒ n (−3) 3 4 3 + n. ⇒ n 2 5 10.3 The Integral Test It is generally quite difficult, often impossible, to determine the value of a series exactly. In many cases it is possible at least to determine whether or not the series converges, and so we will spend most of our time on this problem. If all of the terms an in a series are non-negative, then clearly the sequence of partial sums sn is non-decreasing. This means that if we can show that the sequence of partial sums is bounded, the series must converge. We know that if the series converges, the terms an approach zero, but this does not mean that an ≥ an+1 for every n. Many useful and interesting series do have this property, however, and they are among the easiest to understand. Let’s look at an example. 10.3 EXAMPLE 10.21 Show that The Integral Test 245 ∞ X 1 converges. 2 n n=1 The terms 1/n2 are positive and decreasing, and since lim 1/x2 = 0, the terms 1/n2 x→∞ approach zero. We seek an upper bound for all the partial sums, that is, we want to find a number N so that sn ≤ N for ever n. The upper bound is provided courtesy of integration, and is inherent in figure 10.2. 2 1 A= 0 0 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... ..... ..... ...... ...... 1 ....... ........ .......... ............. .................. ............................ ....................................................... A = 1/4 ......................................................................... 1 Figure 10.2 2 3 4 5 Graph of y = 1/x2 with rectangles. The figure shows the graph of y = 1/x2 together with some rectangles that lie completely below the curve and that all have base length one. Because the heights of the rectangles are determined by the height of the curve, the areas of the rectangles are 1/12 , 1/22 , 1/32 , and so on—in other words, exactly the terms of the series. The partial sum sn is simply the sum of the areas of the first n rectangles. Because the rectangles all lie between the curve and the x-axis, any sum of rectangle areas is less than the corresponding area under the curve, and so of course any sum of rectangle areas is less than the area under the entire curve, that is, all the way to infinity. There is a bit of trouble at the left end, where there is an asymptote, but we can work around that easily. Here it is: 1 1 1 1 sn = 2 + 2 + 2 + · · · + 2 < 1 + 1 2 3 n Z n 1 1 dx < 1 + x2 Z ∞ 1 1 dx = 1 + 1 = 2, x2 recalling that we computed this improper integral in section 9.7. Since the sequence of partial sums sn is increasing and bounded above by 2, we know that lim sn = L < 2, and n→∞ so the series converges to some number less than 2. In fact, it is possible, though difficult, to show that L = π 2 /6 ≈ 1.6. 246 Chapter 10 Sequences and Series P 1/n diverges. What goes wrong if we try to apply this We already know that technique to it? Here’s the calculation: 1 1 1 1 sn = + + + · · · + < 1 + 1 2 3 n Z n 1 1 dx < 1 + x Z ∞ 1 1 dx = 1 + ∞. x The problem is that the improper integral doesn’t converge. Note well that this does P not prove that 1/n diverges, just that this particular calculation fails to prove that it P converges. A slight modification, however, allows us to prove in a second way that 1/n diverges. EXAMPLE 10.22 ure 10.3. 2 Consider a slightly altered version of figure 10.2, shown in fig- ... ... ... ... ... ... ... ... ... ... ... ... ... ..... ..... ..... ...... ...... ...... ....... ........ ......... ........... . A = 1 ............................................. .......................... .................................... ..................................................... A = 1/2 .......................... 1 A = 1/3 0 0 1 Figure 10.3 2 3 4 5 Graph of y = 1/x with rectangles. The rectangles this time are above the curve, that is, each rectangle completely contains the corresponding area under the curve. This means that 1 1 1 1 sn = + + + · · · + > 1 2 3 n Z n+1 1 ¯n+1 1 ¯ dx = ln x¯ = ln(n + 1). x 1 As n gets bigger, ln(n + 1) goes to infinity, so the sequence of partial sums sn must also go to infinity, so the harmonic series diverges. The important fact that clinches this example is that Z lim n→∞ 1 n+1 1 dx = ∞, x 10.3 which we can rewrite as Z ∞ 1 The Integral Test 247 1 dx = ∞. x So these two examples taken together indicate that we can prove that a series converges or prove that it diverges with a single calculation of an improper integral. This is known as the integral test, which we state as a theorem. THEOREM 10.23 Suppose that f (x) > 0 and is decreasing on the infinite interval ∞ X [k, ∞) (for some k ≥ 1) and that an = f (n). Then the series an converges if and only Z if the improper integral n=1 ∞ f (x) dx converges. 1 P The two examples we have seen are called p-series; a p-series is any series of the form 1/np . If p ≤ 0, lim 1/np 6= 0, so the series diverges. For positive values of p we can n→∞ determine precisely which series converge. THEOREM 10.24 Proof. A p-series with p > 0 converges if and only if p > 1. We use the integral test; we have already done p = 1, so assume that p 6= 1. Z ∞ 1 ¯D x1−p ¯¯ 1 1 D1−p dx = lim − . = lim ¯ p D→∞ 1 − p D→∞ 1 − p x 1−p 1 If p > 1 then 1 − p < 0 and lim D1−p = 0, so the integral converges. If 0 < p < 1 then D→∞ 1 − p > 0 and lim D1−p = ∞, so the integral diverges. D→∞ EXAMPLE 10.25 ∞ X 1 Show that converges. 3 n n=1 We could of course use the integral test, but now that we have the theorem we may simply note that this is a p-series with p > 1. ∞ X 5 EXAMPLE 10.26 Show that converges. n4 n=1 P∞ P∞ We know that if n=1 1/n4 converges then n=1 5/n4 also converges, by theorem 10.17. P∞ P∞ Since n=1 1/n4 is a convergent p-series, n=1 5/n4 converges also. 248 Chapter 10 Sequences and Series EXAMPLE 10.27 Show that ∞ X 5 √ diverges. n n=1 P∞ 1/2 This is almost as easy but the logic has a little twist. If converges then n=1 5/n P P∞ ∞ 1/2 1/2 = also converges, but since theorem 10.17 implies that n=1 (1/5)5/n n=1 1/n this is a p-series with p < 1 we know that in fact it does not converge. Therefore the series P∞ 1/2 can’t converge. n=1 5/n Since it is typically difficult to compute the value of a series exactly, a good approximation is frequently required. In a real sense, a good approximation is only as good as we know it is, that is, while an approximation may in fact be good, it is only valuable in practice if we can guarantee its accuracy to some degree. This guarantee is usually easy to come by for series with decreasing positive terms. P 1/n2 to two decimal places. PN Referring to figure 10.2, if we approximate the sum by n=1 1/n2 , the error we make is the total area of the remaining rectangles, all of which lie under the curve 1/x2 from x = N out to infinity. So we know the true value of the series is larger than the approximation, and no bigger than the approximation plus the area under the curve from N to infinity. Roughly, then, we need to find N so that EXAMPLE 10.28 Approximate Z ∞ N We can compute the integral: Z 1 dx < 1/100. x2 ∞ N 1 1 dx = , 2 x N so N = 100 is a good starting point. Adding up the first 100 terms gives approximately 1.634983900, and that plus 1/100 is 1.644983900, so approximating the series by the value halfway between these will be at most 1/200 = 0.005 in error. The midpoint is 1.639983900, but while this is correct to ±0.005, we can’t tell if the correct two-decimal approximation is 1.63 or 1.64. We need to make N big enough to reduce the guaranteed error, perhaps to around 0.004 to be safe, so we would need 1/N ≈ 0.008, or N = 125. Now the sum of the first 125 terms is approximately 1.636965982, and that plus 8/1000 is 1.644965982 and the point halfway between them is 1.640965982. The true value is then 1.640965982 ± 0.004 which rounds to 1.64. We have mentioned that the true value of this series can be shown to be π 2 /6 ≈ 1.644934068 which rounds down to 1.64 (just barely) and is indeed below the upper bound of 1.644965982, again just barely. Frequently approximations will be even better than the “guaranteed” accuracy, but not always, as this example demonstrates. 10.4 Alternating Series 249 Exercises Determine whether each series converges or diverges. ∞ ∞ X X 1 1 1. ⇒ 5. ⇒ n π/4 e n n=1 n=1 2. 3. 4. ∞ X n=1 ∞ X n=1 ∞ X n=1 ∞ X n 6. ⇒ en n=1 n ⇒ 2 n +1 ln n ⇒ n2 n2 7. 1 ⇒ +1 8. ∞ X n=2 ∞ X n=2 1 ⇒ n ln n 1 ⇒ n(ln n)2 ∞ N X X 1 1 9. Find an N so that = ± 0.005. ⇒ 4 4 n n n=1 n=1 10. Find an N so that ∞ N X X 1 1 = ± 10−4 . ⇒ n n e e n=0 n=0 11. Find an N so that ∞ N X X ln n ln n = ± 0.005. ⇒ 2 2 n n n=1 n=1 12. Find an N so that ∞ X n=2 N X 1 1 = ± 0.005. ⇒ 2 n(ln n) n(ln n)2 n=2 10.4 Alternating Series Next we consider series with both positive and negative terms, but in a regular pattern: they alternate, as in the alternating harmonic series for example: ∞ X (−1)n−1 1 −1 1 −1 1 1 1 1 = + + + + ··· = − + − + ···. n 1 2 3 4 1 2 3 4 n=1 In this series the sizes of the terms decrease, that is, |an | forms a decreasing sequence, but this is not required in an alternating series. As with positive term series, however, when the terms do have decreasing sizes it is easier to analyze the series, much easier, in fact, than positive term series. Consider pictorially what is going on in the alternating harmonic series, shown in figure 10.4. Because the sizes of the terms an are decreasing, the partial sums s1 , s3 , s5 , and so on, form a decreasing sequence that is bounded below by s2 , so this sequence must converge. Likewise, the partial sums s2 , s4 , s6 , and so on, form an increasing sequence that is bounded above by s1 , so this sequence also converges. Since all the even numbered partial sums are less than all the odd numbered ones, and 250 Chapter 10 Sequences and Series a2 = − 1 2 ..................................................................................................................................................................................................................................... a a 6 ............................................................................. 4 ...................................................................................................................... ...... s2 = 1 4 s4 1 2 s6 s5 s3 1 = s1 = a1 a ..............................................5 ................................................. ........................................................................................................................................................... a3 Figure 10.4 The alternating harmonic series. since the “jumps” (that is, the ai terms) are getting smaller and smaller, the two sequences must converge to the same value, meaning the entire sequence of partial sums s1 , s2 , s3 , . . . converges as well. There’s nothing special about the alternating harmonic series—the same argument works for any alternating sequence with decreasing size terms. The alternating series test is worth calling a theorem. Suppose that {an }∞ n=0 is a non-increasing sequence of positive num∞ X bers and lim an = 0. Then the alternating series (−1)n an converges. THEOREM 10.29 n→∞ n=0 Proof. The odd numbered partial sums, s1 , s3 , s5 , and so on, form a non-increasing sequence, because s2k+3 = s2k+1 − a2k+2 + a2k+3 ≤ s2k+1 , since a2k+2 ≥ a2k+3 . This sequence is bounded below by s2 , so it must converge, say lim s2k+1 = L. Likewise, k→∞ the partial sums s2 , s4 , s6 , and so on, form a non-decreasing sequence that is bounded above by s1 , so this sequence also converges, say lim s2k = M . Since lim an = 0 and n→∞ k→∞ s2k+1 = s2k + a2k+1 , L = lim s2k+1 = lim (s2k + a2k+1 ) = lim s2k + lim a2k+1 = M + 0 = M, k→∞ k→∞ k→∞ k→∞ so L = M , the two sequences of partial sums converge to the same limit, and this means the entire sequence of partial sums also converges to L. Another useful fact is implicit in this discussion. Suppose that L= ∞ X (−1)n an n=0 10.5 Comparison Tests 251 and that we approximate L by a finite part of this sum, say L≈ N X (−1)n an . n=0 Because the terms are decreasing in size, we know that the true value of L must be between this approximation and the next one, that is, between N X n (−1) an n=0 and N +1 X (−1)n an . n=0 Depending on whether N is odd or even, the second will be larger or smaller than the first. EXAMPLE 10.30 Approximate the alternating harmonic series to one decimal place. We need to go roughly to the point at which the next term to be added or subtracted is 1/10. Adding up the first nine and the first ten terms we get approximately 0.746 and 0.646. These are 1/10 apart, but it is not clear how the correct value would be rounded. It turns out that we are able to settle the question by computing the sums of the first eleven and twelve terms, which give 0.737 and 0.653, so correct to one place the value is 0.7. Exercises Determine whether the following series converge or diverge. ∞ ∞ X X (−1)n−1 n 1. ⇒ 3. (−1)n−1 ⇒ 2n + 5 3n − 2 n=1 n=1 ∞ ∞ X X (−1)n−1 ln n √ ⇒ 4. (−1)n−1 ⇒ n n−3 n=4 n=1 ∞ X 1 5. Approximate (−1)n−1 3 to two decimal places. ⇒ n n=1 2. 6. Approximate ∞ X (−1)n−1 n=1 1 to two decimal places. ⇒ n4 10.5 Comparison Tests As we begin to compile a list of convergent and divergent series, new ones can sometimes be analyzed. EXAMPLE 10.31 Does ∞ X n=2 n2 1 converge? ln n 252 Chapter 10 Sequences and Series The obvious first approach, based on what we know, is the integral test. Unfortunately, we can’t compute the required antiderivative. But looking at the series, it would appear that it must converge, because the terms we are adding are smaller than the terms of a p-series, that is, 1 1 < , n2 ln n n2 when n ≥ 3. Since adding up the terms 1/n2 doesn’t get “too big”, the new series “should” also converge. Let’s make this more precise. ∞ ∞ X X 1 1 converges if and only if converges—all we’ve done is The series 2 2 n ln n n ln n n=3 n=2 ∞ X 1 dropped the initial term. We know that converges. Looking at two typical partial n2 n=3 sums: sn = 32 1 1 1 1 1 1 1 1 + 2 + 2 + ··· + 2 < 2 + 2 + 2 + · · · + 2 = tn . ln 3 4 ln 4 5 ln 5 n ln n 3 4 5 n Since the p-series converges, say to L, and since the terms are positive, tn < L. Since the terms of the new series are positive, the sn form an increasing sequence and sn < tn < L for all n. Hence the sequence {sn } is bounded and so converges. Sometimes, even when the integral test applies, comparison to a known series is easier, so it’s generally a good idea to think about doing a comparison before doing the integral test. EXAMPLE 10.32 ∞ X | sin n| Does converge? 2 n n=2 We can’t apply the integral test here, because the terms of this series are not decreasing. Just as in the previous example, however, | sin n| 1 ≤ 2, 2 n n because | sin n| ≤ 1. Once again the partial sums are non-decreasing and bounded above P by 1/n2 = L, so the new series converges. Like the integral test, the comparison test can be used to show both convergence and divergence. In the case of the integral test, a single calculation will confirm whichever is the case. To use the comparison test we must first have a good idea as to convergence or divergence and pick the sequence for comparison accordingly. 10.5 EXAMPLE 10.33 Does ∞ X √ n=2 1 n2 − 3 253 Comparison Tests converge? We observe that the −3 should have little effect compared to the n2 inside the square √ root, and therefore guess that the terms are enough like 1/ n2 = 1/n that the series should diverge. We attempt to show this by comparison to the harmonic series. We note that 1 1 1 √ >√ = , 2 2 n n −3 n so that sn = √ 1 22 −3 +√ 1 32 −3 + ··· + √ 1 n2 −3 > 1 1 1 + + · · · + = tn , 2 3 n where tn is 1 less than the corresponding partial sum of the harmonic series (because we start at n = 2 instead of n = 1). Since lim tn = ∞, lim sn = ∞ as well. n→∞ n→∞ So the general approach is this: If you believe that a new series is convergent, attempt to find a convergent series whose terms are larger than the terms of the new series; if you believe that a new series is divergent, attempt to find a divergent series whose terms are smaller than the terms of the new series. EXAMPLE 10.34 Does ∞ X √ n=1 1 n2 + 3 converge? Just as in the last example, we guess that this is very much like the harmonic series and so diverges. Unfortunately, 1 1 √ < , n n2 + 3 so we can’t compare the series directly to the harmonic series. A little thought leads us to √ 1 n2 +3 >√ P n2 1 1 = , 2 2n + 3n so if 1/(2n) diverges then the given series diverges. But since theorem 10.17 implies that it does indeed diverge. P 1/(2n) = (1/2) P 1/n, For reference we summarize the comparison test in a theorem. THEOREM 10.35 Suppose that an and bn are non-negative for all n and that an ≤ bn when n ≥ N , for some N . ∞ ∞ X X If bn converges, so does an . n=0 n=0 254 Chapter 10 Sequences and Series If ∞ X an diverges, so does n=0 ∞ X bn . n=0 Exercises Determine whether the series converge or diverge. ∞ X 1 1. ⇒ 2 2n + 3n + 5 n=1 2. 3. 4. 5. ∞ X n=2 ∞ X n=1 ∞ X n=1 ∞ X n=1 6. ∞ X ln n ⇒ n n=1 1 ⇒ 2 2n + 3n − 5 7. ∞ X ln n ⇒ n3 n=1 1 ⇒ 2 2n − 3n − 5 8. ∞ X 1 ⇒ ln n n=2 3n + 4 ⇒ 2 2n + 3n + 5 9. 2 3n + 4 ⇒ 2 2n + 3n + 5 10. ∞ X n=1 ∞ X n=1 3n ⇒ 2n + 5 n 3n ⇒ 2n + 3 n 10.6 Absolute Convergence P Roughly speaking there are two ways for a series to converge: As in the case of 1/n2 , the individual terms get small very quickly, so that the sum of all of them stays finite, or, P P as in the case of (−1)n−1 /n, the terms don’t get small fast enough ( 1/n diverges), but a mixture of positive and negative terms provides enough cancellation to keep the sum finite. You might guess from what we’ve seen that if the terms get small fast enough to do the job, then whether or not some terms are negative and some positive the series converges. THEOREM 10.36 If ∞ X |an | converges, then n=0 Proof. ∞ X an converges. n=0 Note that 0 ≤ an +|an | ≤ 2|an | so by the comparison test ∞ X (an +|an |) converges. n=0 Now ∞ X (an + |an |) − n=0 converges by theorem 10.17. ∞ X n=0 |an | = ∞ X n=0 an + |an | − |an | = ∞ X n=0 an 10.6 Absolute Convergence 255 P an with both positive and negative terms, you should first ask So given a series P whether |an | converges. This may be an easier question to answer, because we have P tests that apply specifically to terms with non-negative terms. If |an | converges then P P you know that an converges as well. If |an | diverges then it still may be true that P an converges—you will have to do more work to decide the question. Another way to P P think of this result is: it is (potentially) easier for an to converge than for |an | to converge, because the latter series cannot take advantage of cancellation. P P P If |an | converges we say that an converges absolutely; to say that an converges absolutely is to say that any cancellation that happens to come along is not really needed, as the terms already get small so fast that convergence is guaranteed by that alone. If P P P an converges but |an | does not, we say that an converges conditionally. For ∞ ∞ X X 1 1 example (−1)n−1 2 converges absolutely, while (−1)n−1 converges conditionally. n n n=1 n=1 EXAMPLE 10.37 ∞ X sin n Does converge? 2 n n=2 ∞ X | sin n| In example 10.32 we saw that converges, so the given series converges abson2 n=2 lutely. EXAMPLE 10.38 Does ∞ X (−1)n n=0 3n + 4 converge? 2n2 + 3n + 5 ∞ X ∞ X 3n + 4 3 Taking the absolute value, diverges by comparison to , so if the 2 2n + 3n + 5 10n n=0 n=1 series converges it does so conditionally. It is true that lim (3n + 4)/(2n2 + 3n + 5) = 0, n→∞ so to apply the alternating series test we need to know whether the terms are decreasing. If we let f (x) = (3x + 4)/(2x2 + 3x + 5) then f 0 (x) = −(6x2 + 16x − 3)/(2x2 + 3x + 5)2 , and it is not hard to see that this is negative for x ≥ 1, so the series is decreasing and by the alternating series test it converges. 256 Chapter 10 Sequences and Series Exercises Determine whether each series converges absolutely, converges conditionally, or diverges. ∞ ∞ X X 1 1 1. (−1)n−1 2 ⇒ 5. (−1)n ⇒ 2n + 3n + 5 ln n n=1 n=2 2. 3. 4. ∞ X (−1)n−1 n=1 ∞ X (−1)n−1 n=1 ∞ X (−1)n−1 n=1 3n2 + 4 ⇒ 2n2 + 3n + 5 6. ln n ⇒ n 7. ln n ⇒ n3 8. ∞ X (−1)n n=0 ∞ X (−1)n n=0 ∞ X 3n ⇒ 2n + 5 n 3n ⇒ 2n + 3 n (−1)n−1 n=1 arctan n ⇒ n 10.7 The Ratio and Root Tests ∞ X n5 Does the series converge? It is possible, but a bit unpleasant, to approach this 5n n=0 with the integral test or the comparison test, but there is an easier way. Consider what happens as we move from one term to the next in this series: ··· + n5 (n + 1)5 + + ··· 5n 5n+1 The denominator goes up by a factor of 5, 5n+1 = 5 · 5n , but the numerator goes up by much less: (n + 1)5 = n5 + 5n4 + 10n3 + 10n2 + 5n + 1, which is much less than 5n5 when n is large, because 5n4 is much less than n5 . So we might guess that in the long run it begins to look as if each term is 1/5 of the previous term. We have seen series that behave like this: ∞ X 1 5 = , n 5 4 n=0 a geometric series. So we might try comparing the given series to some variation of this geometric series. This is possible, but a bit messy. We can in effect do the same thing, but bypass most of the unpleasant work. The key is to notice that 1 1 an+1 (n + 1)5 5n (n + 1)5 1 lim = lim = lim =1· = . 5 n→∞ an n→∞ 5n+1 n5 n→∞ n 5 5 5 This is really just what we noticed above, done a bit more officially: in the long run, each term is one fifth of the previous term. Now pick some number between 1/5 and 1, say 1/2. 10.7 Because The Ratio and Root Tests 257 an+1 1 = , n→∞ an 5 lim then when n is big enough, say n ≥ N for some N , an+1 1 < an 2 and an+1 < an . 2 So aN +1 < aN /2, aN +2 < aN +1 /2 < aN /4, aN +3 < aN +2 /2 < aN +1 /4 < aN /8, and so on. The general form is aN +k < aN /2k . So if we look at the series aN + aN +1 + aN +2 + aN +3 + · · · + aN +k + · · · , its terms are less than or equal to the terms of the sequence ∞ X aN aN aN aN aN aN + + + + ··· + k + ··· = = 2aN . 2 4 8 2 2k k=0 So by the comparison test, ∞ X k=0 aN +k converges, and this means that ∞ X an converges, n=0 since we’ve just added the fixed number a0 + a1 + · · · + aN −1 . Under what circumstances could we do this? What was crucial was that the limit of an+1 /an , say L, was less than 1 so that we could pick a value r so that L < r < 1. The P fact that L < r (1/5 < 1/2 in our example) means that we can compare the series an P n P n to r , and the fact that r < 1 guarantees that r converges. That’s really all that is required to make the argument work. We also made use of the fact that the terms of the series were positive; in general we simply consider the absolute values of the terms and we end up testing for absolute convergence. THEOREM 10.39 The Ratio Test Suppose that lim |an+1 /an | = L. If L < 1 n→∞ P the series an converges absolutely, if L > 1 the series diverges, and if L = 1 this test gives no information. Proof. The example above essentially proves the first part of this, if we simply replace 1/5 by L and 1/2 by r. Suppose that L > 1, and pick r so that 1 < r < L. Then for n ≥ N , for some N , |an+1 | > r and |an+1 | > r|an |. |an | 258 Chapter 10 Sequences and Series This implies that |aN +k | > rk |aN |, but since r > 1 this means that lim |aN +k | 6= 0, which k→∞ means also that lim an 6= 0. By the divergence test, the series diverges. n→∞ To see that we get no information when L = 1, we need to exhibit two series with P P L = 1, one that converges and one that diverges. It is easy to see that 1/n2 and 1/n do the job. EXAMPLE 10.40 The ratio test is particularly useful for series involving the factorial ∞ X function. Consider 5n /n!. n=0 5n+1 n! 5n+1 n! 1 = lim = lim 5 = 0. n n n→∞ (n + 1)! 5 n→∞ 5 (n + 1)! n→∞ (n + 1) lim Since 0 < 1, the series converges. A similar argument, which we will not do, justifies a similar test that is occasionally easier to apply. THEOREM 10.41 The Root Test Suppose that lim |an |1/n = L. If L < 1 the n→∞ P series an converges absolutely, if L > 1 the series diverges, and if L = 1 this test gives no information. The proof of the root test is actually easier than that of the ratio test, and is a good exercise. EXAMPLE 10.42 Analyze ∞ X 5n . n n n=0 The ratio test turns out to be a bit difficult on this series (try it). Using the root test: µ lim n→∞ 5n nn ¶1/n (5n )1/n 5 = lim = 0. n→∞ (nn )1/n n→∞ n = lim Since 0 < 1, the series converges. The root test is frequently useful when n appears as an exponent in the general term of the series. 10.8 Power Series 259 Exercises 1. Compute lim |an+1 /an | for the series n→∞ P 1/n2 . P 2. Compute lim |an+1 /an | for the series 1/n. n→∞ P 3. Compute lim |an |1/n for the series 1/n2 . n→∞ P 4. Compute lim |an |1/n for the series 1/n. n→∞ Determine whether the series converge. ∞ X 3n 5. (−1)n n ⇒ 5 n=0 ∞ X n5 7. ⇒ nn n=1 ∞ X n! 6. ⇒ n n n=1 ∞ X (n!)2 8. ⇒ nn n=1 10.8 Power Series Recall that we were able to analyze all geometric series “simultaneously” to discover that ∞ X kxn = n=0 k , 1−x if |x| < 1, and that the series diverges when |x| ≥ 1. At the time, we thought of x as an unspecified constant, but we could just as well think of it as a variable, in which case the series ∞ X kxn n=0 is a function, namely, the function k/(1 − x), as long as |x| < 1. While k/(1 − x) is a reaP n sonably easy function to deal with, the more complicated kx does have its attractions: it appears to be an infinite version of one of the simplest function types—a polynomial. This leads naturally to the questions: Do other functions have representations as series? Is there an advantage to viewing them in this way? The geometric series has a special feature that makes it unlike a typical polynomial— the coefficients of the powers of x are the same, namely k. We will need to allow more general coefficients if we are to get anything other than the geometric series. DEFINITION 10.43 A power series has the form ∞ X n=0 an xn , 260 Chapter 10 Sequences and Series with the understanding that an may depend on n but not on x. EXAMPLE 10.44 ratio test: ∞ X xn is a power series. We can investigate convergence using the n n=1 |x|n+1 n n = |x|. = lim |x| n→∞ n + 1 |x|n n→∞ n+1 lim Thus when |x| < 1 the series converges and when |x| > 1 it diverges, leaving only two values in doubt. When x = 1 the series is the harmonic series and diverges; when x = −1 it is the alternating harmonic series (actually the negative of the usual alternating harmonic series) ∞ X xn and converges. Thus, we may think of as a function from the interval [−1, 1) to n n=1 the real numbers. A bit of thought reveals that the ratio test applied to a power series will always have the same nice form. In general, we will compute |an+1 | |an+1 ||x|n+1 |an+1 | lim = lim |x| = |x| lim = L|x|, n n→∞ n→∞ n→∞ |an ||x| |an | |an | assuming that lim |an+1 |/|an | exists. Then the series converges if L|x| < 1, that is, if |x| < 1/L, and diverges if |x| > 1/L. Only the two values x = ±1/L require further investigation. Thus the series will definitely define a function on the interval (−1/L, 1/L), and perhaps will extend to one or both endpoints as well. Two special cases deserve mention: if L = 0 the limit is 0 no matter what value x takes, so the series converges for all x and the function is defined for all real numbers. If L = ∞, then no matter what value x takes the limit is infinite and the series converges only when x = 0. The value 1/L is called the radius of convergence of the series, and the interval on which the series converges is the interval of convergence. Consider again the geometric series, ∞ X n=0 xn = 1 . 1−x Whatever benefits there might be in using the series form of this function are only available to us when x is between −1 and 1. Frequently we can address this shortcoming by modifying the power series slightly. Consider this series: ¶n ∞ µ ∞ X X x+2 3 (x + 2)n 1 = = , = x+2 n 3 3 1 − x 1 − 3 n=0 n=0 10.9 Calculus with Power Series 261 because this is just a geometric series with x replaced by (x + 2)/3. Multiplying both sides by 1/3 gives ∞ X (x + 2)n 1 = , n+1 3 1 − x n=0 the same function as before. For what values of x does this series converge? Since it is a geometric series, we know that it converges when |x + 2|/3 < 1 |x + 2| < 3 −3 < x + 2 < 3 −5 < x < 1. So we have a series representation for 1/(1 − x) that works on a larger interval than before, at the expense of a somewhat more complicated series. The endpoints of the interval of convergence now are −5 and 1, but note that they can be more compactly described as −2 ± 3. Again we say that 3 is the radius of convergence, and we now say that the series is centered at −2. DEFINITION 10.45 A power series centered at a has the form ∞ X an (x − a)n , n=0 with the understanding that an may depend on n but not on x. Exercises Find the radius and interval of convergence for each series. ∞ ∞ X X n! 1. nxn ⇒ 4. (x − 2)n ⇒ n n n=0 n=1 ∞ X xn 2. ⇒ n! n=0 ∞ X (n!)2 5. (x − 2)n ⇒ n n n=1 ∞ X n! n 3. x ⇒ n n n=1 ∞ X (x + 5)n 6. ⇒ n(n + 1) n=1 10.9 Calculus with Power Series Now we know that some functions can be expressed as power series, which look like infinite polynomials. Since calculus, that is, computation of derivatives and antiderivatives, is easy 262 Chapter 10 Sequences and Series for polynomials, the obvious question is whether the same is true for infinite series. The answer is yes: THEOREM 10.46 Suppose the power series f (x) = ∞ X an (x − a)n has radius of n=0 convergence R. Then f 0 (x) = ∞ X nan (x − a)n−1 , n=0 Z ∞ X an f (x) dx = C + (x − a)n+1 , n + 1 n=0 and these two series have radius of convergence R as well. EXAMPLE 10.47 Starting with the geometric series: ∞ X 1 = xn 1 − x n=0 Z ∞ X 1 1 dx = − ln |1 − x| = xn+1 1−x n+1 n=0 ln |1 − x| = ∞ X n=0 − 1 xn+1 n+1 when |x| < 1. The series does not converge when x = 1 but does converge when x = −1 or 1 − x = 2. The interval of convergence is [−1, 1), or 0 < 1 − x ≤ 2, so we can use the series to represent ln(x) when 0 < x ≤ 2. For example ln(3/2) = ln(1 − −1/2) = ∞ X (−1)n n=0 and so ln(3/2) ≈ 1 1 n+1 n+12 1 1 1 1 1 1 1 909 − + − + − + = ≈ 0.406. 2 8 24 64 160 384 896 2240 Because this is an alternating series with decreasing terms, we know that the true value is between 909/2240 and 909/2240 − 1/2048 = 29053/71680 ≈ .4053, so correct to two decimal places the value is 0.41. 10.10 Taylor Series 263 What about ln(9/4)? Since 9/4 is larger than 2 we cannot use the series directly, but ln(9/4) = ln((3/2)2 ) = 2 ln(3/2) ≈ 0.82, so in fact we get a lot more from this one calculation than first meets the eye. To estimate the true value accurately we actually need to be a bit more careful. When we multiply by two we know that the true value is between 0.8106 and 0.812, so rounded to two decimal places the true value is 0.81. Exercises 1. Find a series representation for ln 2. ⇒ 2. Find a power series representation for 1/(1 − x)2 . ⇒ 3. Find a power series representation for 2/(1 − x)3 . ⇒ 4. Find a power series representation for 1/(1 − x)3 . What is the radius of convergence? ⇒ Z 5. Find a power series representation for ln(1 − x) dx. ⇒ 10.10 Taylor Series We have seen that some functions can be represented as series, which may give valuable information about the function. So far, we have seen only those examples that result from manipulation of our one fundamental example, the geometric series. We would like to start with a given function and produce a series to represent it, if possible. ∞ X Suppose that f (x) = an xn on some interval of convergence. Then we know that n=0 we can compute derivatives of f by taking derivatives of the terms of the series. Let’s look at the first few in general: 0 f (x) = ∞ X nan xn−1 = a1 + 2a2 x + 3a3 x2 + 4a4 x3 + · · · n=1 00 f (x) = ∞ X n(n − 1)an xn−2 = 2a2 + 3 · 2a3 x + 4 · 3a4 x2 + · · · n=2 000 f (x) = ∞ X n=3 n(n − 1)(n − 2)an xn−3 = 3 · 2a3 + 4 · 3 · 2a4 x + · · · 264 Chapter 10 Sequences and Series By examining these it’s not hard to discern the general pattern. The kth derivative must be ∞ X f (k) (x) = n(n − 1)(n − 2) · · · (n − k + 1)an xn−k n=k = k(k − 1)(k − 2) · · · (2)ak + (k + 1)(k) · · · (2)ak+1 x + + (k + 2)(k + 1) · · · (3)ak+2 x2 + · · · We can shrink this quite a bit by using factorial notation: f (k) (x) = ∞ X n=k n! (k + 2)! an xn−k = k!ak + (k + 1)!ak+1 x + ak+2 x2 + · · · (n − k)! 2! Now substitute x = 0: f (k) (0) = k!ak + ∞ X n=k+1 n! an 0n−k = k!ak , (n − k)! and solve for ak : ak = f (k) (0) . k! Note the special case, obtained from the series for f itself, that gives f (0) = a0 . So if a function f can be represented by a series, we know just what series it is. Given a function f , the series ∞ X f (n) (0) n x n! n=0 is called the Maclaurin series for f . EXAMPLE 10.48 Find the Maclaurin series for f (x) = 1/(1−x). We need to compute the derivatives of f (and hope to spot a pattern). f (x) = (1 − x)−1 f 0 (x) = (1 − x)−2 f 00 (x) = 2(1 − x)−3 f 000 (x) = 6(1 − x)−4 f (4) = 4!(1 − x)−5 .. . f (n) = n!(1 − x)−n−1 10.10 Taylor Series 265 So f (n) (0) n!(1 − 0)−n−1 = =1 n! n! and the Maclaurin series is ∞ X n=0 n 1·x = ∞ X xn , n=0 the geometric series. A warning is in order here. Given a function f we may be able to compute the Maclaurin series, but that does not mean we have found a series representation for f . We still need to know where the series converges, and if, where it converges, it converges to f (x). While for most commonly encountered functions the Maclaurin series does indeed converge to f on some interval, this is not true of all functions, so care is required. As a practical matter, if we are interested in using a series to approximate a function, we will need some finite number of terms of the series. Even for functions with messy derivatives we can compute these using computer software like Maple or Mathematica. If we want to know the whole series, that is, a typical term in the series, we need a function whose derivatives fall into a pattern that we can discern. A few of the most important functions are fortunately very easy. EXAMPLE 10.49 Find the Maclaurin series for sin x. The derivatives are quite easy: f 0 (x) = cos x, f 00 (x) = − sin x, f 000 (x) = − cos x, f (4) (x) = sin x, and then the pattern repeats. We want to know the derivatives at zero: 1, 0, −1, 0, 1, 0, −1, 0,. . . , and so the Maclaurin series is x− ∞ X x3 x5 x2n+1 + − ··· = (−1)n . 3! 5! (2n + 1)! n=0 We should always determine the radius of convergence: |x|2n+3 (2n + 1)! |x|2 = lim = 0, n→∞ (2n + 3)! |x|2n+1 n→∞ (2n + 3)(2n + 2) lim so the series converges for every x. Since it turns out that this series does indeed converge to sin x everywhere, we have a series representation for sin x for every x. Sometimes the formula for the nth derivative of a function f is difficult to discover, but a combination of a known Maclaurin series and some algebraic manipulation leads easily to the Maclaurin series for f . 266 Chapter 10 Sequences and Series EXAMPLE 10.50 Find the Maclaurin series for x sin(−x). To get from sin x to x sin(−x) we substitute −x for x and then multiply by x. We can do the same thing to the series for sin x: x ∞ X (−1)n n=0 ∞ ∞ X X (−x)2n+1 x2n+1 x2n+2 =x (−1)n (−1)2n+1 = (−1)n+1 . (2n + 1)! (2n + 1)! (2n + 1)! n=0 n=0 As we have seen, a general power series can be centered at a point other than zero, and the method that produces the Maclaurin series can also produce such series. EXAMPLE 10.51 Find a series centered at −2 for 1/(1 − x). ∞ X If the series is an (x + 2)n then looking at the kth derivative: n=0 k!(1 − x) −k−1 = ∞ X n=k n! an (x + 2)n−k (n − k)! and substituting x = −2 we get k!3−k−1 = k!ak and ak = 3−k−1 = 1/3k+1 , so the series is ∞ X (x + 2)n . n+1 3 n=0 We’ve already seen this, on page 261. Such a series is called the Taylor series for the function, and the general term has the form f (n) (a) (x − a)n . n! A Maclaurin series is simply a Taylor series with a = 0. 10.11 Taylor’s Theorem 267 Exercises For each function, find the Maclaurin series or Taylor series centered at a, and the radius of convergence. 1. cos x ⇒ 2. ex ⇒ 3. 1/x, a = 5 ⇒ 4. ln x, a = 1 ⇒ 5. ln x, a = 2 ⇒ 6. 1/x2 , a = 1 ⇒ √ 7. 1/ 1 − x ⇒ 8. Find the first four terms of the Maclaurin series for tan x (up to and including the x3 term). ⇒ 9. Use a combination of Maclaurin series and algebraic manipulation to find a series centered at zero for x cos(x2 ). ⇒ 10. Use a combination of Maclaurin series and algebraic manipulation to find a series centered at zero for xe−x . ⇒ 10.11 Taylor's Theorem One of the most important uses of infinite series is the potential for using an initial portion of the series for f to approximate f . We have seen, for example, that when we add up the first n terms of an alternating series with decreasing terms that the difference between this and the true value is at most the size of the next term. A similar result is true of many Taylor series. THEOREM 10.52 Suppose that f is defined on some open interval I around a and suppose f (N +1) (x) exists on this interval. Then for each x 6= a in I there is a value z between x and a so that N X f (n) (a) f (N +1) (z) f (x) = (x − a)n + (x − a)N +1 . n! (N + 1)! n=0 Proof. The proof requires some cleverness to set up, but then the details are quite elementary. We want to define a function F (t). Start with the equation N X f (n) (t) F (t) = (x − t)n + B(x − t)N +1 . n! n=0 268 Chapter 10 Sequences and Series Here we have replaced a by t in the first N + 1 terms of the Taylor series, and added a carefully chosen term on the end, with B to be determined. Note that we are temporarily keeping x fixed, so the only variable in this equation is t, and we will be interested only in t between a and x. Now substitute t = a: N X f (n) (a) F (a) = (x − a)n + B(x − a)N +1 . n! n=0 Set this equal to f (x): N X f (n) (a) f (x) = (x − a)n + B(x − a)N +1 . n! n=0 Since x 6= a, we can solve this for B, which is a “constant”—it depends on x and a but those are temporarily fixed. Now we have defined a function F (t) with the property that F (a) = f (x). Consider also F (x): all terms with a positive power of (x − t) become zero when we substitute x for t, so we are left with F (x) = f (0) (x)/0! = f (x). So F (t) is a function with the same value on the endpoints of the interval [a, x]. By Rolle’s theorem (6.25), we know that there is a value z ∈ (a, x) such that F 0 (z) = 0. Let’s look at F 0 (t). Each term in F (t), except the first term and the extra term involving B, is a product, so to take the derivative we use the product rule on each of these terms. It will help to write out the first few terms of the definition: f (1) (t) f (2) (t) f (3) (t) (x − t)1 + (x − t)2 + (x − t)3 + · · · 1! 2! 3! f (N ) (t) + (x − t)N + B(x − t)N +1 . N! F (t) = f (t) + Now take the derivative: µ 0 0 F (t) = f (t) + µ + µ + µ + f (1) (t) f (2) (t) (x − t)0 (−1) + (x − t)1 1! 1! f (2) (t) f (3) (t) (x − t)1 (−1) + (x − t)2 1! 2! f (3) (t) f (4) (t) (x − t)2 (−1) + (x − t)3 2! 3! ¶ ¶ ¶ + ...+ f (N ) (t) f (N +1) (t) N −1 (x − t) (−1) + (x − t)N (N − 1)! N! + B(N + 1)(x − t)N (−1). ¶ 10.11 Taylor’s Theorem 269 Now most of the terms in this expression cancel out, leaving just F 0 (t) = f (N +1) (t) (x − t)N + B(N + 1)(x − t)N (−1). N! At some z, F 0 (z) = 0 so f (N +1) (z) (x − z)N + B(N + 1)(x − z)N (−1) N! f (N +1) (z) B(N + 1)(x − z)N = (x − z)N N! f (N +1) (z) B= . (N + 1)! 0= Now we can write N X f (n) (t) f (N +1) (z) F (t) = (x − t)n + (x − t)N +1 . n! (N + 1)! n=0 Recalling that F (a) = f (x) we get f (x) = N X f (n) (a) f (N +1) (z) (x − a)n + (x − a)N +1 , n! (N + 1)! n=0 which is what we wanted to show. It may not be immediately obvious that this is particularly useful; let’s look at some examples. EXAMPLE 10.53 Find a polynomial approximation for sin x accurate to ±0.005. From Taylor’s theorem: sin x = N X f (n) (a) f (N +1) (z) (x − a)n + (x − a)N +1 . n! (N + 1)! n=0 What can we say about the size of the term f (N +1) (z) (x − a)N +1 ? (N + 1)! 270 Chapter 10 Sequences and Series 1 0 −1 −2 −3 ......................................... ........................... ............... ............... ............ ............ ........... . . . . . . . . . . .............. .... . . . . . . . . ........... . . ................... .......... .................. ................... ....... ............ ....... ............ .. ....... ....... .......................................... ...... ...... ...... ...... ..... ..... ..... ..... ..... .... ... ... ... ... .. 1 2 3 4 5 −4 −5 Figure 10.5 sin x and a polynomial approximation. Every derivative of sin x is ± sin x or ± cos x, so |f (N +1) (z)| ≤ 1. The factor (x − a)N +1 is a bit more difficult, since x − a could be quite large. Let’s pick a = 0 and |x| ≤ π/2; if we can compute sin x for x ∈ [−π/2, π/2], we can of course compute sin x for all x. We need to pick N so that ¯ N +1 ¯ ¯ x ¯ ¯ ¯ ¯ (N + 1)! ¯ < 0.005. Since we have limited x to [−π/2, π/2], ¯ N +1 ¯ N +1 ¯ x ¯ ¯ ¯< 2 ¯ (N + 1)! ¯ (N + 1)! . The quantity on the right decreases with increasing N , so all we need to do is find an N so that 2N +1 < 0.005. (N + 1)! A little trial and error shows that N = 8 works, and in fact 29 /9! < 0.0015, so 8 X f (n) (0) n sin x = x ± 0.0015 n! n=0 x3 x5 x7 =x− + − ± 0.0015. 6 120 5040 Figure 10.5 shows the graphs of sin x and and the approximation on [0, 3π/2]. As x gets larger, the approximation heads to negative infinity very quickly, since it is essentially acting like −x7 . 10.12 Additional exercises 271 EXAMPLE 10.54 Find a polynomial approximation for ex near x = 2 accurate to ±0.005. From Taylor’s theorem: ex = N X e2 ez (x − 2)n + (x − 2)N +1 , n! (N + 1)! n=0 since f (n) (x) = ex for all n. We are interested in x near 2, and we need to keep |(x−2)N +1 | in check, so we may as well specify that |x − 2| ≤ 1, so x ∈ [1, 3]. Also ¯ ¯ ¯ ¯ e3 ez ¯ ¯≤ ¯ (N + 1)! ¯ (N + 1)! , so we need to find an N that makes e3 /(N + 1)! ≤ 0.005. This time N = 5 makes e3 /(N + 1)! < 0.0015, so the approximating polynomial is ex = e2 + e2 (x − 2) + e2 e2 e2 e2 (x − 2)2 + (x − 2)3 + (x − 2)4 + (x − 2)5 ± 0.0015. 2 6 24 120 This presents an additional problem for approximation, since we also need to approximate e2 , and any approximation we use will increase the error, but we will not pursue this complication. Exercises 1. Find a polynomial approximation for cos x on [0, π], accurate to ±10−3 ⇒ 2. How many terms of the series for ln x centered at 1 are required so that the guaranteed error on [1/2, 3/2] is at most 10−3 ? What if the interval is instead [1, 3/2]? ⇒ 3. Find the first three nonzero terms in the Taylor series for tan x on [−π/4, π/4], and compute the guaranteed error term as given by Taylor’s theorem. (You may want to use Maple or a similar aid.) ⇒ 4. Show that cos x is equal to its Taylor series for all x by showing that the limit of the error term is zero, as N approaches infinity. 5. Show that ex is equal to its Taylor series for all x by showing that the limit of the error term is zero, as N approaches infinity. 10.12 Additional exercises These problems require the techniques of this chapter, and are in no particular order. Some problems may be done in more than one way. 272 Chapter 10 Sequences and Series Exercises Determine whether the series converges. ∞ X n 1. ⇒ 2 n +4 n=0 1 1 1 1 + + + + ··· ⇒ 1·2 3·4 5·6 7·8 ∞ X n 3. ⇒ 2 + 4)2 (n n=0 2. 4. ∞ X n! ⇒ n 8 n=0 3 5 7 9 + − + + ··· ⇒ 4 8 12 16 ∞ X 1 √ 6. ⇒ 2 n +4 n=0 5. 1 − 7. ∞ X sin3 (n) ⇒ n2 n=0 8. ∞ X n ⇒ en n=0 9. 10. ∞ X n=0 ∞ X n=1 n! ⇒ 1 · 3 · 5 · · · (2n − 1) 1 √ n n ⇒ 1 2 3 4 + + + + ··· ⇒ 2·3·4 3·4·5 4·5·6 5·6·7 ∞ X 1 · 3 · 5 · · · (2n − 1) 12. ⇒ (2n)! n=1 11. ∞ X 6n 13. ⇒ n! n=0 ∞ X (−1)n−1 √ 14. ⇒ n n=1 ∞ X 2n 3n−1 15. ⇒ n! n=1 16. 1 + 17. ∞ X n=1 52 54 56 58 + + + + ··· ⇒ 22 (2 · 4)2 (2 · 4 · 6)2 (2 · 4 · 6 · 8)2 sin(1/n) ⇒ 10.12 Additional exercises 273 Find the interval and radius of convergence; you need not check the endpoints of the intervals. ∞ X 2n n 18. x ⇒ n! n=0 19. 20. ∞ X n=0 ∞ X n=1 xn ⇒ 1 + 3n xn ⇒ n3n 1 · 3 x5 1 · 3 · 5 x7 1 x3 + + + ··· ⇒ 2 3 2·4 5 2·4·6 7 ∞ X n! n 22. x ⇒ n2 n=1 21. x + ∞ X (−1)n 2n x ⇒ 23. n2 3n n=1 ∞ X (x − 1)n 24. ⇒ n! n=0 Find a series for each function, using the formula for Maclaurin series and algebraic manipulation as appropriate. 25. 2x ⇒ 26. ln(1 + x) ⇒ „ « 1+x 27. ln ⇒ 1−x √ 28. 1 + x ⇒ 1 29. ⇒ 1 + x2 30. arctan(x) ⇒ 31. Use the answer to the previous problem to discover a series for a well-known mathematical constant. ⇒ A Introduction to Maple A.1 Getting Started Maple is a powerful mathematical calculator, often called a computer algebra system (CAS) or symbolic mathematics program, because unlike most calculators it can do algebra and calculus symbolically. For example, it can expand the product (x + 1)30 into the normal representation of this polynomial, and it can factor the result back into the original √ form. It can compute the derivative or antiderivative of 3x/ x2 + 2x + 47. It can also serve as a numerical calculator, but again unlike most calculators it can do integer and rational number arithmetic exactly, no matter how many digits are involved. If it is undesirable or impossible to express a result exactly, Maple can do numerical approximation. Finally, Maple can do some quite sophisticated graphing in both 2 and 3 dimensions. To learn how to use some of Maple’s features, you should work through this tutorial step by step. What you should type will be shown in the left hand column in typewriter style; comments and explanations will appear at the right. You should do everything listed here, but you should feel free to experiment too. If you leave something out or do something extra it may change the results of some later computations. (If you want to experiment along the way, try using different names for functions and variables than the ones in the examples.) To start Maple, log in on one of the workstations in the math lab. Click on the Maple button on the bar at the bottom of the screen. When the Maple window comes up, you are ready to give commands to Maple. 275 276 Appendix A Introduction to Maple For the most part, the commands you give will be typed in the main Maple window. Some commands can be given by making selections from menus, or by clicking on buttons on the top bar. For example, to leave Maple, you may move the mouse pointer to the button titled ‘File’ and click on the left mouse button. When you do, a list of further commands appears. If you click on the last command, ‘Exit’, Maple will quit and the window will disappear. The instructions given here are for Maple version 9. To make Maple 10 act like Maple 9, choose Options from the Tools menu; choose the Display tab; change Input display to Maple Notation; click on Apply Globally. You should only have to do this the first time you use Maple. A.2 Algebra A.2.1 Numbers 2+3; 123456789* 987654321; You must type the semicolon at the end of the line to signal the end of your expression. 2+3 34*98 -45; Type this as is, without the semicolon on the first line. Maple will warn you about the missing semicolon, but will usually do the calculation anyway. Sometimes you may want to start a new line as part of the same expression; you can do this by holding the shift key while you press return. Do this after the “98”. 2/5 + 1/3; 23/45-167/345*2/3; All rational arithmetic is done exactly. 2^5; 2**5; 3^27; ifactor(%); Maple knows standard mathematical notation, including these two symbols for exponentiation. The percent sign (%) denotes the previous result (it’s called the ‘ditto’ operator). The command ifactor does integer factoring; the command factor factors polynomials. A.2 12345/98765; evalf(%); I^2; evalf(Pi); evalf(exp(1)); A.2.2 Algebra 277 You can force Maple to approximate values as ‘floating point numbers’ (i.e., using decimal notation). Some of Maple’s commands do this automatically, as we will see later. √ The number i = −1 is denoted by I; the case is significant—a lower case ‘i’ is different. Pi denotes π; again case is significant. It is very easy to mistakenly type ‘pi’ instead of ‘Pi’. When you do, things will almost certainly not work properly, but you will probably get no error messages—you just have to watch out for this. There is no built-in symbol for e ≈ 2.71828; use the function exp(x) whenever you need ex . Variables and Expressions Maple can manipulate expressions involving variables as well as numbers. Variables can be assigned values, either actual numeric values or entire expressions. (x+5)^2; f:=%; x:=5; f; x^2; Maple simply repeats the first line since x has no value. The second line saves the expression (x + 5)2 with name f ; the “:=” symbol acts like an equal sign in normal mathematics or in the programming language C. The third line assigns x the value 5. x:=’x’; x; f; This clears the value of x—this is not the same as setting it to zero! Note that Maple remembers the ‘real’ definition of f —it is still an expression involving x, not the fixed value 100. 278 Appendix A Introduction to Maple 2x; 2*x; You must always type the multiplication symbol—Maple will not accept the first line even though it is standard notation. If you make a simple typing mistake like this, you can fix it without retyping the command. In this case, Maple should correctly guess where the problem is and place the cursor between the 2 and the x; simply type the * and press return. In any case, you can always move the mouse pointer to the position of the mistake and click the left button, then make your corrections. You can adjust your position in the line using the arrow keys to the right of the main keyboard. When you press the return key, the command will be re-evaluated. expand(f); factor(%); factor(x^2+4*x+5); If a polynomial doesn’t factor, Maple leaves it alone. (As a general rule, if Maple cannot perform a calculation it will simply restate the command.) WARNING: You can go back anywhere in your Maple session and change a command. When you do this you change only the result of that command—you do not change any subsequent commands that depend on the result. If you want to re-execute subsequent commands, you must move the cursor to each command in turn and press the return key. Do not overuse this editing ability—it can make the chain of events very difficult to follow. It often is better to copy the command, paste it in at the end of the session, modify it there, and execute it as a new command. To copy and paste a command, do this: Position the mouse pointer at the beginning of the command you want to copy, press the left mouse button and hold it down, move the mouse cursor to the end of the command, and release the mouse button. (If the command is on a single line, you may also select the whole command by positioning the cursor in the line, and clicking the left mouse button three times quickly—this is a “triple click”.) The command should now be highlighted. Position the mouse pointer where you want to paste and click the middle button. A.2 A.2.3 Algebra 279 Evaluation and Substitution It usually is not a good idea to assign values to variables—they are more useful as variables. If you want Maple to evaluate an expression you can give the variables temporary values. f:=A*(x+3)^3; Note here that you use a plain ‘=’ inside subs(A=3,f); the substitution command. Two or more subs({x=5,A=2},f); substitutions are done by enclosing them in x; A; f; curly braces. The values of f , A and x don’t change. subs(x=z^2,f); f:=subs(x=z^3,f); f; You can substitute whole expressions for variables, not just values. To force the value of f to change, just reassign it—but be careful. It usually is better to give the expression a new name in case you need the original definition of f later. f:=x^2+3*x+4; f(1); g:=x -> x^2+3*x+4; g(1); h:=unapply(f,x); h(1); h(x); h(x^3); Maple makes a crucial distinction between an expression like x2 + 3x + 4 and the function which assigns to each value of x the value x2 + 3x + 4. In this example, f is an expression, g and h are functions. The same distinction exists in mathematics, but usually it is possible to ignore it, and indeed in many instances Maple will accept either a function or an expression. To employ the normal mathematical notation f (1), however, f must be a function. This example shows two ways to define a function—directly, or using an existing expression. The last two examples show that you can evaluate a function on expressions, not just numbers. 280 Appendix A Introduction to Maple map(g,[-3,-2,-1,0,1,2,3]); map(g,{-3,-2,-1,0,1,2,3}); A.2.4 Using the ‘map’ operation and a function (not an expression) you can evaluate a function on a list of values. The list can be enclosed in square brackets or in set braces— but notice the difference! The set braces produce a set as a result, meaning that repeated values are shown only once, and the order does not necessarily correspond to the order of the input values. In general, the square bracket form is more useful. Solving Equations Maple will solve a limited class of equations solve(x^2-x-10=10,x); solve(x^2-x-20,x); solutions:=solve(g(x),x); solve(x^5+4*x^3-3*x^2+10,x); solve(tan(x)=x+1,x); allvalues(%); solve(tan(x)=x,x); allvalues(%); solve(sin(x)=cos(x)-1,x); exactly and a broader class approximately. The solve command will give exact answers if possible, including complex answers. If Maple can’t solve an equation it may print nothing, or it may display the equation in a standard form, preceded by ‘RootOf’. You can ask Maple to attempt to evaluate roots displayed as a RootOf using allvalues. Maple will attempt to find all solutions for an equation or a RootOf, but often will not succeed. Do not rely on the solve command alone when looking for solutions to equations—at the very least, you should graph your functions so that you know where roots appear to be. Note that if you give an expression instead of an equation Maple assumes that you mean ‘= 0’. A.2 Algebra 281 map(g,[solutions]); simplify(%); In some cases, Maple will not automatically simplify as much as it can, so you need to nudge it a bit. In this case, of course, we knew that the values should be zero. If the result had not simplified to zero, careful scrutiny of the problem would be called for. f:=x^3+3*x^2+x+5: solve(f,x); fsolve(f,x); fsolve(f,x,complex); You can end a line with a colon instead of a semicolon—it tells Maple not to display the result. This can be particularly handy when the result is very long, or simply when you either know what it is or do not need to know (as when you will be using the result to compute a further result). The fsolve operation tells Maple to approximate the roots. In this example, Maple can compute the roots exactly, but it may be more informative to see approximations. Note that Maple only computes real roots unless the ‘complex’ option is present. If the function is not a polynomial, fsolve will find at most one solution, but for polynomials it will find all solutions. g:=x -> exp(x)+x^2-10*x; fsolve(g(x),x); fsolve(g(x),x,-2..2); g(%); fsolve(g(x),x,2..4); g(%); You can specify a range in fsolve—you would normally do this when you know there are multiple roots and you want to approximate a particular one. In this case, a graph of the function clearly indicates a root near 0 and a root near 3. Because g is a function, and fsolve expects an expression, it is necessary to use g(x) in fsolve. (I made g a function so that it would be easy to check the answers.) 282 Appendix A Introduction to Maple solve({3*x+5*y=6,4*x-6*y=-8}, Maple can solve equations simultaneously, {x,y}); with either solve or fsolve. As with single h1:=3*x-6*y-5; equations, fsolve may not find all solutions, h2:=-4*x*y+7; but you can specify ranges for x and y to solve({h1,h2},{x,y}); force fsolve to find particular solutions. evalf(%); fsolve({h1,h2},{x,y}); fsolve({h1,h2},{x,y},{x=-2..0,y=-2..0}); A.3 Plotting Maple can do two and three dimensional graphing. There are many ways to adjust the plots that Maple produces; we’ll cover just the basics here. plot(f,x=-10..10); Maple will accept either an expression or a plot(g(x),x=-5..5); function to plot, but notice that the form of plot(g,-10..10); the range is a bit different in the two cases. plot(g,-10..10,y=-10..10); For an expression, use ‘x=’ before the range, but for a function leave it out. You can also specify the range for y explicitly. This is often useful if the function gets very large on the interval. You can change the appearance of the plot in a variety of ways. Click in the plot and you should see a border appear around it; then you can explore the items on the plot menu. If you prefer, you can have plots come up in separate windows; use the Options item on the Tools menu in the main Maple window, then open the Display tab and change the plot display. A.3 Plotting 283 plot([1+2*sin(t),t,t=-Pi..Pi], coords=polar); You can do plots in polar coordinates. Inside the square brackets, the first expression is the radius, the second is the angle, and the third is the range of the variable. In this case, the angle and the variable are the same, but in general the angle could be a more complicated function of the variable. plot({x^2,x^3},x=-5..5); plot({ [1+2*sin(t),t,t=-Pi..Pi], [cos(4*t),t,t=-Pi..Pi]}, coords=polar); You can put more than one curve in the same plot by enclosing a list of expressions in curly braces. r:=sqrt(x^2+y^2); f:=sin(r)/r; plot3d(f,x=-10..10, y=-10..10); Try changing the appearance of a 3D plot by using the Style, Color, Axes and Projection menus. If you position the cursor in the drawing area and press and hold the left mouse button, you can move the mouse to change the orientation of the plot—try it. with(plots): polarplot(1+sin(t)); polarplot(sin(t/3)); polarplot(sin(t/3), t=0..3*Pi); When you start Maple, only the most basic plotting commands are available. By typing “with(plots)” you tell Maple to load a number of special purpose plotting functions. One of the most useful is the polarplot routine shown here. By default, Maple uses the range [−π, π]; the last command shows how to specify a different range. 284 Appendix A Introduction to Maple A.4 Calculus A.4.1 Limits f:=x^2+4*x+30: limit(f,x=3); subs(x=3,f); g:=sin(x)/x; limit(g,x=0); limit(g,x=infinity); g:=x/abs(x); limit(g,x=0); limit(g,x=0,left); limit(g,x=0,right); A.4.2 The limit command limit(f,x=a) computes the limit limx→a f (x). Maple can handle many non-trivial limit problems and limits at infinity. Differentiation f:=x->x^3+4*x^2+3*x+4: fp:=diff(f(x),x); critpts:=solve(fp,x); map(f,[critpts]); evalf(%); evalf(critpts); plot({f(x),fp},x=-5..5, y=-5..10); The diff command does differentiation. Here I have found the derivative with respect to x, then the x coordinates of the critical points of f , then the value of f at these points, then floating point representations for the x and y coordinates. Because Maple can do calculations exactly, you should do approximations at the end; it would not be a good idea to approximate critpts before substitution into f (though in this simple case the results are almost identical). Note that if you click in the plot, the coordinates of the point under the cursor appear at the upper left of the Maple window. Youe can use this to approximate critical points and double check the computed values. A.4 A.4.3 285 Implicit Differentiation eqn:=x^2+y^2=1; xp:=implicitdiff(eqn,x,y); yp:=implicitdiff(eqn,y,x); subs({x=1/2,y=sqrt(3/4)},yp); A.4.4 Calculus Notice that the order of the second two arguments to the implicitdiff function is important—xp is dx/dy and yp is dy/dx. The last line computes y 0 at a specific point, √ (1/2, 3/2). Integration f:=(2*z-3)*sqrt(z-3); g:=int(f,z); diff(g,z); factor(%); The int command shown here will compute an antiderivative. The computer is not infallible, and you may not notice a typing mistake, so it’s not a bad idea to use Maple to check your work. Here, I’ve taken the derivative to check the antiderivative. int(f,z=5..10); simplify(%); evalf(%); subs(z=10,g)-subs(z=5,g); To do definite integration give the limits after the variable. Of course, you can also do it yourself using the antiderivative. Notice that the answer can look quite different before simplification. f:=1/sqrt(x): int(f,x=0..1); f:=1/x^2: int(f,x=0..1); int(f,x=1..infinity); f:=exp(-x^2): int(f,x=-infinity..infinity); int(sin(x),x=0..infinity); Maple can do many improper integrals exactly. If the integral does not converge, Maple may answer with ‘∞’ if appropriate, or it may give an error message. f:=tan(x^2): int(f,x); int(f,x=0..1); evalf(%); Int(f,x=0..1); evalf(%); Maple can’t find antiderivatives of some functions. In such cases you can use evalf to approximate the value of a definite integral. If you don’t want Maple to even try to find an antiderivative, use the capital I form of the integration operator. 286 Appendix A Introduction to Maple A.5 Adding text to a Maple session You can add plain text to a Maple session. This is especially useful if you are going to give a printed or electronic version of a session to someone else, since it lets you explain what is going on. It is also a good idea to add text to remind yourself at a later date what you were doing and why. To add text above a command, position the cursor anywhere in the command. Then open the Insert menu, and then the Paragraph submenu, and choose Before Cursor. When you type you will get ordinary text that will not be interpreted as part of the command. A.6 Printing BEFORE YOU PRINT: You should clean up your session before you print, so that you do not waste paper and so that the printed copy is attractive and easy to read. If you want to print only part of the session, first save the session (see the section Saving your work ) and then cut out the portions of the session that you do not want to print. You might want to save the trimmed version using a different name. To print your worksheet, choose Print from the File menu, or click on the print button (the fourth little button from the left in the menu bar). If you would like to print just a plot, you need to display it in a window of its own. You can do this by changing the plot display option, as described in the Plotting section. Then position the cursor in the plot command and press return. The plot should open in a new window, and you can use the Print menu in that window to print the plot. A.7 Saving your work You can save your Maple worksheet so that you can come back to it at a later time. Choose the Save or Save As. . . item from the File menu in Maple; a new window will pop up. (Use Save As if you want to save your worksheet under a new name.) You may also save a worksheet using the third button from the left in the menu bar. When you reload a worksheet (with the Open item on the File menu or with the second button from the left) Maple will not automatically recreate all the values created in the worksheet. You may selectively re-execute commands in the worksheet, or you may use the Execute item on the Edit menu to execute the entire worksheet. A.8 Getting Help 287 A.8 Getting Help There are (somewhat out of date) manuals for Maple in the lab. You can also get help ‘on line’ from the Maple program. The help system is very good and always available as you work in Maple. ?int If you know the command you need help on, just type its name after a question mark. You can browse through the entire help system or search it using the Help menu. B Selected Answers 1.1.1. (2/3)x + (1/3) 1.1.2. y = −2x 1.1.3. (−2/3)x + (1/3) 1.1.4. y = 2x + 2, 2, −1 1.1.16. y ( = 0.15x 0 ≤ x ≤ 19450 0.28x − 2528.50 19450 < x ≤ 47050 0.33x − 4881 47050 < x ≤ 97620 1.1.5. y = −x + 6, 6, 6 1.1.17. (a) P = −0.0001x + 2 (b) x = −10000P + 20000 1.1.6. y = x/2 + 1/2, 1/2, −1 1.1.18. (2/25)x − (16/5) 1.1.7. y = 3/2, y-intercept: 3/2, no x-intercept 1.1.8. y = (−2/3)x − 2, −2, −3 1.1.9. yes 1.1.10. y = 0, y = −2x + 2, y = 2x + 2 1.1.11. y = 75t, 164 minutes 1.1.12. y = (9/5)x + 32, (−40, −40) 1.1.13. y = 0.15x + 10 1.1.14. 0.03x + 1.2 1.1.15. (a) y = ( 0 0 ≤ x < 100 (x/10) − 10 100 ≤ x ≤ 1000 x − 910 1000 < x 1.2.1. (a) x2 + y 2 = 9 (b) (x − 5)2 + (y − 6)2 = 9 (c) (x + 5)2 + (y + 6)2 = 9 1.2.2. (a) ∆x = 2, ∆y = 3, m = 3/2, √ y = (3/2)x − 3, 13 (b) ∆x = −1, ∆y = 3, m = −3, √ y = −3x + 2, 10 (c) ∆x = −2, ∆y = −2, m = 1, √ y = x, 8 1.2.6. (x + 2/7)2 + (y − 41/7)2 = 1300/49 1.3.1. {x | x ≥ 3/2} 289 290 Appendix B Selected Answers 1.3.2. {x | x 6= −1} 2.3.2. 5 1.3.3. {x | x 6= 1 and x 6= −1} 2.3.3. 0 1.3.4. {x | x < 0} 2.3.4. undefined 1.3.5. {x | x ∈ R}, i.e., all x 2.3.5. 1/6 1.3.6. {x | x ≥ 0} 2.3.6. 0 1.3.7. {x | h − r ≤ x ≤ h + r} 2.3.7. 3 2.3.8. 172 1.3.8. {x | x ≥ 1} 1.3.9. {x | −1/3 < x < 1/3} 1.3.10. {x | x ≥ 0 and x 6= 1} 1.3.11. {x | x ≥ 0 and x 6= 1} 1.3.12. R 1.3.13. {x | x ≥ 3}, {x | x ≥ 0} 1.3.14. A = x(500 − 2x), {x | 0 ≤ x ≤ 250} 2 1.3.15. V = r(50 − πr ), {r | 0 < r ≤ p 50/π} 1.3.16. A = 2πr2 +2000/r, {r | 0 < r < ∞} 2.1.1. −5, −2.47106145, −2.4067927, −2.400676, −2.4 2.3.9. 0 2.3.10. 2 2.3.11. does not exist √ 2.3.12. 2 2.3.13. 3a2 2.3.14. 512 2.3.15. −4 2.3.16. 0 2.3.18. (a) 8, (b) 6, (c) dne, (d) −2, (e) −1, (f) 8, (g) 7, (h) 6, (i) 3, (j) −3/2, (k) 6, (l) 2 √ 2.4.1. −x/ 169 − x2 2.1.2. −4/3, −24/7, 7/24, 3/4 2.4.2. −9.8t 2.1.3. −0.107526881, −0.11074197, −1 −1 −0.1110741, → 3(3 + ∆x) 9 2.4.3. 2x + 1/x2 3 + 3∆x + ∆x2 2.1.4. →3 1 + ∆x 2.1.5. 3.31, 3.003001, 3.0000, 3 + 3∆x + ∆x2 → 3 2.4.4. 2ax + b 2.4.5. 3x2 3.1.1. 100x99 3.1.2. −100x−101 3.1.3. −5x−6 2.1.6. m 3.1.4. πxπ−1 2.2.1. 10, 25/2, 20, 15, 25, 35. 3.1.5. (3/4)x−1/4 2.2.2. 5, 4.1, 4.01, 4.001, 4 + ∆t → 4 3.1.6. −(9/7)x−16/7 2.2.3. −10.29, −9.849, −9.8049, −9.8 − 4.9∆t → −9.8 3.1.7. −98t/10, −98/10 2.3.1. 7 3.2.2. −20x4 + 6x + 10/x3 3.2.1. 15x2 + 24x 291 Appendix B Selected Answers 3.2.3. −30x + 25 3.2.4. 6x2 + 2x − 8 3.2.5. 3x2 + 6x − 1 √ 3.2.6. 9x2 − x/ 625 − x2 3.2.7. y = 13x/4 + 5 3.2.8. y = 24x − 48 − π 3 3.2.9. −49t/5 + 5, −49/5 3.2.12. n X kak xk−1 k=1 3.2.13. x3 /16 − 3x/4 + 4 3.3.1. 3x2 (x3 − 5x + 10) + x3 (3x2 − 5) 3.3.2. (x2 + 5x − 3)(5x4 − 18x2 + 6x − 7) + (2x + 5)(x5 − 6x3 + 3x2 − 7x + 1) √ √ 625 − x2 x x √ 3.3.3. −√ 2 x 625 − x2 √ −1 20 625 − x2 √ 3.3.4. − x21 x19 625 − x2 3.3.5. f 0 = 4(2x − 3), y = 4x − 7 x3 (3x2 − 5) 3x2 − x3 − 5x + 10 (x3 − 5x + 10)2 2x + 5 3.4.2. 5 − x − 6x3 + 3x2 − 7x + 1 (x2 + 5x − 3)(5x4 − 18x2 + 6x − 7) (x5 − 6x3 + 3x2 − 7x + 1)2 3.4.1. 1 x3/2 + √ √ (625 − x2 )3/2 2 x 625 − x2 √ −1 20 625 − x2 √ 3.4.4. − x21 x19 625 − x2 3.4.3. √ 3.5.2. 3x2 − 4x + 2/ x 3.5.3. 6(x2 + 1)2 x √ √ 3.5.4. 169 − x2 − x2 / 169 − x2 √ 3.5.5. (2x − 4) 25 − x2 − √ (x2 − 4x + 5)x/ 25 − x2 √ 3.5.6. −x/ r2 − x2 √ 3.5.7. 2x3 / 1 + x4 1 √ 3.5.8. √ 4 x(5 − x)3/2 3.5.9. 6 + 18x 2x + 1 x2 + x + 1 3.5.10. + 1−x (1 − x)2 √ √ 3.5.11. −1/ 25 − x2 − 25 − x2 /x2 µ ¶ .r 1 −169 169 3.5.12. −1 −x 2 2 x x 3.5.13. 3x2 − 2x + 1/x2 p 2 x3 − x2 − (1/x) 3.5.14. 300x (100 − x2 )5/2 3.5.15. 1 + 3x2 3(x + x3 )2/3 Ã 3.5.16. 4x3 + 4x 4x(x2 + 1) + p 2 1 + (x2 + 1)2 q p 2 (x2 + 1)2 + 1 + (x2 + 1)2 3.5.17. 5(x + 8)4 3.5.18. −3(4 − x)2 3.5.19. 6x(x2 + 5)2 3.4.5. y = 17x/4 − 41/4 3.5.20. −12x(6 − 2x2 )2 3.4.6. y = 11x/16 − 15/16 3.5.21. 24x2 (1 − 4x3 )−3 3.4.8. y = 19/169 − 5x/338 3.5.22. 5 + 5/x2 3.4.9. 13/18 3.5.23. −8(4x − 1)(2x2 − x + 3)−3 3.5.1. 4x3 − 9x2 + x + 7 3.5.24. 1/(x + 1)2 ! . 292 Appendix B Selected Answers 3.5.25. 3(8x − 2)/(4x2 − 2x + 1)2 sin x √ √ + x cos x 2 x cos x 4.4.3. − 2 sin x (2x + 1) sin x − (x2 + x) cos x 4.4.4. sin2 x − sin x cos x 4.4.5. p 1 − sin2 x 4.4.2. 3.5.26. −3x2 + 5x − 1 3.5.27. 120x4 − 576x3 + 888x2 − 480x + 96 3.5.28. −2/(x − 1)2 3.5.29. 4x/(x2 + 1)2 3.5.30. (x2 − 6x + 7)/(x − 3)2 3.5.31. −5/(3x − 4)2 4.5.1. cos2 x − sin2 x 3.5.32. 60x4 + 72x3 + 18x2 + 18x − 6 2 4.5.2. − sin x cos(cos x) 2 3.5.33. (5 − 4x)/((2x + 1) (x − 3) ) 4.5.3. tan x + x sec2 x √ 2 x tan x 3.5.35. 56x6 + 72x5 + 110x4 + 100x3 + 60x2 + 28x + 6 4.5.4. sec2 x(1 + sin x) − tan x cos x (1 + sin x)2 3.5.36. y = 23x/96 − 29/96 4.5.5. − csc2 x 3.5.37. y = 3 − 2x/3 4.5.6. − csc x cot x 3.5.38. y = 13x/2 − 23/2 4.5.7. 3x2 sin(23x2 ) + 46x4 cos(23x2 ) 3.5.39. y = 2x − 11 √ √ 20 + 2 5 3 5 3.5.40. y = p √ x+ p √ 5 4+ 5 5 4+ 5 4.5.8. 0 3.5.34. 1/(2(2 + 3x)2 ) 4.5.9. −6 cos(cos(6x)) sin(6x) 4.5.10. sin θ/(cos θ + 1)2 4.1.1. 2nπ − π/2, any integer n 4.5.11. 5t4 cos(6t) − 6t5 sin(6t) 4.1.2. nπ ± π/6, any integer n √ √ 4.1.3. ( 2 + 6)/2 √ √ 4.1.4. −(1 + 3)/(1 − 3) 4.5.12. 3t2 (sin(3t) + t cos(3t))/ cos(2t) + 2t3 sin(3t) sin(2t)/ cos2 (2t) 4.1.11. t = π/2 4.3.1. 5 4.3.2. 7/2 4.3.3. 3/4 4.3.4. 1 √ 4.3.5. − 2/2 4.3.6. 7 4.3.7. 2 √ √ √ 4.4.1. sin( x) cos( x)/ x 4.5.13. nπ/2, any integer n 4.5.14. π/2 + nπ, any integer n √ √ 4.5.15. 3x/2 + 3/4 − 3π/6 √ √ 4.5.16. 8 3x + 4 − 8 3π/3 √ √ 4.5.17. 3 3x/2 − 3π/4 4.5.18. π/6 + 2nπ, 5π/6 + 2nπ, any integer n 2 4.7.1. 2 ln(3)x3x cos x − sin x 4.7.2. ex 2x 4.7.3. 2e 293 Appendix B Selected Answers 4.7.4. ex cos(ex ) 4.7.5. cos(x)esin x µ ¶ sin x sin x 4.7.6. x cos x ln x + x 4.7.7. 3x2 ex + x3 ex √ √ √ √ 4.9.13. ( 3, 2 3), (− 3, −2 3), √ √ √ √ (2 3, 3), (−2 3, − 3) √ √ 4.9.14. y = 7x/ 3 − 8/ 3 1/3 1/3 1/3 1/3 4.9.15. y = (−y1 x+y1 x1 +x1 y1 )/x1 4.9.16. (y − y1 )/(x − x1 ) = (2x31 + 2x1 y12 − x1 )/(2y13 + 2y1 x21 + y1 ) x 4.7.8. 1 + 2 ln(2) 2 4.7.9. −2x ln(3)(1/3)x 4.7.10. e4x (4x − 1)/x2 4.7.11. (3x2 + 3)/(x3 + 3x) 4.7.12. − tan(x) 4.7.13. (1 − ln(x2 ))/(x2 p ln(x2 )) 4.10.3. −1/(1 + x2 ) 2x 4.10.4. √ 1 − x4 ex 4.10.5. 1 + e2x 5.1.1. min at x = 1/2 4.7.14. sec(x) 5.1.2. min at x = −1, max at x = 1 4.7.15. xsin(x) (cos(x) ln(x) + sin(x)/x) 5.1.3. max at x = 2, min at x = 4 4.7.20. e 5.1.4. min at x = ±1, max at x = 0. 4.8.1. 0 5.1.5. min at x = 1 4.8.2. ∞ 5.1.6. none 4.8.3. 1 5.1.7. none 4.8.4. 0 4.8.5. 0 5.1.8. min at x = 7π/12 + kπ, max at x = −π/12 + kπ, for integer k. 4.8.7. y = 1 and y = −1 5.1.9. none 4.9.1. x/y 5.1.10. local max at x = 5 4.9.2. −(2x + y)/(x + 2y) 5.1.11. local min at x = 49 4.9.3. −(3x2 + y 2 − 2xy)/(2xy − 3y 2 − x2 ) 5.1.12. local min at x = 0 4.9.4. sin(x) sin(y)/(cos(x) cos(y)) √ √ 4.9.5. − y/ x 5.1.15. one 5.2.1. min at x = 1/2 4.9.6. (y sec2 (x/y)−y 2 )/(x sec2 (x/y)+y 2 ) 5.2.2. min at x = −1, max at x = 1 4.9.7. (y − cos(x + y))/(cos(x + y) − x) 5.2.3. max at x = 2, min at x = 4 4.9.8. −y 2 /x2 5.2.4. min at x = ±1, max at x = 0. 4.9.9. 1 5.2.5. min at x = 1 4.9.11. y = 2x ± 6 5.2.6. none 4.9.12. y = x/2 ± 3 5.2.7. none 294 Appendix B Selected Answers 5.2.8. min at x = 7π/12 + kπ, max at x = −π/12 + kπ, for integer k. 5.2.9. none 5.2.10. max at x = 0, min at x = ±11 5.2.11. min at x = −3/2, neither at x = 0 5.2.13. min at nπ, max at π/2 + nπ 5.2.14. min at 2nπ, max at (2n + 1)π 5.2.15. min at π/2+2nπ, max at 3π/2+2nπ 5.3.1. min at x = 1/2 5.3.2. min at x = −1, max at x = 1 √ 5.4.4. concave up when x < −1/ 3 or √ x > 1/ 3, concave down when √ √ −1/ 3 < x < 1/ 3 5.4.5. concave up when x < 0 or x > 2/3, concave down when 0 < x < 2/3 5.4.6. concave up when x < 0, concave down when x > 0 5.4.7. concave up when x < −1 or x > 1, concave down when −1 < x < 0 or 0 0 6.1.2. 25 × 25 5.4.3. concave down when x < 3, concave up when x > 3 6.1.4. w = l = 2 · 52/3 , h = 52/3 , h/w = 1/2 6.1.3. P/4 × P/4 Appendix B Selected Answers 6.1.5. √ 3 100 × √ 3 √ 100 × 2 3 100, h/s = 2 6.1.6. w = l = 21/3 V 1/3 , h = V 1/3 /22/3 , h/w = 1/2 6.1.7. 1250 square feet 6.1.8. l2 /8 square feet 6.1.9. $5000 6.1.10. 100 6.1.11. r2 6.1.12. h/r = 2 6.1.13. h/r = 2 6.1.14. r = 5, h = 40/π, h/r = 8/π 6.1.15. 8/π 6.1.16. 4/27 6.1.17. Go direct from A to D. 6.1.18. (a) 2, (b) 7/2 √ √ √ 3 3 1 1 3 6.1.19. × + × − 6 6 2 4 12 √ 2 6.1.20. (a) a/6, (b) (a+b− a − ab + b2 )/6 6.1.21. 1.5 meters wide by 1.25 meters tall 6.1.22. If k ≤ 2/π the ratio is (2 − kπ)/4; if k ≥ 2/π, the ratio is zero: the window should be semicircular with no rectangular part. 6.1.23. a/b √ √ √ 6.1.24. w = 2r/ 3, h = 2 2r/ 3 √ 6.1.25. 1/ 3 ≈ 58% 6.1.26. 18 × 18 × 36 6.1.27. r = 5/(2π)1/3 ≈ 2.7 cm, h = 5 · 25/3 /π 1/3 = 4r ≈ 10.8 cm µ ¶1/3 750 2π 2 6.1.28. h = , r = π 7502 µ ¶1/6 7502 2π 2 6.1.29. h/r = √ 295 2 6.1.30. The ratio of the volume of the sphere to the volume of the cone is √ 1033/4096 + 33/4096 17 ≈ 0.2854, so the cone occupies approximately 28.54% of the sphere. √ √ 6.1.31. P should be at distance c 3 a/( 3 a + √ 3 b) from charge A. 6.1.32. 1/2 6.1.33. $7000 6.1.34. There is a critical point when sin θ1 /v1 = sin θ2 /v2 , and the second derivative is positive, so there is a minimum at the critical point. 6.2.1. 1/(16π) cm/s 6.2.2. 3/(1000π) meters/second 6.2.3. 1/4 m/s 6.2.4. −6/25 m/s 6.2.5. 80π mi/min √ 6.2.6. 3 5 ft/s 6.2.7. 20/(3π) cm/s 6.2.8. 13/20 ft/s √ 6.2.9. 5 10/2 m/s 6.2.10. 75/64 m/min 6.2.11. 145π/72 m/s 6.2.12. 25π/144 m/min √ 6.2.13. π 2/36 ft3 /s 6.2.14. tip: 6 ft/s, length: 5/2 ft/s 6.2.15. tip: 20/11 m/s, length: 9/11 m/s √ 6.2.16. 380/ 3 − 150 ≈ 69.4 mph 6.2.17. 81 km/hr 296 Appendix B Selected Answers 6.2.18. 18 m/s √ 6.2.19. 136 475/19 ≈ 156 km/hr 6.2.20. −50 m/s 7.2.1. (16/3)x3/2 + C 7.2.2. t3 + t + C √ 7.2.3. 8 x + C 7.2.4. −2/z + C 6.2.21. 68 m/s √ 7.2.5. 7 ln s + C 6.2.22. 3800/ 329 ≈ 210 km/hr 3 √ √ √ 6.2.23. 820/ 329 + 150 57/ 47 ≈ 210 km/hr 7.2.6. (5x + 1) /15 + C 7.2.7. (x − 6)3 /3 + C 6.2.24. 4000/49 m/s 7.2.8. 2x5/2 /5 + C 6.2.25. (a) x = a cos θ − a sin θ cot(θ + β) = √ a sin β/ sin(θ + β), (c) ẋ ≈ 3.79 cm/s 7.2.9. −4/ x + C 7.2.10. 4t − t2 + C, t < 2; t2 − 4t + 8 + C, 6.3.1. x3 = 1.475773162 t≥2 6.3.2. 2.15 7.2.11. 87/2 6.3.3. 3.36 7.2.12. 2 6.3.4. 2.19 or 1.26 7.2.13. ln(10) 6.4.1. ∆y = 65/16, dy = 2 7.2.14. e5 − 1 p 6.4.2. ∆y = 11/10 − 1, dy = 0.05 7.2.15. 34 /4 6.4.4. dV = 32π/25 7.2.16. 26 /6 − 1/6 6.5.1. c = 1/2 7.2.17. x2 − 3x √ 6.5.2. c = 18 − 2 7.2.18. 2x(x4 − 3x2 ) 2 6.5.6. x3 /3 + 47x2 /2 − 5x + k 7.2.19. ex 6.5.7. arctan x + k 6.5.8. x4 /4 − ln x + k 6.5.9. − cos(2x)/2 + k 7.1.1. 10 7.1.2. 35/3 7.1.3. x2 7.1.4. 2x2 7.1.5. 2x2 − 8 7.1.6. 2b2 − 2a2 7.1.7. 4 rectangles: 41/4 = 10.25, 8 rectangles: 183/16 = 11.4375 7.1.8. 23/4 7.2.20. 2xex 4 7.3.1. It rises until t = 100/49, then falls. The position of the object at time t is s(t) = −4.9t2 + 20t + k. The net distance traveled is −45/2, that is, it ends up 45/2 meters below where it started. The total distance traveled is 6205/98 meters. Z 2π 7.3.2. sin t dt = 0 0 √ 7.3.3. net: 2π, total: 2π/3 + 4 3 7.3.4. 8 7.3.5. 17/3 Appendix B Selected Answers 297 7.3.6. A = 18, B = 44/3, C = 10/3 8.3.1. − ln | csc x + cot x| + C 8.1.1. −(1 − t)10 /10 + C 8.3.2. − csc x cot x/2 − (1/2) ln | csc x + cot x| + C √ √ 8.3.3. x x2 − 1/2 − ln |x + x2 − 1|/2 + C √ 8.3.4. x 9 + 4x2 /2 + √ (9/4) ln |2x + 9 + 4x2 | + C 8.1.2. x5 /5 + 2x3 /3 + x + C 8.1.3. (x2 + 1)101 /202 + C 8.1.4. −3(1 − 5t)2/3 /10 + C 8.1.5. (sin4 x)/4 + C 8.1.6. −(100 − x2 )3/2 /3 + C p 8.1.7. −2 1 − x3 /3 + C 8.1.8. sin(sin πt)/π + C 8.1.9. 1/(2 cos2 x) = (1/2) sec2 x + C 8.1.10. − ln | cos x| + C 8.3.6. arcsin(x)/8 − sin(4 arcsin x)/32 + C √ 8.3.7. ln |x + 1 + x2 | + C √ 8.3.8. (x + 1) x2 + 2x/2 − √ ln |x + 1 + x2 + 2x|/2 + C 8.3.9. − arctan x − 1/x + C √ 8.3.10. 2 arcsin(x/2) − x 4 − x2 /2 + C √ √ √ 8.3.11. arcsin( x) − x 1 − x + C √ 8.3.12. (2x2 + 1) 4x2 − 1/24 + C 8.1.11. 0 8.1.12. tan2 (x)/2 + C 8.1.13. 1/4 8.1.14. − cos(tan x) + C 8.4.1. cos x + x sin x + C 8.1.15. 1/10 √ 8.1.16. 3/4 8.4.2. x2 sin x − 2 sin x + 2x cos x + C 8.1.17. (27/8)(x2 − 7)8/9 7 8.1.18. −(3 + 1)/14 8.4.3. (x − 1)ex + C 2 8.4.4. (1/2)ex + C 8.4.5. (x/2) − sin(2x)/4 + C = (x/2) − (sin x cos x)/2 + C 8.1.19. 0 8.1.20. f (x)2 /2 8.2.1. x/2 − sin(2x)/4 + C 3 8.2.2. − cos x + (cos x)/3 + C 8.2.3. 3x/8 − (sin 2x)/4 + (sin 4x)/32 + C 5 8.3.5. −(1 − x2 )3/2 /3 + C 3 8.2.4. (cos x)/5 − (cos x)/3 + C 8.2.5. sin x − (sin3 x)/3 + C 8.4.6. x ln x − x + C 8.4.7. (x2 arctan x + arctan x − x)/2 + C 8.4.8. −x2 cos x + 2x sin x + 2 cos x + C 8.4.9. x2 /4−(cos2 x)/4−(x sin x cos x)/2+ C 8.2.8. −2(cos x)5/2 /5 + C 8.4.10. x/4 − (x cos2 x)/2 + (cos x sin x)/4 + C √ √ √ 8.4.11. x arctan( x)+arctan( x)− x+C √ √ √ 8.4.12. 2 sin( x) − 2 x cos( x) + C 8.2.9. tan x − cot x + C 8.4.13. sec x csc x − 2 cot x + C 8.2.6. x/8 − (sin 4x)/32 + C 8.2.7. (sin3 x)/3 − (sin5 x)/5 + C 3 8.2.10. (sec x)/3 − sec x + C 8.5.1. − ln |x − 2|/4 + ln |x + 2|/4 + C 298 Appendix B Selected Answers 8.5.2. −x3 /3 − 4x − 4 ln |x − 2|+ 4 ln |x + 2| + C 8.5.3. −1/(x + 5) + C 8.5.4. −x − ln |x − 2| + ln |x + 2| + C 8.5.5. −4x + x3 /3 + 8 arctan(x/2) + C 8.5.6. (1/2) arctan(x/2 + 5/2) + C 8.5.7. x2 /2 − 2 ln(4 + x2 ) + C 8.5.8. (1/4) ln |x + 3| − (1/4) ln |x + 7| + C 8.5.9. (1/5) ln |2x − 3| − (1/5) ln |1 + x| + C 8.5.10. (1/3) ln |x| − (1/3) ln |x + 3| + C (t + 4)4 +C 8.6.1. 4 (t2 − 9)5/2 8.6.2. +C 5 2 8.6.3. 8.6.4. 8.6.5. 8.6.6. 8.6.7. 8.6.8. 8.6.9. 8.6.10. 8.6.11. 8.6.12. 8.6.13. (et + 16)2 +C 4 2 cos t − cos3 t + C 3 2 tan t +C 2 ln |t2 + t + 3| + C 1 ln |1 − 4/t2 | + C 8 1 tan(arcsin(t/5)) + C = 25 t √ +C 25 25 − t2 2√ sin 3t + C 3 t tan t + ln | cos t| + C √ 2 et + 1 + C 3t sin 2t sin 4t + + +C 8 4 32 ln |t| ln |t + 3| − +C 3 3 p −1 + C = − 1 + t2 /t + C sin arctan t −1 8.6.15. +C 2(1 + tan t)2 8.6.14. 8.6.16. 8.6.17. 8.6.18. 8.6.19. 8.6.20. 8.6.21. 8.6.22. 8.6.23. 8.6.24. 8.6.25. 8.6.26. 8.6.27. 8.6.28. 9.1.1. (t2 + 1)5/2 (t2 + 1)3/2 − +C 5 3 et sin t − et cos t +C 2 (t3/2 + 47)4 +C 6 2 1 − +C 2 3/2 3(2 − t ) (2 − t2 )1/2 ln | sin(arctan(2t/3))| +C = 9 (ln(4t2 ) − ln(9 + 4t2 ))/18 + C (arctan(2t))2 +C 4 3 ln |t + 3| ln |t − 1| + +C 4 4 cos7 t cos5 t − +C 7 5 −1 +C t−3 −1 +C ln t t2 (ln t)2 t2 ln t t2 − + +C 2 2 4 3 2 t (t − 3t + 6t − 6)e + C √ √ 5+ 5 ln(2t + 1 − 5) + 10√ √ 5− 5 ln(2t + 1 + 5) + C 10 √ 8 2/15 9.1.2. 1/12 9.1.3. 9/2 9.1.4. 4/3 9.1.5. 2/3 − 2/π √ 9.1.6. 3/π − 3 3/(2π) − 1/8 Appendix B Selected Answers 9.1.7. 1/3 √ 9.1.8. 10 5/3 − 6 9.4.2. 4/3 9.1.9. 500/3 9.4.4. π/4 9.4.3. 1/A 9.1.11. 1/5 9.4.5. −1/3, 1 √ √ 9.4.6. −4 1224 ft/s; −8 1224 ft/s 9.1.12. 1/6 9.5.1. ≈ 5, 305, 028, 517 N-m 9.1.10. 2 9.2.1. 1/π, 5/π 9.5.2. ≈ 4, 457, 854, 041 N-m 9.2.2. 0, 245 9.5.3. 367, 500π N-m 9.2.3. 20, 28 9.5.4. 49000π + 196000/3 √ 9.2.4. (3 − π)/(2π), (18 − 12 3 + π)/(4π) 9.2.5. 10/49 meters, 20/49 seconds 9.2.6. 45/98 meters, 30/49 seconds 9.2.7. 25000/49 meters, 1000/49 seconds 9.2.8. s(t) = cos t, v(t) = − sin t, maximum distance is 1, maximum speed is 1 9.2.9. s(t) = − sin(πt)/π 2 + t/π, v(t) = − cos(πt)/π + 1/π, maximum speed is 2/π 9.2.10. s(t) = t2 /2 − sin(πt)/π 2 + t/π, v(t) = t − cos(πt)/π + 1/π 2 2 9.2.11. s(t) = t /2 + sin(πt)/π − t/π, v(t) = t + cos(πt)/π − 1/π 9.5.5. 2450π N-m 9.5.6. 0.05 N-m 9.5.7. 6/5 N-m 9.5.8. 3920 N-m 9.5.9. 23520 N-m 9.6.1. 15/2 9.6.2. 5 9.6.3. 16/5 9.6.5. x̄ = 45/28, ȳ = 93/70 9.6.6. x̄ = 0, ȳ = 4/(3π) 9.6.7. x̄ = 1/2, ȳ = 2/5 9.6.8. x̄ = 0, ȳ = 8/5 9.6.9. x̄ = 4/7, ȳ = 2/5 9.6.10. x̄ = ȳ = 1/5 9.3.5. 8π/3 9.6.11. x̄ = 0, ȳ = 28/(9π) 9.3.6. π/30 9.6.12. x̄ = ȳ = 28/(9π) 9.3.7. π(π/2 − 1) 9.7.1. ∞ 9.3.8. (a) 114π/5 (b) 74π/5 (c) 20π (d) 4π 9.7.2. 1/2 9.3.9. 16π, 24π 9.7.4. diverges 2 9.7.3. diverges 9.3.11. πh (3r − h)/3 9.7.5. 1 9.3.13. 2π 9.7.6. diverges 9.4.1. 2/π; 2/π; 0 9.7.7. 2 299 300 Appendix B Selected Answers 9.7.8. diverges 10.3.4. converges 9.7.9. π/6 10.3.5. converges 9.7.10. diverges, 0 10.3.6. converges 9.7.11. diverges, 0 10.3.7. diverges 9.7.12. diverges, no CPV 10.3.8. converges 9.7.13. π 10.3.9. N = 5 9.7.14. 80 mph: 90.8 to 95.3 N 90 mph: 114.9 to 120.6 N 100.9 mph: 144.5 to 151.6 N 9.8.13. 4 through 16 √ 9.9.1. (22 22 − 8)/27 9.9.2. ln(2) + 3/8 9.9.3. a + a3 /3 √ √ 9.9.4. ln(( 2 + 1)/ 3) 10.1.1. 1 10.1.3. 0 10.1.4. 1 10.1.5. 1 10.1.6. 0 10.2.1. lim n2 /(2n2 + 1) = 1/2 n→∞ 10.3.10. N = 10 10.3.11. N = 1687 10.3.12. any integer greater than e200 10.4.1. converges 10.4.2. converges 10.4.3. diverges 10.4.4. converges 10.4.5. 0.90 10.4.6. 0.95 10.5.1. converges 10.5.2. converges 10.5.3. converges 10.5.4. diverges 10.5.5. diverges 10.2.2. lim 5/(21/n + 14) = 1/3 10.5.6. diverges ∞ X 3 10.2.3. If converges so does n n=1 10.5.7. converges n→∞ ∞ ∞ X 1X3 1 = , but the latter 3 n=1 n n n=1 in fact diverges. 10.5.8. diverges 10.5.9. converges 10.5.10. diverges 10.6.1. converges absolutely 10.2.4. −3/2 10.6.2. diverges 10.2.5. 11 10.6.3. converges conditionally 10.3.1. diverges 10.6.4. converges absolutely 10.3.2. diverges 10.6.5. converges conditionally 10.3.3. converges 10.6.6. converges absolutely Appendix B Selected Answers 10.6.7. diverges 10.10.6. 10.6.8. converges conditionally 10.7.5. converges 10.7.6. converges ∞ X (−1)n (n + 1)(x − 1)n , R = 1 n=0 ∞ X 1 · 3 · 5 · · · (2n − 1) n 10.10.7. 1 + x = n n!2 n=1 10.7.7. converges ∞ X 1+ 10.7.8. diverges n=1 10.8.2. R = ∞, I = (−∞, ∞) 10.8.3. R = e, I = (−e, e) 10.10.9. 10.8.5. R = 0, converges only when x = 2 10.10.10. 10.8.6. R = 1, I = (−6, −4) 10.9.1. the alternating harmonic series ∞ X 10.9.2. (n + 1)xn 10.10.4. (−1)n xn+1 /n! n=0 x2 x4 x6 x12 + − + ··· + 2 24 720 12! 10.11.2. 1000; 8 10.11.1. 1 − x3 2x5 + , error ±4.26. 3 15 10.12.1. diverges n=0 ∞ X 10.12.2. converges (n + 1)(n + 2)xn −1 10.9.5. C + xn+2 (n + 1)(n + 2) n=0 10.10.3. ∞ X 10.11.3. x + ∞ X 10.10.2. (−1)n x4n+1 /(2n)! n=0 ∞ X (n + 1)(n + 2) n 10.9.4. x ,R=1 2 n=0 10.10.1. ∞ X n=0 10.8.4. R = e, I = (2 − e, 2 + e) 10.9.3. ∞ X (−1) x /(2n)!, R = ∞ (−1)n n=0 ∞ X n=1 10.10.5. ln(2) + ∞ X (x − 1)n ,R=1 n (−1)n−1 n=1 10.12.5. diverges 10.12.7. converges 10.12.9. converges (x − 5)n ,R=5 5n+1 (−1)n−1 10.12.4. diverges 10.12.8. converges xn /n!, R = ∞ n=0 ∞ X 10.12.3. converges 10.12.6. diverges n 2n n=0 ∞ X (2n − 1)! xn , R = 1 − 1)! n! 22n−1 (n 10.10.8. x + x3 /3 10.8.1. R = 1, I = (−1, 1) 301 10.12.10. converges 10.12.11. converges 10.12.12. converges 10.12.13. converges 10.12.14. converges (x − 2)n ,R=2 n n2 10.12.15. converges 302 Appendix B Selected Answers 10.12.16. converges 10.12.17. diverges 10.12.18. (−∞, ∞) 10.12.19. (−3, 3) 10.12.20. (−3, 3) 10.12.21. (−1, 1) 10.12.22. radius is 0—it converges only when x=0 √ √ 10.12.23. (− 3, 3) 10.12.24. (−∞, ∞) ∞ X (ln(2))n n 10.12.25. x n! n=0 10.12.26. 10.12.27. ∞ X (−1)n n+1 x n + 1 n=0 ∞ X 2 x2n+1 2n + 1 n=0 10.12.28. 1 + x/2 + ∞ X 1 · 3 · 5 · · · (2n − 3) n (−1)n+1 x n n! 2 n=2 10.12.29. ∞ X (−1)n x2n n=0 ∞ X (−1)n 2n+1 10.12.30. x 2n + 1 n=0 10.12.31. π = ∞ X (−1)n n=0 4 2n + 1 Index A absolute extremum, 105 algebraic precedence, xii alternating harmonic series, 249 antiderivative, 145 arccosine, 90 arcsine, 89 asymptote, 10, 102 B bell curve, 214 bounded function, 40 C Cauchy Principal Value, 207 center of mass, 202 centroid, 203 chain rule, 56 chord, 20 circle area, xiii circumference, xiii equation of, xiii, 7, 8 unit, 7 completing the square, xii composition of functions, 13 concave down, 101 concave up, 100 cone lateral area, xiii surface area, xiii volume, xiii continuous, 42, 51 convergent sequence, 235 convergent series, 241 cosines law of, xiii critical value, 94 cumulative distribution function, 213 cylinder lateral area, xiii surface area, xiii volume, xiii D definite integral, 149 dependent variable, 10 derivative, 22 dot notation, 119 Leibniz notation, 36 difference quotient, 20 differentiable, 42 differential, 132 differential equation, 227 first order, 228 separable, 228 303 304 Index discrete probability, 210 divergence test, 243 divergent sequence, 235 divergent series, 241 domain, 9 dot notation, 119 E ellipse equation of, xiii ellipsoid, 227 escape velocity, 208 even function, 215 expected value, 210 exponential distribution, 214 exponential function, 72 Extreme Value Theorem, 107 F Fermat’s Theorem, 94 floor, 51 frustum, 223 function, 8 bounded, 40 differentiable, 42 even, 215 implicit, 84 linear, 8 rational, 170 unbounded, 41 function composition, 13 Fundamental Theorem of Algebra, 97 Fundamental Theorem of Calculus, 143 G Gabriel’s horn, 209, 227 geometric series, 241 global extremum, 105 greatest integer, 51, 97 H harmonic series, 244 alternating, 249 Hooke’s Law, 198 I implicit differentiation, 84 implicit function, 84 improper integral, 206 convergent, 206 diverges, 206 indefinite integral, 149 independent variable, 10 inflection point, 101 initial value problem first order, 228 integral improper, 206 of sec x, 164 of sec3 x, 165 properties of, 153 integral sign, 145 integral test, 247 integration by parts, 167 Intermediate Value Theorem, 42 interval of convergence, 260 inverse function, 72 inverse sine, 89 K kinetic energy, 208 L L’Hôpital’s Rule, 81 lateral area, 117 law of cosines, xiii law of sines, xiii Leibniz notation, 36 limit, 29 limit at infinity, 81 limit of a sequence, 235 linear approximation, 131 linearity of the derivative, 48 local extremum, 93 local maximum, 93 local minimum, 93 logarithm, 72 logarithmic function, 72 logistic equation, 231 M Maclaurin series, 264 mean, 215 Mean Value Theorem, 134 Index median, 214 mode, 215 moment, 201 quadratic formula, xii quotient rule, 53 convergent, 235 decreasing, 238 divergent, 235 increasing, 238 monotonic, 238 non-decreasing, 238 non-increasing, 238 of partial sums, 241 series, 234 p-series, 247 absolute convergence, 255 alternating harmonic, 249 conditional convergence, 255 convergent, 241 divergent, 241 geometric, 241 harmonic, 244 integral test, 247 interval of convergence, 260 Maclaurin, 264 radius of convergence, 260 Taylor, 266 sines law of, xiii sphere surface area, xiii volume, xiii squeeze theorem, 67 standard deviation, 216 standard normal distribution, 214 standard normal probability density function, 214 subtend, 64 sum rule, 49 R T radian measure, 63 radius of convergence, 260 random variable, 211 rational function, 56, 170 related rates, 118 Rolle’s Theorem, 134 tangent line, 20 Taylor series, 266 Toricelli’s trumpet, 209, 227 torque, 200 torus, 227 transcendental function, 63 triangle inequality, 31 trigonometric identities, xiii N Newton, 197 Newton’s law of cooling, 227 normal distribution, 219 O one sided limit, 34 optimization, 105 P p-series, 247 physicists, 119 point-slope formula, xiii power function, 45 power rule, 45, 79, 87 precedence of algebraic operations, xii probability density function, 211 probability distribution, 213 product rule, 50, 51 generalized, 53 properties of integrals, 153 Q S separation of variables, 229 sequence, 234 bounded, 239 bounded above, 239 bounded below, 239 305 U unbounded function, 41 uniform distribution, 213 uniform probability density function, 213 unit circle, 7 306 Index W witch of Agnesi, 56 work, 195

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