SlideShare ist ein Scribd-Unternehmen logo
1 von 28
Downloaden Sie, um offline zu lesen
Section 12.1–12.2
                       Double Integrals over Rectangles
                              Iterated Integrals

                                         Math 21a


                                      March 17, 2008


        Announcements
            ◮    Office hours Tuesday, Wednesday 2–4pm SC 323
            ◮    Problem Sessions: Mon, 8:30; Thur, 7:30; SC 103b

.       .
Image: Flickr user Cobalt123
                                                         .   .      .   .   .   .
Announcements




    ◮   Office hours Tuesday, Wednesday 2–4pm SC 323
    ◮   Problem Sessions: Mon, 8:30; Thur, 7:30; SC 103b




                                                .   .      .   .   .   .
Outline


   Last Time


   Double Integrals over Rectangles
     Recall the definite integral
     Definite integrals in two dimensions


   Iterated Integrals
       Partial Integration
       Fubini’s Theorem
       Average value




                                           .   .   .   .   .   .
Outline


   Last Time


   Double Integrals over Rectangles
     Recall the definite integral
     Definite integrals in two dimensions


   Iterated Integrals
       Partial Integration
       Fubini’s Theorem
       Average value




                                           .   .   .   .   .   .
Cavalieri’s method
   Let f be a positive function defined on the interval [a, b]. We want to
   find the area between x = a, x = b, y = 0, and y = f(x).
   For each positive integer n, divide up the interval into n pieces. Then
          b−a
   ∆x =         . For each i between 1 and n, let xi be the nth step
            n
   between a and b. So

                                            x0 = a
                                                                 b−a
                                            x 1 = x 0 + ∆x = a +
                                                                    n
                                                                     b−a
                                            x 2 = x 1 + ∆x = a + 2 ·
                                                                      n
                                           ······
                                                          b−a
                                             xi = a + i ·
                                                           n
        x x x
        .0 .1 .2 . i . n −1 . n
                 xx x                      ······
    .    . . . . . . .
        .
        a                                                 b−a
                             b
                             .              xn = a + n ·       =b
                                                   .    .   n
                                                            .    .    .      .
Forming Riemann sums

  We have many choices of how to approximate the area:

    Ln = f(x0 )∆x + f(x1 )∆x + · · · + f(xn−1 )∆x
   Rn = f(x1 )∆x + f(x2 )∆x + · · · + f(xn )∆x
          (          )        (         )                  (              )
            x0 + x 1            x1 + x2                      x n −1 + x n
   Mn = f              ∆x + f               ∆x + · · · + f                  ∆x
               2                    2                              2




                                                     .     .    .    .    .      .
Forming Riemann sums

  We have many choices of how to approximate the area:

    Ln = f(x0 )∆x + f(x1 )∆x + · · · + f(xn−1 )∆x
   Rn = f(x1 )∆x + f(x2 )∆x + · · · + f(xn )∆x
          (          )        (         )                  (              )
            x0 + x 1            x1 + x2                      x n −1 + x n
   Mn = f              ∆x + f               ∆x + · · · + f                  ∆x
               2                    2                              2

  In general, choose x∗ to be a point in the ith interval [xi−1 , xi ]. Form
                      i
  the Riemann sum
                  Sn = f(x∗ )∆x + f(x∗ )∆x + · · · + f(x∗ )∆x
                           1          2                 n
                       ∑ n
                     =       f(x∗ )∆x
                                i
                        i=1




                                                     .     .    .    .    .      .
Definition
The definite integral of f from a to b is the limit
                    ∫     b                   ∑
                                              n
                              f(x) dx = lim         f(x∗ )∆x
                                                       i
                      a                n→∞
                                              i=1


(The big deal is that for continuous functions this limit is the same no
matter how you choose the x∗ ).i




                                                        .      .   .   .   .   .
The problem




  Let R = [a, b] × [c, d] be a rectangle in the plane, f a positive function
  defined on R, and

         S = { (x, y, z) | a ≤ x ≤ b, c ≤ y ≤ d, 0 ≤ z ≤ f(x, y) }

  Our goal is to find the volume of S




                                                    .    .    .    .    .      .
The strategy: Divide and conquer


   For each m and n, divide the interval [a, b] into m subintervals of
   equal width, and the interval [c, d] into n subintervals. For each i and
   j, form the subrectangles

                          Rij = [xi−1 , xi ] × [yj−1 , yj ]

   Choose a sample point (x∗ , y∗ ) in each subrectangle and form the
                           ij ij
   Riemann sum
                               ∑∑m    n
                      Smn =              f(x∗ , y∗ ) ∆A
                                            ij ij
                                  i=1 j=1

   where ∆A = ∆x ∆y.




                                                              .   .   .   .   .   .
Definition
The double integral of f over the rectangle R is
             ∫∫                          ∑∑
                                         m n
                  f(x, y) dA =    lim              f(x∗ , y∗ ) ∆A
                                                      ij ij
                                 m,n→∞
              R                          i=1 j=1



(Again, for continuous f this limit is the same regardless of method
for choosing the sample points.)




                                                     .     .        .   .   .   .
Worksheet #1




  Problem
  Estimate the volume of the solid that lies below the surface z = xy and
  above the rectangle [0, 6] × [0, 4] in the xy-plane using a Riemann sum
  with m = 3 and n = 2. Take the sample point to be the upper right
  corner of each rectangle.




                                                   .    .    .     .    .   .
Worksheet #1




  Problem
  Estimate the volume of the solid that lies below the surface z = xy and
  above the rectangle [0, 6] × [0, 4] in the xy-plane using a Riemann sum
  with m = 3 and n = 2. Take the sample point to be the upper right
  corner of each rectangle.

  Answer
  288




                                                   .    .    .     .    .   .
Theorem (Midpoint Rule)
                     ∫∫                  ∑∑
                                         m n
                          f(x, y) dA ≈             f(¯i , ¯j ) ∆A
                                                     x y
                      R                  i=1 j=1

where ¯i is the midpoint of [xi−1 , xi ] and ¯j is the midpoint of [yj−1 , yj ].
      x                                      y




                                                         .      .    .    .        .   .
Worksheet #2




  Problem
  Use the Midpoint Rule to evaluate the volume of the solid in Problem 1.




                                                    .    .    .    .    .   .
Worksheet #2




  Problem
  Use the Midpoint Rule to evaluate the volume of the solid in Problem 1.

  Answer
  144




                                                    .    .    .    .    .   .
Outline


   Last Time


   Double Integrals over Rectangles
     Recall the definite integral
     Definite integrals in two dimensions


   Iterated Integrals
       Partial Integration
       Fubini’s Theorem
       Average value




                                           .   .   .   .   .   .
Partial Integration


   Let f be a function on a rectangle R = [a, b] × [c, d]. Then for each
   fixed x we have a number
                                     ∫ d
                             A(x) =      f(x, y) dy
                                        c

   The is a function of x, and can be integrated itself. So we have an
   iterated integral
                    ∫ b            ∫ b [∫ d           ]
                         A(x) dx =          f(x, y) dy dx
                      a             a       c




                                                   .    .    .    .      .   .
Worksheet #3




  Problem
  Calculate
        ∫ 3∫    1                           ∫   1∫ 3
                    (1 + 4xy) dx dy   and              (1 + 4xy) dy dx.
        1   0                               0     1




                                                  .       .    .    .     .   .
Fubini’s Theorem


   Double integrals look hard. Iterated integrals look easy/easier. The
   good news is:
   Theorem (Fubini’s Theorem)
   If f is continuous on R = [a, b] × [c, d], then
          ∫∫                  ∫ b∫     d                     ∫ d∫         b
               f(x, y) dA =                f(x, y) dy dx =                    f(x, y) dx dy
                               a   c                          c       a
           R

   This is also true if f is bounded on R, f is discontinuous only on a finite
   number of smooth curves, and the iterated integrals exist.




                                                                  .       .       .     .     .   .
Worksheet #4




  Problem
  Evaluate the volume of the solid in Problem 1 by computing an iterated
  integral.




                                                   .    .    .    .        .   .
Worksheet #4




  Problem
  Evaluate the volume of the solid in Problem 1 by computing an iterated
  integral.

  Answer
  144




                                                   .    .    .    .        .   .
Meet the mathematician: Guido Fubini




   ◮   Italian, 1879–1943
   ◮   graduated Pisa 1900
   ◮   professor in Turin,
       1908–1938
   ◮   escaped to US and died
       five years later




                                       .   .   .   .   .   .
Worksheet #5



  Problem
  Calculate                     ∫∫
                                       xy2
                                            dA
                                     x2 + 1
                                R

  where R = [0, 1] × [−3, 3].




                                                 .   .   .   .   .   .
Worksheet #5



  Problem
  Calculate                     ∫∫
                                       xy2
                                            dA
                                     x2 + 1
                                R

  where R = [0, 1] × [−3, 3].

  Answer
  ln 512 = 9 ln 2




                                                 .   .   .   .   .   .
Average value



    ◮   One variable: If f is a function defined on [a, b], then
                                            ∫   b
                                       1
                             fave   =               f(x) dx
                                      b−a   a

    ◮   Two variables: If f is a function defined on a rectangle R, then
                                            ∫∫
                                       1
                            fave =              f(x, y) dA
                                   Area(R)
                                            R




                                                         .    .   .   .   .   .
Worksheet #6



  Problem
  Find the average value of f(x, y) = x2 y over the rectangle
  R = [−1, 1] × [0, 5].




                                                      .    .    .   .   .   .
Worksheet #6



  Problem
  Find the average value of f(x, y) = x2 y over the rectangle
  R = [−1, 1] × [0, 5].

  Answer
                                ∫   5∫ 1
                            1                             5
                                           x2 y dx dy =
                           10   0     −1                  6




                                                          .   .   .   .   .   .

Weitere ähnliche Inhalte

Was ist angesagt?

Gabarito completo anton_calculo_8ed_caps_01_08
Gabarito completo anton_calculo_8ed_caps_01_08Gabarito completo anton_calculo_8ed_caps_01_08
Gabarito completo anton_calculo_8ed_caps_01_08
joseotaviosurdi
 

Was ist angesagt? (15)

Double integration
Double integrationDouble integration
Double integration
 
1574 multiple integral
1574 multiple integral1574 multiple integral
1574 multiple integral
 
Low Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse ProblemsLow Complexity Regularization of Inverse Problems
Low Complexity Regularization of Inverse Problems
 
近似ベイズ計算によるベイズ推定
近似ベイズ計算によるベイズ推定近似ベイズ計算によるベイズ推定
近似ベイズ計算によるベイズ推定
 
BBMP1103 - Sept 2011 exam workshop - part 8
BBMP1103 - Sept 2011 exam workshop - part 8BBMP1103 - Sept 2011 exam workshop - part 8
BBMP1103 - Sept 2011 exam workshop - part 8
 
Mesh Processing Course : Active Contours
Mesh Processing Course : Active ContoursMesh Processing Course : Active Contours
Mesh Processing Course : Active Contours
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)
 
A Generalized Metric Space and Related Fixed Point Theorems
A Generalized Metric Space and Related Fixed Point TheoremsA Generalized Metric Space and Related Fixed Point Theorems
A Generalized Metric Space and Related Fixed Point Theorems
 
Crib Sheet AP Calculus AB and BC exams
Crib Sheet AP Calculus AB and BC examsCrib Sheet AP Calculus AB and BC exams
Crib Sheet AP Calculus AB and BC exams
 
Maths-double integrals
Maths-double integralsMaths-double integrals
Maths-double integrals
 
整卷
整卷整卷
整卷
 
Proximal Splitting and Optimal Transport
Proximal Splitting and Optimal TransportProximal Splitting and Optimal Transport
Proximal Splitting and Optimal Transport
 
Gabarito completo anton_calculo_8ed_caps_01_08
Gabarito completo anton_calculo_8ed_caps_01_08Gabarito completo anton_calculo_8ed_caps_01_08
Gabarito completo anton_calculo_8ed_caps_01_08
 
Derivadas
DerivadasDerivadas
Derivadas
 
Lesson 26: The Fundamental Theorem of Calculus (Section 4 version)
Lesson 26: The Fundamental Theorem of Calculus (Section 4 version)Lesson 26: The Fundamental Theorem of Calculus (Section 4 version)
Lesson 26: The Fundamental Theorem of Calculus (Section 4 version)
 

Andere mochten auch

Lesson 19: Double Integrals over General Regions
Lesson 19: Double Integrals over General RegionsLesson 19: Double Integrals over General Regions
Lesson 19: Double Integrals over General Regions
Matthew Leingang
 
Lesson 19: Optimization Problems
Lesson 19: Optimization ProblemsLesson 19: Optimization Problems
Lesson 19: Optimization Problems
Matthew Leingang
 

Andere mochten auch (20)

Lesson 19: Double Integrals over General Regions
Lesson 19: Double Integrals over General RegionsLesson 19: Double Integrals over General Regions
Lesson 19: Double Integrals over General Regions
 
Making Lesson Plans
Making Lesson PlansMaking Lesson Plans
Making Lesson Plans
 
Lesson 20: (More) Optimization Problems
Lesson 20: (More) Optimization ProblemsLesson 20: (More) Optimization Problems
Lesson 20: (More) Optimization Problems
 
Lesson 18: Graphing
Lesson 18: GraphingLesson 18: Graphing
Lesson 18: Graphing
 
Lesson 8: Curves, Arc Length, Acceleration
Lesson 8: Curves, Arc Length, AccelerationLesson 8: Curves, Arc Length, Acceleration
Lesson 8: Curves, Arc Length, Acceleration
 
Lesson 17: The Mean Value Theorem and the shape of curves
Lesson 17: The Mean Value Theorem and the shape of curvesLesson 17: The Mean Value Theorem and the shape of curves
Lesson 17: The Mean Value Theorem and the shape of curves
 
Analytical class spectroscopy, turbidimetry
Analytical class  spectroscopy, turbidimetryAnalytical class  spectroscopy, turbidimetry
Analytical class spectroscopy, turbidimetry
 
Lesson 19: Optimization Problems
Lesson 19: Optimization ProblemsLesson 19: Optimization Problems
Lesson 19: Optimization Problems
 
Fourier series and fourier integral
Fourier series and fourier integralFourier series and fourier integral
Fourier series and fourier integral
 
Lesson 9: Parametric Surfaces
Lesson 9: Parametric SurfacesLesson 9: Parametric Surfaces
Lesson 9: Parametric Surfaces
 
Lesson 9: The Product and Quotient Rule
Lesson 9: The Product and Quotient RuleLesson 9: The Product and Quotient Rule
Lesson 9: The Product and Quotient Rule
 
Lesson 10: Functions and Level Sets
Lesson 10: Functions and Level SetsLesson 10: Functions and Level Sets
Lesson 10: Functions and Level Sets
 
Lesson 8: Derivatives of Polynomials and Exponential functions
Lesson 8: Derivatives of Polynomials and Exponential functionsLesson 8: Derivatives of Polynomials and Exponential functions
Lesson 8: Derivatives of Polynomials and Exponential functions
 
Math 21a Midterm I Review
Math 21a Midterm I ReviewMath 21a Midterm I Review
Math 21a Midterm I Review
 
Lesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric FunctionsLesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric Functions
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functions
 
Lesson 17: The Method of Lagrange Multipliers
Lesson 17: The Method of Lagrange MultipliersLesson 17: The Method of Lagrange Multipliers
Lesson 17: The Method of Lagrange Multipliers
 
Lesson 7: What does f' say about f?
Lesson 7: What does f' say about f?Lesson 7: What does f' say about f?
Lesson 7: What does f' say about f?
 
Lesson 20: Integration in Polar Coordinates
Lesson 20: Integration in Polar CoordinatesLesson 20: Integration in Polar Coordinates
Lesson 20: Integration in Polar Coordinates
 
Lesson 21: Indeterminate forms and L'Hôpital's Rule
Lesson 21: Indeterminate forms and L'Hôpital's RuleLesson 21: Indeterminate forms and L'Hôpital's Rule
Lesson 21: Indeterminate forms and L'Hôpital's Rule
 

Ähnlich wie Lesson18 Double Integrals Over Rectangles Slides

Bai giang ham so kha vi va vi phan cua ham nhieu bien
Bai giang ham so kha vi va vi phan cua ham nhieu bienBai giang ham so kha vi va vi phan cua ham nhieu bien
Bai giang ham so kha vi va vi phan cua ham nhieu bien
Nhan Nguyen
 
X2 T05 06 Partial Fractions
X2 T05 06 Partial FractionsX2 T05 06 Partial Fractions
X2 T05 06 Partial Fractions
Nigel Simmons
 
X2 T06 01 Discs & Washers
X2 T06 01 Discs & WashersX2 T06 01 Discs & Washers
X2 T06 01 Discs & Washers
Nigel Simmons
 

Ähnlich wie Lesson18 Double Integrals Over Rectangles Slides (20)

Lesson 24: The Definite Integral (Section 4 version)
Lesson 24: The Definite Integral (Section 4 version)Lesson 24: The Definite Integral (Section 4 version)
Lesson 24: The Definite Integral (Section 4 version)
 
Lesson 24: The Definite Integral (Section 10 version)
Lesson 24: The Definite Integral (Section 10 version)Lesson 24: The Definite Integral (Section 10 version)
Lesson 24: The Definite Integral (Section 10 version)
 
Lesson 28: Lagrange Multipliers II
Lesson 28: Lagrange Multipliers IILesson 28: Lagrange Multipliers II
Lesson 28: Lagrange Multipliers II
 
Lesson 28: Lagrange Multipliers II
Lesson 28: Lagrange  Multipliers IILesson 28: Lagrange  Multipliers II
Lesson 28: Lagrange Multipliers II
 
Lesson 21: Curve Sketching (Section 4 version)
Lesson 21: Curve Sketching (Section 4 version)Lesson 21: Curve Sketching (Section 4 version)
Lesson 21: Curve Sketching (Section 4 version)
 
Bai giang ham so kha vi va vi phan cua ham nhieu bien
Bai giang ham so kha vi va vi phan cua ham nhieu bienBai giang ham so kha vi va vi phan cua ham nhieu bien
Bai giang ham so kha vi va vi phan cua ham nhieu bien
 
X2 T05 06 Partial Fractions
X2 T05 06 Partial FractionsX2 T05 06 Partial Fractions
X2 T05 06 Partial Fractions
 
Lesson 21: Curve Sketching (Section 10 version)
Lesson 21: Curve Sketching (Section 10 version)Lesson 21: Curve Sketching (Section 10 version)
Lesson 21: Curve Sketching (Section 10 version)
 
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)
 
Lesson 21: Curve Sketching II (Section 10 version)
Lesson 21: Curve Sketching II (Section 10 version)Lesson 21: Curve Sketching II (Section 10 version)
Lesson 21: Curve Sketching II (Section 10 version)
 
Lesson 21: Curve Sketching II (Section 4 version)
Lesson 21: Curve Sketching  II (Section 4 version)Lesson 21: Curve Sketching  II (Section 4 version)
Lesson 21: Curve Sketching II (Section 4 version)
 
Lesson 9: Basic Differentiation Rules
Lesson 9: Basic Differentiation RulesLesson 9: Basic Differentiation Rules
Lesson 9: Basic Differentiation Rules
 
Lesson 25: Evaluating Definite Integrals (Section 10 version)
Lesson 25: Evaluating Definite Integrals (Section 10 version)Lesson 25: Evaluating Definite Integrals (Section 10 version)
Lesson 25: Evaluating Definite Integrals (Section 10 version)
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)
 
Lesson 25: Evaluating Definite Integrals (Section 4 version)
Lesson 25: Evaluating Definite Integrals (Section 4 version)Lesson 25: Evaluating Definite Integrals (Section 4 version)
Lesson 25: Evaluating Definite Integrals (Section 4 version)
 
0708 ch 7 day 8
0708 ch 7 day 80708 ch 7 day 8
0708 ch 7 day 8
 
X2 T06 01 Discs & Washers
X2 T06 01 Discs & WashersX2 T06 01 Discs & Washers
X2 T06 01 Discs & Washers
 
Lesson 22: Graphing
Lesson 22: GraphingLesson 22: Graphing
Lesson 22: Graphing
 
Lesson 22: Graphing
Lesson 22: GraphingLesson 22: Graphing
Lesson 22: Graphing
 
Lesson 11: Limits and Continuity
Lesson 11: Limits and ContinuityLesson 11: Limits and Continuity
Lesson 11: Limits and Continuity
 

Mehr von Matthew Leingang

Mehr von Matthew Leingang (20)

Streamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choiceStreamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choice
 
Electronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsElectronic Grading of Paper Assessments
Electronic Grading of Paper Assessments
 
Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)
 
Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (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)
Lesson 26: The Fundamental Theorem of Calculus (handout)
 
Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)
 
Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)
 
Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)
 
Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)
 
Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)
 
Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)
 
Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)
 
Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)
 
Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)
 
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 18: Maximum and Minimum Values (slides)
Lesson 18: Maximum and Minimum Values (slides)Lesson 18: Maximum and Minimum Values (slides)
Lesson 18: Maximum and Minimum Values (slides)
 

Kürzlich hochgeladen

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
Enterprise Knowledge
 

Kürzlich hochgeladen (20)

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 

Lesson18 Double Integrals Over Rectangles Slides

  • 1. Section 12.1–12.2 Double Integrals over Rectangles Iterated Integrals Math 21a March 17, 2008 Announcements ◮ Office hours Tuesday, Wednesday 2–4pm SC 323 ◮ Problem Sessions: Mon, 8:30; Thur, 7:30; SC 103b . . Image: Flickr user Cobalt123 . . . . . .
  • 2. Announcements ◮ Office hours Tuesday, Wednesday 2–4pm SC 323 ◮ Problem Sessions: Mon, 8:30; Thur, 7:30; SC 103b . . . . . .
  • 3. Outline Last Time Double Integrals over Rectangles Recall the definite integral Definite integrals in two dimensions Iterated Integrals Partial Integration Fubini’s Theorem Average value . . . . . .
  • 4. Outline Last Time Double Integrals over Rectangles Recall the definite integral Definite integrals in two dimensions Iterated Integrals Partial Integration Fubini’s Theorem Average value . . . . . .
  • 5. Cavalieri’s method Let f be a positive function defined on the interval [a, b]. We want to find the area between x = a, x = b, y = 0, and y = f(x). For each positive integer n, divide up the interval into n pieces. Then b−a ∆x = . For each i between 1 and n, let xi be the nth step n between a and b. So x0 = a b−a x 1 = x 0 + ∆x = a + n b−a x 2 = x 1 + ∆x = a + 2 · n ······ b−a xi = a + i · n x x x .0 .1 .2 . i . n −1 . n xx x ······ . . . . . . . . . a b−a b . xn = a + n · =b . . n . . . .
  • 6. Forming Riemann sums We have many choices of how to approximate the area: Ln = f(x0 )∆x + f(x1 )∆x + · · · + f(xn−1 )∆x Rn = f(x1 )∆x + f(x2 )∆x + · · · + f(xn )∆x ( ) ( ) ( ) x0 + x 1 x1 + x2 x n −1 + x n Mn = f ∆x + f ∆x + · · · + f ∆x 2 2 2 . . . . . .
  • 7. Forming Riemann sums We have many choices of how to approximate the area: Ln = f(x0 )∆x + f(x1 )∆x + · · · + f(xn−1 )∆x Rn = f(x1 )∆x + f(x2 )∆x + · · · + f(xn )∆x ( ) ( ) ( ) x0 + x 1 x1 + x2 x n −1 + x n Mn = f ∆x + f ∆x + · · · + f ∆x 2 2 2 In general, choose x∗ to be a point in the ith interval [xi−1 , xi ]. Form i the Riemann sum Sn = f(x∗ )∆x + f(x∗ )∆x + · · · + f(x∗ )∆x 1 2 n ∑ n = f(x∗ )∆x i i=1 . . . . . .
  • 8. Definition The definite integral of f from a to b is the limit ∫ b ∑ n f(x) dx = lim f(x∗ )∆x i a n→∞ i=1 (The big deal is that for continuous functions this limit is the same no matter how you choose the x∗ ).i . . . . . .
  • 9. The problem Let R = [a, b] × [c, d] be a rectangle in the plane, f a positive function defined on R, and S = { (x, y, z) | a ≤ x ≤ b, c ≤ y ≤ d, 0 ≤ z ≤ f(x, y) } Our goal is to find the volume of S . . . . . .
  • 10. The strategy: Divide and conquer For each m and n, divide the interval [a, b] into m subintervals of equal width, and the interval [c, d] into n subintervals. For each i and j, form the subrectangles Rij = [xi−1 , xi ] × [yj−1 , yj ] Choose a sample point (x∗ , y∗ ) in each subrectangle and form the ij ij Riemann sum ∑∑m n Smn = f(x∗ , y∗ ) ∆A ij ij i=1 j=1 where ∆A = ∆x ∆y. . . . . . .
  • 11. Definition The double integral of f over the rectangle R is ∫∫ ∑∑ m n f(x, y) dA = lim f(x∗ , y∗ ) ∆A ij ij m,n→∞ R i=1 j=1 (Again, for continuous f this limit is the same regardless of method for choosing the sample points.) . . . . . .
  • 12. Worksheet #1 Problem Estimate the volume of the solid that lies below the surface z = xy and above the rectangle [0, 6] × [0, 4] in the xy-plane using a Riemann sum with m = 3 and n = 2. Take the sample point to be the upper right corner of each rectangle. . . . . . .
  • 13. Worksheet #1 Problem Estimate the volume of the solid that lies below the surface z = xy and above the rectangle [0, 6] × [0, 4] in the xy-plane using a Riemann sum with m = 3 and n = 2. Take the sample point to be the upper right corner of each rectangle. Answer 288 . . . . . .
  • 14. Theorem (Midpoint Rule) ∫∫ ∑∑ m n f(x, y) dA ≈ f(¯i , ¯j ) ∆A x y R i=1 j=1 where ¯i is the midpoint of [xi−1 , xi ] and ¯j is the midpoint of [yj−1 , yj ]. x y . . . . . .
  • 15. Worksheet #2 Problem Use the Midpoint Rule to evaluate the volume of the solid in Problem 1. . . . . . .
  • 16. Worksheet #2 Problem Use the Midpoint Rule to evaluate the volume of the solid in Problem 1. Answer 144 . . . . . .
  • 17. Outline Last Time Double Integrals over Rectangles Recall the definite integral Definite integrals in two dimensions Iterated Integrals Partial Integration Fubini’s Theorem Average value . . . . . .
  • 18. Partial Integration Let f be a function on a rectangle R = [a, b] × [c, d]. Then for each fixed x we have a number ∫ d A(x) = f(x, y) dy c The is a function of x, and can be integrated itself. So we have an iterated integral ∫ b ∫ b [∫ d ] A(x) dx = f(x, y) dy dx a a c . . . . . .
  • 19. Worksheet #3 Problem Calculate ∫ 3∫ 1 ∫ 1∫ 3 (1 + 4xy) dx dy and (1 + 4xy) dy dx. 1 0 0 1 . . . . . .
  • 20. Fubini’s Theorem Double integrals look hard. Iterated integrals look easy/easier. The good news is: Theorem (Fubini’s Theorem) If f is continuous on R = [a, b] × [c, d], then ∫∫ ∫ b∫ d ∫ d∫ b f(x, y) dA = f(x, y) dy dx = f(x, y) dx dy a c c a R This is also true if f is bounded on R, f is discontinuous only on a finite number of smooth curves, and the iterated integrals exist. . . . . . .
  • 21. Worksheet #4 Problem Evaluate the volume of the solid in Problem 1 by computing an iterated integral. . . . . . .
  • 22. Worksheet #4 Problem Evaluate the volume of the solid in Problem 1 by computing an iterated integral. Answer 144 . . . . . .
  • 23. Meet the mathematician: Guido Fubini ◮ Italian, 1879–1943 ◮ graduated Pisa 1900 ◮ professor in Turin, 1908–1938 ◮ escaped to US and died five years later . . . . . .
  • 24. Worksheet #5 Problem Calculate ∫∫ xy2 dA x2 + 1 R where R = [0, 1] × [−3, 3]. . . . . . .
  • 25. Worksheet #5 Problem Calculate ∫∫ xy2 dA x2 + 1 R where R = [0, 1] × [−3, 3]. Answer ln 512 = 9 ln 2 . . . . . .
  • 26. Average value ◮ One variable: If f is a function defined on [a, b], then ∫ b 1 fave = f(x) dx b−a a ◮ Two variables: If f is a function defined on a rectangle R, then ∫∫ 1 fave = f(x, y) dA Area(R) R . . . . . .
  • 27. Worksheet #6 Problem Find the average value of f(x, y) = x2 y over the rectangle R = [−1, 1] × [0, 5]. . . . . . .
  • 28. Worksheet #6 Problem Find the average value of f(x, y) = x2 y over the rectangle R = [−1, 1] × [0, 5]. Answer ∫ 5∫ 1 1 5 x2 y dx dy = 10 0 −1 6 . . . . . .