SlideShare a Scribd company logo
1 of 51
Download to read offline
Section 4.2
            The Mean Value Theorem

                   V63.0121.002.2010Su, Calculus I

                           New York University


                            June 8, 2010



Announcements
   Exams not graded yet
   Assignment 4 is on the website
   Quiz 3 on Thursday covering 3.3, 3.4, 3.5, 3.7
Announcements




           Exams not graded yet
           Assignment 4 is on the
           website
           Quiz 3 on Thursday covering
           3.3, 3.4, 3.5, 3.7




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   2 / 28
Objectives




           Understand and be able to
           explain the statement of
           Rolle’s Theorem.
           Understand and be able to
           explain the statement of the
           Mean Value Theorem.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   3 / 28
Outline



 Rolle’s Theorem


 The Mean Value Theorem
   Applications


 Why the MVT is the MITC
   Functions with derivatives that are zero
   MVT and differentiability




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   4 / 28
Heuristic Motivation for Rolle’s Theorem

 If you bike up a hill, then back down, at some point your elevation was
 stationary.




Image credit: SpringSun
V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   5 / 28
Mathematical Statement of Rolle’s Theorem


Theorem (Rolle’s Theorem)



   Let f be continuous on [a, b]
   and differentiable on (a, b).
   Suppose f (a) = f (b). Then
   there exists a point c in (a, b)
   such that f (c) = 0.
                                      a     b
Mathematical Statement of Rolle’s Theorem


 Theorem (Rolle’s Theorem)

                                                                                 c

        Let f be continuous on [a, b]
        and differentiable on (a, b).
        Suppose f (a) = f (b). Then
        there exists a point c in (a, b)
        such that f (c) = 0.
                                                                             a            b




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   6 / 28
Flowchart proof of Rolle’s Theorem


                                                                             endpoints
       Let c be                             Let d be
                                                                              are max
      the max pt                           the min pt
                                                                              and min



                                                                               f is
        is c an                              is d an
                            yes                                yes           constant
       endpoint?                            endpoint?
                                                                             on [a, b]

             no                                   no

                                                                             f (x) ≡ 0
       f (c) = 0                            f (d) = 0
                                                                              on (a, b)

V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   8 / 28
Outline



 Rolle’s Theorem


 The Mean Value Theorem
   Applications


 Why the MVT is the MITC
   Functions with derivatives that are zero
   MVT and differentiability




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   9 / 28
Heuristic Motivation for The Mean Value Theorem

 If you drive between points A and B, at some time your speedometer
 reading was the same as your average speed over the drive.




Image credit: ClintJCL
V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   10 / 28
The Mean Value Theorem


Theorem (The Mean Value Theorem)


   Let f be continuous on [a, b]
   and differentiable on (a, b).
   Then there exists a point c in
   (a, b) such that

       f (b) − f (a)                    b
                     = f (c).
           b−a
                                    a
The Mean Value Theorem


Theorem (The Mean Value Theorem)


   Let f be continuous on [a, b]
   and differentiable on (a, b).
   Then there exists a point c in
   (a, b) such that

       f (b) − f (a)                    b
                     = f (c).
           b−a
                                    a
The Mean Value Theorem


 Theorem (The Mean Value Theorem)


                                                                                 c
        Let f be continuous on [a, b]
        and differentiable on (a, b).
        Then there exists a point c in
        (a, b) such that

               f (b) − f (a)                                                               b
                             = f (c).
                   b−a
                                                                             a




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   11 / 28
Rolle vs. MVT

                                                                       f (b) − f (a)
                     f (c) = 0                                                       = f (c)
                                                                           b−a

                              c                                                  c




                                                                                           b

                     a                  b                                    a




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   12 / 28
Rolle vs. MVT

                                                                       f (b) − f (a)
                     f (c) = 0                                                       = f (c)
                                                                           b−a

                              c                                                  c




                                                                                           b

                     a                  b                                    a

 If the x-axis is skewed the pictures look the same.


V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   12 / 28
Proof of the Mean Value Theorem
 Proof.
 The line connecting (a, f (a)) and (b, f (b)) has equation

                                                   f (b) − f (a)
                                   y − f (a) =                   (x − a)
                                                       b−a




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   13 / 28
Proof of the Mean Value Theorem
 Proof.
 The line connecting (a, f (a)) and (b, f (b)) has equation

                                                   f (b) − f (a)
                                   y − f (a) =                   (x − a)
                                                       b−a
 Apply Rolle’s Theorem to the function

                                                           f (b) − f (a)
                          g (x) = f (x) − f (a) −                        (x − a).
                                                               b−a




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem          June 8, 2010   13 / 28
Proof of the Mean Value Theorem
 Proof.
 The line connecting (a, f (a)) and (b, f (b)) has equation

                                                   f (b) − f (a)
                                   y − f (a) =                   (x − a)
                                                       b−a
 Apply Rolle’s Theorem to the function

                                                           f (b) − f (a)
                          g (x) = f (x) − f (a) −                        (x − a).
                                                               b−a
 Then g is continuous on [a, b] and differentiable on (a, b) since f is.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem          June 8, 2010   13 / 28
Proof of the Mean Value Theorem
 Proof.
 The line connecting (a, f (a)) and (b, f (b)) has equation

                                                   f (b) − f (a)
                                   y − f (a) =                   (x − a)
                                                       b−a
 Apply Rolle’s Theorem to the function

                                                           f (b) − f (a)
                          g (x) = f (x) − f (a) −                        (x − a).
                                                               b−a
 Then g is continuous on [a, b] and differentiable on (a, b) since f is. Also
 g (a) = 0 and g (b) = 0 (check both)




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem          June 8, 2010   13 / 28
Proof of the Mean Value Theorem
 Proof.
 The line connecting (a, f (a)) and (b, f (b)) has equation

                                                   f (b) − f (a)
                                   y − f (a) =                   (x − a)
                                                       b−a
 Apply Rolle’s Theorem to the function

                                                           f (b) − f (a)
                          g (x) = f (x) − f (a) −                        (x − a).
                                                               b−a
 Then g is continuous on [a, b] and differentiable on (a, b) since f is. Also
 g (a) = 0 and g (b) = 0 (check both) So by Rolle’s Theorem there exists a
 point c in (a, b) such that

                                                               f (b) − f (a)
                                 0 = g (c) = f (c) −                         .
                                                                   b−a

V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem          June 8, 2010   13 / 28
Using the MVT to count solutions

 Example
 Show that there is a unique solution to the equation x 3 − x = 100 in the
 interval [4, 5].




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   14 / 28
Using the MVT to count solutions

 Example
 Show that there is a unique solution to the equation x 3 − x = 100 in the
 interval [4, 5].

 Solution

         By the Intermediate Value Theorem, the function f (x) = x 3 − x must
         take the value 100 at some point on c in (4, 5).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   14 / 28
Using the MVT to count solutions

 Example
 Show that there is a unique solution to the equation x 3 − x = 100 in the
 interval [4, 5].

 Solution

         By the Intermediate Value Theorem, the function f (x) = x 3 − x must
         take the value 100 at some point on c in (4, 5).
         If there were two points c1 and c2 with f (c1 ) = f (c2 ) = 100, then
         somewhere between them would be a point c3 between them with
         f (c3 ) = 0.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   14 / 28
Using the MVT to count solutions

 Example
 Show that there is a unique solution to the equation x 3 − x = 100 in the
 interval [4, 5].

 Solution

         By the Intermediate Value Theorem, the function f (x) = x 3 − x must
         take the value 100 at some point on c in (4, 5).
         If there were two points c1 and c2 with f (c1 ) = f (c2 ) = 100, then
         somewhere between them would be a point c3 between them with
         f (c3 ) = 0.
         However, f (x) = 3x 2 − 1, which is positive all along (4, 5). So this is
         impossible.


V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   14 / 28
Using the MVT to estimate

 Example
 We know that |sin x| ≤ 1 for all x. Show that |sin x| ≤ |x|.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   15 / 28
Using the MVT to estimate

 Example
 We know that |sin x| ≤ 1 for all x. Show that |sin x| ≤ |x|.

 Solution
 Apply the MVT to the function f (t) = sin t on [0, x]. We get

                                          sin x − sin 0
                                                        = cos(c)
                                              x −0
 for some c in (0, x). Since |cos(c)| ≤ 1, we get

                                        sin x
                                              ≤ 1 =⇒ |sin x| ≤ |x|
                                          x



V63.0121.002.2010Su, Calculus I (NYU)    Section 4.2 The Mean Value Theorem   June 8, 2010   15 / 28
Using the MVT to estimate II
 Example
 Let f be a differentiable function with f (1) = 3 and f (x) < 2 for all x in
 [0, 5]. Could f (4) ≥ 9?




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   16 / 28
Using the MVT to estimate II
 Example
 Let f be a differentiable function with f (1) = 3 and f (x) < 2 for all x in
 [0, 5]. Could f (4) ≥ 9?

 Solution
                                                                             y    (4, 9)
        By MVT

                     f (4) − f (1)                                                   (4, f (4))
                                   = f (c) < 2
                         4−1
        for some c in (1, 4). Therefore

         f (4) = f (1) + f (c)(3) < 3 + 2 · 3 = 9.
                                                                                 (1, 3)
        So no, it is impossible that f (4) ≥ 9.
                                                                                                 x
V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem        June 8, 2010       16 / 28
Food for Thought



 Question
 A driver travels along the New Jersey Turnpike using E-ZPass. The system
 takes note of the time and place the driver enters and exits the Turnpike.
 A week after his trip, the driver gets a speeding ticket in the mail. Which
 of the following best describes the situation?
 (a) E-ZPass cannot prove that the driver was speeding
 (b) E-ZPass can prove that the driver was speeding
 (c) The driver’s actual maximum speed exceeds his ticketed speed
 (d) Both (b) and (c).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   17 / 28
Food for Thought



 Question
 A driver travels along the New Jersey Turnpike using E-ZPass. The system
 takes note of the time and place the driver enters and exits the Turnpike.
 A week after his trip, the driver gets a speeding ticket in the mail. Which
 of the following best describes the situation?
 (a) E-ZPass cannot prove that the driver was speeding
 (b) E-ZPass can prove that the driver was speeding
 (c) The driver’s actual maximum speed exceeds his ticketed speed
 (d) Both (b) and (c).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   17 / 28
Outline



 Rolle’s Theorem


 The Mean Value Theorem
   Applications


 Why the MVT is the MITC
   Functions with derivatives that are zero
   MVT and differentiability




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   18 / 28
Functions with derivatives that are zero
 Fact
 If f is constant on (a, b), then f (x) = 0 on (a, b).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   19 / 28
Functions with derivatives that are zero
 Fact
 If f is constant on (a, b), then f (x) = 0 on (a, b).

         The limit of difference quotients must be 0
         The tangent line to a line is that line, and a constant function’s graph
         is a horizontal line, which has slope 0.
         Implied by the power rule since c = cx 0




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   19 / 28
Functions with derivatives that are zero
 Fact
 If f is constant on (a, b), then f (x) = 0 on (a, b).

         The limit of difference quotients must be 0
         The tangent line to a line is that line, and a constant function’s graph
         is a horizontal line, which has slope 0.
         Implied by the power rule since c = cx 0

 Question
 If f (x) = 0 is f necessarily a constant function?




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   19 / 28
Functions with derivatives that are zero
 Fact
 If f is constant on (a, b), then f (x) = 0 on (a, b).

         The limit of difference quotients must be 0
         The tangent line to a line is that line, and a constant function’s graph
         is a horizontal line, which has slope 0.
         Implied by the power rule since c = cx 0

 Question
 If f (x) = 0 is f necessarily a constant function?

         It seems true
         But so far no theorem (that we have proven) uses information about
         the derivative of a function to determine information about the
         function itself
V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   19 / 28
Why the MVT is the MITC
Most Important Theorem In Calculus!




 Theorem
 Let f = 0 on an interval (a, b).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   20 / 28
Why the MVT is the MITC
Most Important Theorem In Calculus!




 Theorem
 Let f = 0 on an interval (a, b). Then f is constant on (a, b).




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   20 / 28
Why the MVT is the MITC
Most Important Theorem In Calculus!




 Theorem
 Let f = 0 on an interval (a, b). Then f is constant on (a, b).

 Proof.
 Pick any points x and y in (a, b) with x < y . Then f is continuous on
 [x, y ] and differentiable on (x, y ). By MVT there exists a point z in (x, y )
 such that
                            f (y ) − f (x)
                                           = f (z) = 0.
                                y −x
 So f (y ) = f (x). Since this is true for all x and y in (a, b), then f is
 constant.



V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   20 / 28
Functions with the same derivative


 Theorem
 Suppose f and g are two differentiable functions on (a, b) with f = g .
 Then f and g differ by a constant. That is, there exists a constant C such
 that f (x) = g (x) + C .




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   21 / 28
Functions with the same derivative


 Theorem
 Suppose f and g are two differentiable functions on (a, b) with f = g .
 Then f and g differ by a constant. That is, there exists a constant C such
 that f (x) = g (x) + C .

 Proof.

         Let h(x) = f (x) − g (x)
         Then h (x) = f (x) − g (x) = 0 on (a, b)
         So h(x) = C , a constant
         This means f (x) − g (x) = C on (a, b)




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   21 / 28
MVT and differentiability
 Example
 Let
                                                     −x       if x ≤ 0
                                        f (x) =
                                                     x2       if x ≥ 0
 Is f differentiable at 0?




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   22 / 28
MVT and differentiability
 Example
 Let
                                                     −x       if x ≤ 0
                                        f (x) =
                                                     x2       if x ≥ 0
 Is f differentiable at 0?


 Solution (from the definition)
 We have
                                f (x) − f (0)        −x
                           lim                = lim      = −1
                         x→0−       x −0       x→0 − x

                                f (x) − f (0)        x2
                           lim+               = lim+    = lim+ x = 0
                          x→0       x −0       x→0 x      x→0

 Since these limits disagree, f is not differentiable at 0.
V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   22 / 28
MVT and differentiability
 Example
 Let
                                                     −x       if x ≤ 0
                                        f (x) =
                                                     x2       if x ≥ 0
 Is f differentiable at 0?


 Solution (Sort of)
 If x < 0, then f (x) = −1. If x > 0, then f (x) = 2x. Since

                               lim f (x) = 0 and lim f (x) = −1,
                              x→0+                         x→0−

 the limit lim f (x) does not exist and so f is not differentiable at 0.
               x→0



V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   22 / 28
Why only “sort of”?

                                                                                           f (x)
          This solution is valid but less                                    y             f (x)
          direct.
          We seem to be using the
          following fact: If lim f (x)
                             x→a
          does not exist, then f is not
                                                                                            x
          differentiable at a.
          equivalently: If f is
          differentiable at a, then
          lim f (x) exists.
          x→a
          But this “fact” is not true!




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem       June 8, 2010   23 / 28
Differentiable with discontinuous derivative

 It is possible for a function f to be differentiable at a even if lim f (x)
                                                                  x→a
 does not exist.
 Example
                       x 2 sin(1/x)       if x = 0
 Let f (x) =                                       . Then when x = 0,
                       0                  if x = 0

      f (x) = 2x sin(1/x) + x 2 cos(1/x)(−1/x 2 ) = 2x sin(1/x) − cos(1/x),

 which has no limit at 0. However,

                         f (x) − f (0)       x 2 sin(1/x)
         f (0) = lim                   = lim              = lim x sin(1/x) = 0
                     x→0     x −0        x→0        x       x→0

 So f (0) = 0. Hence f is differentiable for all x, but f is not continuous at
 0!

V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   24 / 28
Differentiability FAIL

                         f (x)                                               f (x)




                                           x                                                    x




  This function is differentiable at                          But the derivative is not
  0.                                                         continuous at 0!



V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem           June 8, 2010   25 / 28
MVT to the rescue
 Lemma
 Suppose f is continuous on [a, b] and lim+ f (x) = m. Then
                                                          x→a

                                               f (x) − f (a)
                                        lim+                 = m.
                                        x→a        x −a




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   26 / 28
MVT to the rescue
 Lemma
 Suppose f is continuous on [a, b] and lim+ f (x) = m. Then
                                                          x→a

                                               f (x) − f (a)
                                        lim+                 = m.
                                        x→a        x −a


 Proof.
 Choose x near a and greater than a. Then

                                         f (x) − f (a)
                                                       = f (cx )
                                             x −a
 for some cx where a < cx < x. As x → a, cx → a as well, so:
                            f (x) − f (a)
                     lim                  = lim+ f (cx ) = lim+ f (x) = m.
                   x→a+         x −a       x→a            x→a

V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   26 / 28
Theorem
 Suppose
                               lim f (x) = m1 and lim+ f (x) = m2
                             x→a−                            x→a

 If m1 = m2 , then f is differentiable at a. If m1 = m2 , then f is not
 differentiable at a.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   27 / 28
Theorem
 Suppose
                               lim f (x) = m1 and lim+ f (x) = m2
                             x→a−                              x→a

 If m1 = m2 , then f is differentiable at a. If m1 = m2 , then f is not
 differentiable at a.

 Proof.
 We know by the lemma that

                                         f (x) − f (a)
                                    lim                = lim f (x)
                                   x→a−      x −a       x→a−
                                         f (x) − f (a)
                                    lim+               = lim+ f (x)
                                   x→a       x −a       x→a

 The two-sided limit exists if (and only if) the two right-hand sides
 agree.

V63.0121.002.2010Su, Calculus I (NYU)     Section 4.2 The Mean Value Theorem   June 8, 2010   27 / 28
Summary




         Rolle’s Theorem: under suitable conditions, functions must have
         critical points.
         Mean Value Theorem: under suitable conditions, functions must have
         an instantaneous rate of change equal to the average rate of change.
         A function whose derivative is identically zero on an interval must be
         constant on that interval.
         E-ZPass is kinder than we realized.




V63.0121.002.2010Su, Calculus I (NYU)   Section 4.2 The Mean Value Theorem   June 8, 2010   28 / 28

More Related Content

What's hot

Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)
Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)
Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)tanafuyu
 
Discussion of Faming Liang's talk
Discussion of Faming Liang's talkDiscussion of Faming Liang's talk
Discussion of Faming Liang's talkChristian Robert
 
IGARSS_AMASM_woo_20110727.pdf
IGARSS_AMASM_woo_20110727.pdfIGARSS_AMASM_woo_20110727.pdf
IGARSS_AMASM_woo_20110727.pdfgrssieee
 
Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Matthew Leingang
 
Approximate Bayesian Computation on GPUs
Approximate Bayesian Computation on GPUsApproximate Bayesian Computation on GPUs
Approximate Bayesian Computation on GPUsMichael Stumpf
 
改进的固定点图像复原算法_英文_阎雪飞
改进的固定点图像复原算法_英文_阎雪飞改进的固定点图像复原算法_英文_阎雪飞
改进的固定点图像复原算法_英文_阎雪飞alen yan
 
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)Joo-Haeng Lee
 
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Wavelet-based Reflection Symmetry Detection via Textural and Color HistogramsWavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Wavelet-based Reflection Symmetry Detection via Textural and Color HistogramsMohamed Elawady
 
Fission rate and_time_of_higly_excited_nuclei
Fission rate and_time_of_higly_excited_nucleiFission rate and_time_of_higly_excited_nuclei
Fission rate and_time_of_higly_excited_nucleiYuri Anischenko
 
Module 13 Gradient And Area Under A Graph
Module 13  Gradient And Area Under A GraphModule 13  Gradient And Area Under A Graph
Module 13 Gradient And Area Under A Graphguestcc333c
 
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES Toru Tamaki
 
EM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysisEM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysiszukun
 
Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms
Global Bilateral Symmetry Detection Using Multiscale Mirror HistogramsGlobal Bilateral Symmetry Detection Using Multiscale Mirror Histograms
Global Bilateral Symmetry Detection Using Multiscale Mirror HistogramsMohamed Elawady
 
ABC in London, May 5, 2011
ABC in London, May 5, 2011ABC in London, May 5, 2011
ABC in London, May 5, 2011Christian Robert
 
A brief introduction to Hartree-Fock and TDDFT
A brief introduction to Hartree-Fock and TDDFTA brief introduction to Hartree-Fock and TDDFT
A brief introduction to Hartree-Fock and TDDFTJiahao Chen
 
Mesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsMesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsGabriel Peyré
 

What's hot (18)

確率伝播その2
確率伝播その2確率伝播その2
確率伝播その2
 
Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)
Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)
Quantum Minimax Theorem in Statistical Decision Theory (RIMS2014)
 
Discussion of Faming Liang's talk
Discussion of Faming Liang's talkDiscussion of Faming Liang's talk
Discussion of Faming Liang's talk
 
IGARSS_AMASM_woo_20110727.pdf
IGARSS_AMASM_woo_20110727.pdfIGARSS_AMASM_woo_20110727.pdf
IGARSS_AMASM_woo_20110727.pdf
 
Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)
 
Approximate Bayesian Computation on GPUs
Approximate Bayesian Computation on GPUsApproximate Bayesian Computation on GPUs
Approximate Bayesian Computation on GPUs
 
改进的固定点图像复原算法_英文_阎雪飞
改进的固定点图像复原算法_英文_阎雪飞改进的固定点图像复原算法_英文_阎雪飞
改进的固定点图像复原算法_英文_阎雪飞
 
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)
 
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Wavelet-based Reflection Symmetry Detection via Textural and Color HistogramsWavelet-based Reflection Symmetry Detection via Textural and Color Histograms
Wavelet-based Reflection Symmetry Detection via Textural and Color Histograms
 
Fission rate and_time_of_higly_excited_nuclei
Fission rate and_time_of_higly_excited_nucleiFission rate and_time_of_higly_excited_nuclei
Fission rate and_time_of_higly_excited_nuclei
 
Module 13 Gradient And Area Under A Graph
Module 13  Gradient And Area Under A GraphModule 13  Gradient And Area Under A Graph
Module 13 Gradient And Area Under A Graph
 
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES
SCALE RATIO ICP FOR 3D POINT CLOUDS WITH DIFFERENT SCALES
 
EM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysisEM algorithm and its application in probabilistic latent semantic analysis
EM algorithm and its application in probabilistic latent semantic analysis
 
Future CMB Experiments
Future CMB ExperimentsFuture CMB Experiments
Future CMB Experiments
 
Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms
Global Bilateral Symmetry Detection Using Multiscale Mirror HistogramsGlobal Bilateral Symmetry Detection Using Multiscale Mirror Histograms
Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms
 
ABC in London, May 5, 2011
ABC in London, May 5, 2011ABC in London, May 5, 2011
ABC in London, May 5, 2011
 
A brief introduction to Hartree-Fock and TDDFT
A brief introduction to Hartree-Fock and TDDFTA brief introduction to Hartree-Fock and TDDFT
A brief introduction to Hartree-Fock and TDDFT
 
Mesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsMesh Processing Course : Geodesics
Mesh Processing Course : Geodesics
 

Viewers also liked

Lesson22 -optimization_problems_slides
Lesson22 -optimization_problems_slidesLesson22 -optimization_problems_slides
Lesson22 -optimization_problems_slidesMel Anthony Pepito
 
Lesson 3: Limits (Section 21 slides)
Lesson 3: Limits (Section 21 slides)Lesson 3: Limits (Section 21 slides)
Lesson 3: Limits (Section 21 slides)Mel Anthony Pepito
 
Lesson 25: The Definite Integral
Lesson 25: The Definite IntegralLesson 25: The Definite Integral
Lesson 25: The Definite IntegralMel Anthony Pepito
 
Lesson 14: Derivatives of Logarithmic and Exponential Functions
Lesson 14: Derivatives of Logarithmic and Exponential FunctionsLesson 14: Derivatives of Logarithmic and Exponential Functions
Lesson 14: Derivatives of Logarithmic and Exponential FunctionsMel Anthony Pepito
 
Lesson 11: Implicit Differentiation
Lesson 11: Implicit DifferentiationLesson 11: Implicit Differentiation
Lesson 11: Implicit DifferentiationMel Anthony Pepito
 
2-10 Adding and Subtracting Decimals
2-10 Adding and Subtracting Decimals2-10 Adding and Subtracting Decimals
2-10 Adding and Subtracting DecimalsRudy Alfonso
 
6-10 Dividing Decimals by Decimals
6-10 Dividing Decimals by Decimals6-10 Dividing Decimals by Decimals
6-10 Dividing Decimals by DecimalsMel Anthony Pepito
 
Lesson 16: Inverse Trigonometric Functions
Lesson 16: Inverse Trigonometric FunctionsLesson 16: Inverse Trigonometric Functions
Lesson 16: Inverse Trigonometric FunctionsMel Anthony Pepito
 

Viewers also liked (11)

Lesson22 -optimization_problems_slides
Lesson22 -optimization_problems_slidesLesson22 -optimization_problems_slides
Lesson22 -optimization_problems_slides
 
Lesson 3: Limits (Section 21 slides)
Lesson 3: Limits (Section 21 slides)Lesson 3: Limits (Section 21 slides)
Lesson 3: Limits (Section 21 slides)
 
Lesson 25: The Definite Integral
Lesson 25: The Definite IntegralLesson 25: The Definite Integral
Lesson 25: The Definite Integral
 
Introduction
IntroductionIntroduction
Introduction
 
Lesson 14: Derivatives of Logarithmic and Exponential Functions
Lesson 14: Derivatives of Logarithmic and Exponential FunctionsLesson 14: Derivatives of Logarithmic and Exponential Functions
Lesson 14: Derivatives of Logarithmic and Exponential Functions
 
6-5 Multiplying Decimals
6-5 Multiplying Decimals6-5 Multiplying Decimals
6-5 Multiplying Decimals
 
Decimals Add and Subtract
Decimals Add and SubtractDecimals Add and Subtract
Decimals Add and Subtract
 
Lesson 11: Implicit Differentiation
Lesson 11: Implicit DifferentiationLesson 11: Implicit Differentiation
Lesson 11: Implicit Differentiation
 
2-10 Adding and Subtracting Decimals
2-10 Adding and Subtracting Decimals2-10 Adding and Subtracting Decimals
2-10 Adding and Subtracting Decimals
 
6-10 Dividing Decimals by Decimals
6-10 Dividing Decimals by Decimals6-10 Dividing Decimals by Decimals
6-10 Dividing Decimals by Decimals
 
Lesson 16: Inverse Trigonometric Functions
Lesson 16: Inverse Trigonometric FunctionsLesson 16: Inverse Trigonometric Functions
Lesson 16: Inverse Trigonometric Functions
 

Similar to Lesson 19: The Mean Value Theorem

Lesson 20: Derivatives and the shapes of curves
Lesson 20: Derivatives and the shapes of curvesLesson 20: Derivatives and the shapes of curves
Lesson 20: Derivatives and the shapes of curvesMatthew Leingang
 
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slidesLesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slidesMel Anthony Pepito
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)Mel Anthony Pepito
 
Lesson 18: Derivatives and the Shapes of Curves
Lesson 18: Derivatives and the Shapes of CurvesLesson 18: Derivatives and the Shapes of Curves
Lesson 18: Derivatives and the Shapes of CurvesMatthew Leingang
 
Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Mel Anthony Pepito
 
Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Matthew Leingang
 
Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)Mel Anthony Pepito
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)Matthew Leingang
 
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)Matthew Leingang
 
Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)Matthew Leingang
 
Lesson 27: The Fundamental Theorem of Calculus
Lesson 27: The Fundamental Theorem of Calculus Lesson 27: The Fundamental Theorem of Calculus
Lesson 27: The Fundamental Theorem of Calculus Mel Anthony Pepito
 
Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)Matthew Leingang
 
Lesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of CalculusLesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of CalculusMatthew Leingang
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsMatthew Leingang
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsMel Anthony Pepito
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsMatthew Leingang
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsMel Anthony Pepito
 
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)Matthew Leingang
 
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)Matthew Leingang
 

Similar to Lesson 19: The Mean Value Theorem (20)

Lesson 20: Derivatives and the shapes of curves
Lesson 20: Derivatives and the shapes of curvesLesson 20: Derivatives and the shapes of curves
Lesson 20: Derivatives and the shapes of curves
 
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slidesLesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
 
Lesson 18: Derivatives and the Shapes of Curves
Lesson 18: Derivatives and the Shapes of CurvesLesson 18: Derivatives and the Shapes of Curves
Lesson 18: Derivatives and the Shapes of Curves
 
Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)
 
Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)Lesson 19: The Mean Value Theorem (slides)
Lesson 19: The Mean Value Theorem (slides)
 
Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
 
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
 
Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)
 
Lesson 27: The Fundamental Theorem of Calculus
Lesson 27: The Fundamental Theorem of Calculus Lesson 27: The Fundamental Theorem of Calculus
Lesson 27: The Fundamental Theorem of Calculus
 
Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)
 
Lesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of CalculusLesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of Calculus
 
Lesson 4: Continuity
Lesson 4: ContinuityLesson 4: Continuity
Lesson 4: Continuity
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential Functions
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential Functions
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential Functions
 
Lesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential FunctionsLesson 2: A Catalog of Essential Functions
Lesson 2: A Catalog of Essential Functions
 
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 021 handout)
 
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)
Lesson 26: The Fundamental Theorem of Calculus (Section 041 handout)
 

More from Mel Anthony Pepito

Lesson 12: Linear Approximation
Lesson 12: Linear ApproximationLesson 12: Linear Approximation
Lesson 12: Linear ApproximationMel Anthony Pepito
 
Lesson 13: Related Rates Problems
Lesson 13: Related Rates ProblemsLesson 13: Related Rates Problems
Lesson 13: Related Rates ProblemsMel Anthony Pepito
 
Lesson 15: Exponential Growth and Decay
Lesson 15: Exponential Growth and DecayLesson 15: Exponential Growth and Decay
Lesson 15: Exponential Growth and DecayMel Anthony Pepito
 
Lesson 17: Indeterminate Forms and L'Hôpital's Rule
Lesson 17: Indeterminate Forms and L'Hôpital's RuleLesson 17: Indeterminate Forms and L'Hôpital's Rule
Lesson 17: Indeterminate Forms and L'Hôpital's RuleMel Anthony Pepito
 
Lesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slidesLesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slidesMel Anthony Pepito
 
Lesson 26: Evaluating Definite Integrals
Lesson 26: Evaluating Definite IntegralsLesson 26: Evaluating Definite Integrals
Lesson 26: Evaluating Definite IntegralsMel Anthony Pepito
 
Lesson 28: Integration by Subsitution
Lesson 28: Integration by SubsitutionLesson 28: Integration by Subsitution
Lesson 28: Integration by SubsitutionMel Anthony Pepito
 
Lesson 5: Continuity (Section 41 slides)
Lesson 5: Continuity (Section 41 slides)Lesson 5: Continuity (Section 41 slides)
Lesson 5: Continuity (Section 41 slides)Mel Anthony Pepito
 
Lesson 5: Continuity (Section 21 slides)
Lesson 5: Continuity (Section 21 slides)Lesson 5: Continuity (Section 21 slides)
Lesson 5: Continuity (Section 21 slides)Mel Anthony Pepito
 
Lesson 4: Calculating Limits (Section 41 slides)
Lesson 4: Calculating Limits (Section 41 slides)Lesson 4: Calculating Limits (Section 41 slides)
Lesson 4: Calculating Limits (Section 41 slides)Mel Anthony Pepito
 
Lesson 4: Calculating Limits (Section 21 slides)
Lesson 4: Calculating Limits (Section 21 slides)Lesson 4: Calculating Limits (Section 21 slides)
Lesson 4: Calculating Limits (Section 21 slides)Mel Anthony Pepito
 
Lesson 6: Limits Involving ∞ (Section 41 slides)
Lesson 6: Limits Involving ∞ (Section 41 slides)Lesson 6: Limits Involving ∞ (Section 41 slides)
Lesson 6: Limits Involving ∞ (Section 41 slides)Mel Anthony Pepito
 
Lesson 7: The Derivative (Section 41 slides)
Lesson 7: The Derivative (Section 41 slides)Lesson 7: The Derivative (Section 41 slides)
Lesson 7: The Derivative (Section 41 slides)Mel Anthony Pepito
 
Lesson 6: Limits Involving ∞ (Section 21 slides)
Lesson 6: Limits Involving ∞ (Section 21 slides)Lesson 6: Limits Involving ∞ (Section 21 slides)
Lesson 6: Limits Involving ∞ (Section 21 slides)Mel Anthony Pepito
 

More from Mel Anthony Pepito (20)

Lesson 12: Linear Approximation
Lesson 12: Linear ApproximationLesson 12: Linear Approximation
Lesson 12: Linear Approximation
 
Lesson 13: Related Rates Problems
Lesson 13: Related Rates ProblemsLesson 13: Related Rates Problems
Lesson 13: Related Rates Problems
 
Lesson 15: Exponential Growth and Decay
Lesson 15: Exponential Growth and DecayLesson 15: Exponential Growth and Decay
Lesson 15: Exponential Growth and Decay
 
Lesson 17: Indeterminate Forms and L'Hôpital's Rule
Lesson 17: Indeterminate Forms and L'Hôpital's RuleLesson 17: Indeterminate Forms and L'Hôpital's Rule
Lesson 17: Indeterminate Forms and L'Hôpital's Rule
 
Lesson 21: Curve Sketching
Lesson 21: Curve SketchingLesson 21: Curve Sketching
Lesson 21: Curve Sketching
 
Lesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slidesLesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slides
 
Lesson 24: Area and Distances
Lesson 24: Area and DistancesLesson 24: Area and Distances
Lesson 24: Area and Distances
 
Lesson 23: Antiderivatives
Lesson 23: AntiderivativesLesson 23: Antiderivatives
Lesson 23: Antiderivatives
 
Lesson 26: Evaluating Definite Integrals
Lesson 26: Evaluating Definite IntegralsLesson 26: Evaluating Definite Integrals
Lesson 26: Evaluating Definite Integrals
 
Introduction
IntroductionIntroduction
Introduction
 
Lesson 28: Integration by Subsitution
Lesson 28: Integration by SubsitutionLesson 28: Integration by Subsitution
Lesson 28: Integration by Subsitution
 
Lesson 3: Limits
Lesson 3: LimitsLesson 3: Limits
Lesson 3: Limits
 
Lesson 1: Functions
Lesson 1: FunctionsLesson 1: Functions
Lesson 1: Functions
 
Lesson 5: Continuity (Section 41 slides)
Lesson 5: Continuity (Section 41 slides)Lesson 5: Continuity (Section 41 slides)
Lesson 5: Continuity (Section 41 slides)
 
Lesson 5: Continuity (Section 21 slides)
Lesson 5: Continuity (Section 21 slides)Lesson 5: Continuity (Section 21 slides)
Lesson 5: Continuity (Section 21 slides)
 
Lesson 4: Calculating Limits (Section 41 slides)
Lesson 4: Calculating Limits (Section 41 slides)Lesson 4: Calculating Limits (Section 41 slides)
Lesson 4: Calculating Limits (Section 41 slides)
 
Lesson 4: Calculating Limits (Section 21 slides)
Lesson 4: Calculating Limits (Section 21 slides)Lesson 4: Calculating Limits (Section 21 slides)
Lesson 4: Calculating Limits (Section 21 slides)
 
Lesson 6: Limits Involving ∞ (Section 41 slides)
Lesson 6: Limits Involving ∞ (Section 41 slides)Lesson 6: Limits Involving ∞ (Section 41 slides)
Lesson 6: Limits Involving ∞ (Section 41 slides)
 
Lesson 7: The Derivative (Section 41 slides)
Lesson 7: The Derivative (Section 41 slides)Lesson 7: The Derivative (Section 41 slides)
Lesson 7: The Derivative (Section 41 slides)
 
Lesson 6: Limits Involving ∞ (Section 21 slides)
Lesson 6: Limits Involving ∞ (Section 21 slides)Lesson 6: Limits Involving ∞ (Section 21 slides)
Lesson 6: Limits Involving ∞ (Section 21 slides)
 

Recently uploaded

Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxBkGupta21
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rick Flair
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 

Recently uploaded (20)

Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
unit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptxunit 4 immunoblotting technique complete.pptx
unit 4 immunoblotting technique complete.pptx
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...Rise of the Machines: Known As Drones...
Rise of the Machines: Known As Drones...
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 

Lesson 19: The Mean Value Theorem

  • 1. Section 4.2 The Mean Value Theorem V63.0121.002.2010Su, Calculus I New York University June 8, 2010 Announcements Exams not graded yet Assignment 4 is on the website Quiz 3 on Thursday covering 3.3, 3.4, 3.5, 3.7
  • 2. Announcements Exams not graded yet Assignment 4 is on the website Quiz 3 on Thursday covering 3.3, 3.4, 3.5, 3.7 V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 2 / 28
  • 3. Objectives Understand and be able to explain the statement of Rolle’s Theorem. Understand and be able to explain the statement of the Mean Value Theorem. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 3 / 28
  • 4. Outline Rolle’s Theorem The Mean Value Theorem Applications Why the MVT is the MITC Functions with derivatives that are zero MVT and differentiability V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 4 / 28
  • 5. Heuristic Motivation for Rolle’s Theorem If you bike up a hill, then back down, at some point your elevation was stationary. Image credit: SpringSun V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 5 / 28
  • 6. Mathematical Statement of Rolle’s Theorem Theorem (Rolle’s Theorem) Let f be continuous on [a, b] and differentiable on (a, b). Suppose f (a) = f (b). Then there exists a point c in (a, b) such that f (c) = 0. a b
  • 7. Mathematical Statement of Rolle’s Theorem Theorem (Rolle’s Theorem) c Let f be continuous on [a, b] and differentiable on (a, b). Suppose f (a) = f (b). Then there exists a point c in (a, b) such that f (c) = 0. a b V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 6 / 28
  • 8. Flowchart proof of Rolle’s Theorem endpoints Let c be Let d be are max the max pt the min pt and min f is is c an is d an yes yes constant endpoint? endpoint? on [a, b] no no f (x) ≡ 0 f (c) = 0 f (d) = 0 on (a, b) V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 8 / 28
  • 9. Outline Rolle’s Theorem The Mean Value Theorem Applications Why the MVT is the MITC Functions with derivatives that are zero MVT and differentiability V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 9 / 28
  • 10. Heuristic Motivation for The Mean Value Theorem If you drive between points A and B, at some time your speedometer reading was the same as your average speed over the drive. Image credit: ClintJCL V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 10 / 28
  • 11. The Mean Value Theorem Theorem (The Mean Value Theorem) Let f be continuous on [a, b] and differentiable on (a, b). Then there exists a point c in (a, b) such that f (b) − f (a) b = f (c). b−a a
  • 12. The Mean Value Theorem Theorem (The Mean Value Theorem) Let f be continuous on [a, b] and differentiable on (a, b). Then there exists a point c in (a, b) such that f (b) − f (a) b = f (c). b−a a
  • 13. The Mean Value Theorem Theorem (The Mean Value Theorem) c Let f be continuous on [a, b] and differentiable on (a, b). Then there exists a point c in (a, b) such that f (b) − f (a) b = f (c). b−a a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 11 / 28
  • 14. Rolle vs. MVT f (b) − f (a) f (c) = 0 = f (c) b−a c c b a b a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 12 / 28
  • 15. Rolle vs. MVT f (b) − f (a) f (c) = 0 = f (c) b−a c c b a b a If the x-axis is skewed the pictures look the same. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 12 / 28
  • 16. Proof of the Mean Value Theorem Proof. The line connecting (a, f (a)) and (b, f (b)) has equation f (b) − f (a) y − f (a) = (x − a) b−a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 13 / 28
  • 17. Proof of the Mean Value Theorem Proof. The line connecting (a, f (a)) and (b, f (b)) has equation f (b) − f (a) y − f (a) = (x − a) b−a Apply Rolle’s Theorem to the function f (b) − f (a) g (x) = f (x) − f (a) − (x − a). b−a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 13 / 28
  • 18. Proof of the Mean Value Theorem Proof. The line connecting (a, f (a)) and (b, f (b)) has equation f (b) − f (a) y − f (a) = (x − a) b−a Apply Rolle’s Theorem to the function f (b) − f (a) g (x) = f (x) − f (a) − (x − a). b−a Then g is continuous on [a, b] and differentiable on (a, b) since f is. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 13 / 28
  • 19. Proof of the Mean Value Theorem Proof. The line connecting (a, f (a)) and (b, f (b)) has equation f (b) − f (a) y − f (a) = (x − a) b−a Apply Rolle’s Theorem to the function f (b) − f (a) g (x) = f (x) − f (a) − (x − a). b−a Then g is continuous on [a, b] and differentiable on (a, b) since f is. Also g (a) = 0 and g (b) = 0 (check both) V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 13 / 28
  • 20. Proof of the Mean Value Theorem Proof. The line connecting (a, f (a)) and (b, f (b)) has equation f (b) − f (a) y − f (a) = (x − a) b−a Apply Rolle’s Theorem to the function f (b) − f (a) g (x) = f (x) − f (a) − (x − a). b−a Then g is continuous on [a, b] and differentiable on (a, b) since f is. Also g (a) = 0 and g (b) = 0 (check both) So by Rolle’s Theorem there exists a point c in (a, b) such that f (b) − f (a) 0 = g (c) = f (c) − . b−a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 13 / 28
  • 21. Using the MVT to count solutions Example Show that there is a unique solution to the equation x 3 − x = 100 in the interval [4, 5]. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 14 / 28
  • 22. Using the MVT to count solutions Example Show that there is a unique solution to the equation x 3 − x = 100 in the interval [4, 5]. Solution By the Intermediate Value Theorem, the function f (x) = x 3 − x must take the value 100 at some point on c in (4, 5). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 14 / 28
  • 23. Using the MVT to count solutions Example Show that there is a unique solution to the equation x 3 − x = 100 in the interval [4, 5]. Solution By the Intermediate Value Theorem, the function f (x) = x 3 − x must take the value 100 at some point on c in (4, 5). If there were two points c1 and c2 with f (c1 ) = f (c2 ) = 100, then somewhere between them would be a point c3 between them with f (c3 ) = 0. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 14 / 28
  • 24. Using the MVT to count solutions Example Show that there is a unique solution to the equation x 3 − x = 100 in the interval [4, 5]. Solution By the Intermediate Value Theorem, the function f (x) = x 3 − x must take the value 100 at some point on c in (4, 5). If there were two points c1 and c2 with f (c1 ) = f (c2 ) = 100, then somewhere between them would be a point c3 between them with f (c3 ) = 0. However, f (x) = 3x 2 − 1, which is positive all along (4, 5). So this is impossible. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 14 / 28
  • 25. Using the MVT to estimate Example We know that |sin x| ≤ 1 for all x. Show that |sin x| ≤ |x|. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 15 / 28
  • 26. Using the MVT to estimate Example We know that |sin x| ≤ 1 for all x. Show that |sin x| ≤ |x|. Solution Apply the MVT to the function f (t) = sin t on [0, x]. We get sin x − sin 0 = cos(c) x −0 for some c in (0, x). Since |cos(c)| ≤ 1, we get sin x ≤ 1 =⇒ |sin x| ≤ |x| x V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 15 / 28
  • 27. Using the MVT to estimate II Example Let f be a differentiable function with f (1) = 3 and f (x) < 2 for all x in [0, 5]. Could f (4) ≥ 9? V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 16 / 28
  • 28. Using the MVT to estimate II Example Let f be a differentiable function with f (1) = 3 and f (x) < 2 for all x in [0, 5]. Could f (4) ≥ 9? Solution y (4, 9) By MVT f (4) − f (1) (4, f (4)) = f (c) < 2 4−1 for some c in (1, 4). Therefore f (4) = f (1) + f (c)(3) < 3 + 2 · 3 = 9. (1, 3) So no, it is impossible that f (4) ≥ 9. x V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 16 / 28
  • 29. Food for Thought Question A driver travels along the New Jersey Turnpike using E-ZPass. The system takes note of the time and place the driver enters and exits the Turnpike. A week after his trip, the driver gets a speeding ticket in the mail. Which of the following best describes the situation? (a) E-ZPass cannot prove that the driver was speeding (b) E-ZPass can prove that the driver was speeding (c) The driver’s actual maximum speed exceeds his ticketed speed (d) Both (b) and (c). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 17 / 28
  • 30. Food for Thought Question A driver travels along the New Jersey Turnpike using E-ZPass. The system takes note of the time and place the driver enters and exits the Turnpike. A week after his trip, the driver gets a speeding ticket in the mail. Which of the following best describes the situation? (a) E-ZPass cannot prove that the driver was speeding (b) E-ZPass can prove that the driver was speeding (c) The driver’s actual maximum speed exceeds his ticketed speed (d) Both (b) and (c). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 17 / 28
  • 31. Outline Rolle’s Theorem The Mean Value Theorem Applications Why the MVT is the MITC Functions with derivatives that are zero MVT and differentiability V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 18 / 28
  • 32. Functions with derivatives that are zero Fact If f is constant on (a, b), then f (x) = 0 on (a, b). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 19 / 28
  • 33. Functions with derivatives that are zero Fact If f is constant on (a, b), then f (x) = 0 on (a, b). The limit of difference quotients must be 0 The tangent line to a line is that line, and a constant function’s graph is a horizontal line, which has slope 0. Implied by the power rule since c = cx 0 V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 19 / 28
  • 34. Functions with derivatives that are zero Fact If f is constant on (a, b), then f (x) = 0 on (a, b). The limit of difference quotients must be 0 The tangent line to a line is that line, and a constant function’s graph is a horizontal line, which has slope 0. Implied by the power rule since c = cx 0 Question If f (x) = 0 is f necessarily a constant function? V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 19 / 28
  • 35. Functions with derivatives that are zero Fact If f is constant on (a, b), then f (x) = 0 on (a, b). The limit of difference quotients must be 0 The tangent line to a line is that line, and a constant function’s graph is a horizontal line, which has slope 0. Implied by the power rule since c = cx 0 Question If f (x) = 0 is f necessarily a constant function? It seems true But so far no theorem (that we have proven) uses information about the derivative of a function to determine information about the function itself V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 19 / 28
  • 36. Why the MVT is the MITC Most Important Theorem In Calculus! Theorem Let f = 0 on an interval (a, b). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 20 / 28
  • 37. Why the MVT is the MITC Most Important Theorem In Calculus! Theorem Let f = 0 on an interval (a, b). Then f is constant on (a, b). V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 20 / 28
  • 38. Why the MVT is the MITC Most Important Theorem In Calculus! Theorem Let f = 0 on an interval (a, b). Then f is constant on (a, b). Proof. Pick any points x and y in (a, b) with x < y . Then f is continuous on [x, y ] and differentiable on (x, y ). By MVT there exists a point z in (x, y ) such that f (y ) − f (x) = f (z) = 0. y −x So f (y ) = f (x). Since this is true for all x and y in (a, b), then f is constant. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 20 / 28
  • 39. Functions with the same derivative Theorem Suppose f and g are two differentiable functions on (a, b) with f = g . Then f and g differ by a constant. That is, there exists a constant C such that f (x) = g (x) + C . V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 21 / 28
  • 40. Functions with the same derivative Theorem Suppose f and g are two differentiable functions on (a, b) with f = g . Then f and g differ by a constant. That is, there exists a constant C such that f (x) = g (x) + C . Proof. Let h(x) = f (x) − g (x) Then h (x) = f (x) − g (x) = 0 on (a, b) So h(x) = C , a constant This means f (x) − g (x) = C on (a, b) V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 21 / 28
  • 41. MVT and differentiability Example Let −x if x ≤ 0 f (x) = x2 if x ≥ 0 Is f differentiable at 0? V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 22 / 28
  • 42. MVT and differentiability Example Let −x if x ≤ 0 f (x) = x2 if x ≥ 0 Is f differentiable at 0? Solution (from the definition) We have f (x) − f (0) −x lim = lim = −1 x→0− x −0 x→0 − x f (x) − f (0) x2 lim+ = lim+ = lim+ x = 0 x→0 x −0 x→0 x x→0 Since these limits disagree, f is not differentiable at 0. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 22 / 28
  • 43. MVT and differentiability Example Let −x if x ≤ 0 f (x) = x2 if x ≥ 0 Is f differentiable at 0? Solution (Sort of) If x < 0, then f (x) = −1. If x > 0, then f (x) = 2x. Since lim f (x) = 0 and lim f (x) = −1, x→0+ x→0− the limit lim f (x) does not exist and so f is not differentiable at 0. x→0 V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 22 / 28
  • 44. Why only “sort of”? f (x) This solution is valid but less y f (x) direct. We seem to be using the following fact: If lim f (x) x→a does not exist, then f is not x differentiable at a. equivalently: If f is differentiable at a, then lim f (x) exists. x→a But this “fact” is not true! V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 23 / 28
  • 45. Differentiable with discontinuous derivative It is possible for a function f to be differentiable at a even if lim f (x) x→a does not exist. Example x 2 sin(1/x) if x = 0 Let f (x) = . Then when x = 0, 0 if x = 0 f (x) = 2x sin(1/x) + x 2 cos(1/x)(−1/x 2 ) = 2x sin(1/x) − cos(1/x), which has no limit at 0. However, f (x) − f (0) x 2 sin(1/x) f (0) = lim = lim = lim x sin(1/x) = 0 x→0 x −0 x→0 x x→0 So f (0) = 0. Hence f is differentiable for all x, but f is not continuous at 0! V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 24 / 28
  • 46. Differentiability FAIL f (x) f (x) x x This function is differentiable at But the derivative is not 0. continuous at 0! V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 25 / 28
  • 47. MVT to the rescue Lemma Suppose f is continuous on [a, b] and lim+ f (x) = m. Then x→a f (x) − f (a) lim+ = m. x→a x −a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 26 / 28
  • 48. MVT to the rescue Lemma Suppose f is continuous on [a, b] and lim+ f (x) = m. Then x→a f (x) − f (a) lim+ = m. x→a x −a Proof. Choose x near a and greater than a. Then f (x) − f (a) = f (cx ) x −a for some cx where a < cx < x. As x → a, cx → a as well, so: f (x) − f (a) lim = lim+ f (cx ) = lim+ f (x) = m. x→a+ x −a x→a x→a V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 26 / 28
  • 49. Theorem Suppose lim f (x) = m1 and lim+ f (x) = m2 x→a− x→a If m1 = m2 , then f is differentiable at a. If m1 = m2 , then f is not differentiable at a. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 27 / 28
  • 50. Theorem Suppose lim f (x) = m1 and lim+ f (x) = m2 x→a− x→a If m1 = m2 , then f is differentiable at a. If m1 = m2 , then f is not differentiable at a. Proof. We know by the lemma that f (x) − f (a) lim = lim f (x) x→a− x −a x→a− f (x) − f (a) lim+ = lim+ f (x) x→a x −a x→a The two-sided limit exists if (and only if) the two right-hand sides agree. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 27 / 28
  • 51. Summary Rolle’s Theorem: under suitable conditions, functions must have critical points. Mean Value Theorem: under suitable conditions, functions must have an instantaneous rate of change equal to the average rate of change. A function whose derivative is identically zero on an interval must be constant on that interval. E-ZPass is kinder than we realized. V63.0121.002.2010Su, Calculus I (NYU) Section 4.2 The Mean Value Theorem June 8, 2010 28 / 28