SlideShare una empresa de Scribd logo
1 de 51
Descargar para leer sin conexión
Section	4.5
                Optimization	II

                V63.0121.034, Calculus	I



                  November	25, 2009



Announcements
   Final	Exam, December	18, 2:00–3:50pm

                                      .    .   .   .   .   .
Outline



  Recall


  More	examples
    Addition
    Distance
    Triangles
    Economics
    The	Statue	of	Liberty




                            .   .   .   .   .   .
Checklist	for	optimization	problems


    1. Understand	the	Problem What	is	known? What	is
       unknown? What	are	the	conditions?
    2. Draw	a	diagram
    3. Introduce	Notation
    4. Express	the	“objective	function” Q in	terms	of	the	other
       symbols
    5. If Q is	a	function	of	more	than	one	“decision	variable”, use
       the	given	information	to	eliminate	all	but	one	of	them.
    6. Find	the	absolute	maximum	(or	minimum, depending	on	the
       problem)	of	the	function	on	its	domain.




                                               .   .    .   .     .   .
Recall: The	Closed	Interval	Method
See	Section	4.1




     To	find	the	extreme	values	of	a	function f on [a, b], we	need	to:
          Evaluate f at	the endpoints a and b
          Evaluate f at	the critical	points x where	either f′ (x) = 0 or f is
          not	differentiable	at x.
          The	points	with	the	largest	function	value	are	the	global
          maximum	points
          The	points	with	the	smallest	or	most	negative	function	value
          are	the	global	minimum	points.




                                                     .    .    .    .    .      .
Recall: The	First	Derivative	Test
See	Section	4.3




     Theorem	(The	First	Derivative	Test)
     Let f be	a	continuous	function	and c a	critical	point	of f in (a, b).
          If f′ (x) > 0 on (a, c) (i.e., “before c”)	and f′ (x) < 0 on (c, b)
          (i.e., “after c”, then c is	a	local	maximum	for f.
          If f′ (x) < 0 on (a, c) and f′ (x) > 0 on (c, b), then c is	a	local
          minimum	for f.
          If f′ (x) has	the	same	sign	on (a, c) and (c, b), then c is	not	a
          local	extremum.




                                                      .    .    .    .    .     .
Recall: The	Second	Derivative	Test
See	Section	4.3




     Theorem	(The	Second	Derivative	Test)
     Let f, f′ , and f′′ be	continuous. Let c be	be	a	point	in (a, b) with
     f′ (c) = 0.
          If f′′ (c) < 0, then f(c) is	a	local	maximum.
          If f′′ (c) > 0, then f(c) is	a	local	minimum.

     If f′′ (c) = 0, the	second	derivative	test	is	inconclusive	(this	does
     not	mean c is	neither; we	just	don’t	know	yet).




                                                     .    .    .    .    .   .
Which	to	use	when? The	bottom	line




      Use	CIM if	it	applies: the	domain	is	a	closed, bounded
      interval
      If	domain	is	not	closed	or	not	bounded, use	2DT if	you	like
      to	take	derivatives, or	1DT if	you	like	to	compare	signs.




                                             .   .    .   .    .    .
Outline



  Recall


  More	examples
    Addition
    Distance
    Triangles
    Economics
    The	Statue	of	Liberty




                            .   .   .   .   .   .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.




                                             .    .   .   .      .   .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.

   Solution
       Objective: minimize S = x + y subject	to	the	constraint	that
       xy = 16




                                              .    .   .    .    .    .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.

   Solution
       Objective: minimize S = x + y subject	to	the	constraint	that
       xy = 16
       Eliminate y: y = 16/x so S = x + 16/x. The	domain	of
       consideration	is (0, ∞).




                                              .    .   .    .    .    .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.

   Solution
       Objective: minimize S = x + y subject	to	the	constraint	that
       xy = 16
       Eliminate y: y = 16/x so S = x + 16/x. The	domain	of
       consideration	is (0, ∞).
       Find	the	critical	points: S′ (x) = 1 − 16/x2 , which	is 0 when
       x = 4.




                                                .    .   .    .    .    .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.

   Solution
       Objective: minimize S = x + y subject	to	the	constraint	that
       xy = 16
       Eliminate y: y = 16/x so S = x + 16/x. The	domain	of
       consideration	is (0, ∞).
       Find	the	critical	points: S′ (x) = 1 − 16/x2 , which	is 0 when
       x = 4.
       Classify	the	critical	points: S′′ (x) = 32/x3 , which	is	always
       positive. So	the	graph	is	always	concave	up, 4 is	a	local	min,
       and	therefore	the	global	min.


                                                .    .   .    .    .     .
Addition	with	a	constraint
   Example
   Find	two	positive	numbers x and y with xy = 16 and x + y as
   small	as	possible.

   Solution
       Objective: minimize S = x + y subject	to	the	constraint	that
       xy = 16
       Eliminate y: y = 16/x so S = x + 16/x. The	domain	of
       consideration	is (0, ∞).
       Find	the	critical	points: S′ (x) = 1 − 16/x2 , which	is 0 when
       x = 4.
       Classify	the	critical	points: S′′ (x) = 32/x3 , which	is	always
       positive. So	the	graph	is	always	concave	up, 4 is	a	local	min,
       and	therefore	the	global	min.
       So	the	numbers	are x = y = 4, Smin = 8.
                                                .    .   .    .    .     .
Distance
  Example
  Find	the	point P on	the	parabola y = x2 closest	to	the	point (3, 0).




                                               .    .    .   .    .      .
Distance
  Example
  Find	the	point P on	the	parabola y = x2 closest	to	the	point (3, 0).


 Solution                                     y
                                              .




                                              . x, x2 )
                                              (
                                                              .
                                               .                            .       x
                                                                                    .
                                                                          3
                                                                          .
                                                  .       .       .   .     .   .
Distance
  Example
  Find	the	point P on	the	parabola y = x2 closest	to	the	point (3, 0).


 Solution                                     y
                                              .
 The	distance	between (x, x2 )
 and (3, 0) is	given	by
        √
 f(x) = (x − 3)2 + (x2 − 0)2




                                              . x, x2 )
                                              (
                                                              .
                                               .                            .       x
                                                                                    .
                                                                          3
                                                                          .
                                                  .       .       .   .     .   .
Distance
  Example
  Find	the	point P on	the	parabola y = x2 closest	to	the	point (3, 0).


 Solution                                     y
                                              .
 The	distance	between (x, x2 )
 and (3, 0) is	given	by
        √
 f(x) = (x − 3)2 + (x2 − 0)2

  We	may	instead	minimize
 the square of f:

  g(x) = f(x)2 = (x − 3)2 + x4                . x, x2 )
                                              (
                                                              .
                                               .                            .       x
                                                                                    .
                                                                          3
                                                                          .
                                                  .       .       .   .     .   .
Distance
  Example
  Find	the	point P on	the	parabola y = x2 closest	to	the	point (3, 0).


 Solution                                     y
                                              .
 The	distance	between (x, x2 )
 and (3, 0) is	given	by
        √
 f(x) = (x − 3)2 + (x2 − 0)2

  We	may	instead	minimize
 the square of f:

  g(x) = f(x)2 = (x − 3)2 + x4                . x, x2 )
                                              (
                                                              .
                                               .                            .       x
                                                                                    .
  The	domain	is (−∞, ∞).
                                                                          3
                                                                          .
                                                  .       .       .   .     .   .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .




                                              .    .   .   .      .   .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .
          g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3)




                                                .    .   .    .     .   .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .
          g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3)
          If	a	polynomial	has	integer	roots, they	are	factors	of	the
          constant	term	(Euler)




                                                   .    .   .    .     .   .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .
          g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3)
          If	a	polynomial	has	integer	roots, they	are	factors	of	the
          constant	term	(Euler)
          1 is	a	root, so 2x3 + x − 3 is	divisible	by x − 1:

                    f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3)

          The	quadratic	has	no	real	roots	(the	discriminant
          b2 − 4ac < 0)




                                                     .   .     .   .   .   .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .
          g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3)
          If	a	polynomial	has	integer	roots, they	are	factors	of	the
          constant	term	(Euler)
          1 is	a	root, so 2x3 + x − 3 is	divisible	by x − 1:

                    f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3)

          The	quadratic	has	no	real	roots	(the	discriminant
          b2 − 4ac < 0)
          We	see f′ (1) = 0, f′ (x) < 0 if x < 1, and f′ (x) > 0 if x > 1. So
          1 is	the	global	minimum.



                                                     .    .    .    .    .      .
Distance	problem
minimization	step

    We	want	to	find	the	global	minimum	of g(x) = (x − 3)2 + x4 .
          g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3)
          If	a	polynomial	has	integer	roots, they	are	factors	of	the
          constant	term	(Euler)
          1 is	a	root, so 2x3 + x − 3 is	divisible	by x − 1:

                    f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3)

          The	quadratic	has	no	real	roots	(the	discriminant
          b2 − 4ac < 0)
          We	see f′ (1) = 0, f′ (x) < 0 if x < 1, and f′ (x) > 0 if x > 1. So
          1 is	the	global	minimum.
          The	point	on	the	parabola	closest	to (3, 0) is (1, 1). The
                               √
          minimum	distance	is 5.

                                                     .    .    .    .    .      .
Remark




     We’ve	used	each	of	the	methods	(CIM,	1DT,	2DT) so	far.
     Notice	how	we	argued	that	the	critical	points	were	absolute
     extremes	even	though	1DT and	2DT only	tell	you
     relative/local	extremes.




                                           .    .   .    .    .    .
A problem	with	a	triangle
   Example
   Find	the	rectangle	of	maximal	area	inscribed	in	a	3-4-5	right
   triangle	as	shown.

  Solution




                                                         5
                                                         .
                                                                     4
                                                                     .



                                             .
                                                         3
                                                         .

                                                 .   .       .   .       .   .
A problem	with	a	triangle
   Example
   Find	the	rectangle	of	maximal	area	inscribed	in	a	3-4-5	right
   triangle	as	shown.

  Solution
      Let	the	dimensions	of
      the	rectangle	be x and y.


                                                         5
                                                         .       x
                                                                 .
                                                                         4
                                                                         .

                                                     y
                                                     .

                                             .
                                                         3
                                                         .

                                                 .   .       .       .       .   .
A problem	with	a	triangle
   Example
   Find	the	rectangle	of	maximal	area	inscribed	in	a	3-4-5	right
   triangle	as	shown.

  Solution
      Let	the	dimensions	of
      the	rectangle	be x and y.
      Similar	triangles	give

       y    4                                            5
                                                         .       x
                                                                 .
          =   =⇒ 3y = 4(3−x)                                             4
                                                                         .
      3−x   3
                                                     y
                                                     .

                                             .
                                                         3
                                                         .

                                                 .   .       .       .       .   .
A problem	with	a	triangle
   Example
   Find	the	rectangle	of	maximal	area	inscribed	in	a	3-4-5	right
   triangle	as	shown.

  Solution
      Let	the	dimensions	of
      the	rectangle	be x and y.
      Similar	triangles	give

       y    4                                            5
                                                         .       x
                                                                 .
          =   =⇒ 3y = 4(3−x)                                             4
                                                                         .
      3−x   3
                4                                    y
                                                     .
      So y = 4 −  x and
                3
              (       )                      .
                    4     4                              3
                                                         .
      A(x) = x 4 − x = 4x− x2
                    3     3
                                                 .   .       .       .       .   .
Triangle	Problem
maximization	step




                                                         4 2
    We	want	to	find	the	absolute	maximum	of A(x) = 4x −     x on
                                                         3
    the	interval [0, 3].




                                            .   .   .     .   .   .
Triangle	Problem
maximization	step




                                                         4 2
    We	want	to	find	the	absolute	maximum	of A(x) = 4x −     x on
                                                         3
    the	interval [0, 3].
          A(0) = A(3) = 0




                                            .   .   .     .   .   .
Triangle	Problem
maximization	step




                                                             4 2
    We	want	to	find	the	absolute	maximum	of A(x) = 4x −         x on
                                                             3
    the	interval [0, 3].
          A(0) = A(3) = 0
                      8                          12
          A′ (x) = 4 − x, which	is	zero	when x =    = 1.5.
                      3                           8




                                               .   .   .      .   .   .
Triangle	Problem
maximization	step




                                                               4 2
    We	want	to	find	the	absolute	maximum	of A(x) = 4x −           x on
                                                               3
    the	interval [0, 3].
          A(0) = A(3) = 0
                      8                            12
          A′ (x) = 4 − x, which	is	zero	when x =      = 1.5.
                      3                             8
          Since A(1.5) = 3, this	is	the	absolute	maximum.




                                                .    .   .      .   .   .
Triangle	Problem
maximization	step




                                                               4 2
    We	want	to	find	the	absolute	maximum	of A(x) = 4x −           x on
                                                               3
    the	interval [0, 3].
          A(0) = A(3) = 0
                      8                            12
          A′ (x) = 4 − x, which	is	zero	when x =      = 1.5.
                      3                             8
          Since A(1.5) = 3, this	is	the	absolute	maximum.
          So	the	dimensions	of	the	rectangle	of	maximal	area	are
          1.5 × 2.




                                                .    .   .      .   .   .
An	Economics	problem
  Example
  Let r be	the	monthly	rent	per	unit	in	an	apartment	building	with
  100 units. A survey	reveals	that	all	units	can	be	rented	when
  r = 900 and	that	one	unit	becomes	vacant	with	each 10 increase
  in	rent. Suppose	the	average	monthly	maintenance	costs	per
  occupied	unit	is	$100/month. What	rent	should	be	charged	to
  maximize	profit?




                                             .   .    .   .    .     .
An	Economics	problem
  Example
  Let r be	the	monthly	rent	per	unit	in	an	apartment	building	with
  100 units. A survey	reveals	that	all	units	can	be	rented	when
  r = 900 and	that	one	unit	becomes	vacant	with	each 10 increase
  in	rent. Suppose	the	average	monthly	maintenance	costs	per
  occupied	unit	is	$100/month. What	rent	should	be	charged	to
  maximize	profit?

  Solution
      Let n be	the	number	of	units	rented, depending	on	price	(the
      demand	function).
                                   ∆n        1
      We	have n(900) = 100 and         = − . So
                                   ∆r       10
                            1                      1
             n − 100 = −      (r − 900) =⇒ n(r) = − r + 190
                           10                      10
                                             .   .    .   .    .     .
Economics	Problem
Finishing	the	model	and	maximizing


          The	profit	per	unit	rented	is r − 100, so
                                                (          )
                                                   1
                P(r) = (r − 100)n(r) = (r − 100) − r + 190
                                                  10
                           1 2
                     = − r + 200r − 19000
                          10




                                                     .   .   .   .   .   .
Economics	Problem
Finishing	the	model	and	maximizing


          The	profit	per	unit	rented	is r − 100, so
                                                (          )
                                                   1
                P(r) = (r − 100)n(r) = (r − 100) − r + 190
                                                  10
                           1 2
                     = − r + 200r − 19000
                          10

          We	want	to	maximize P on	the	interval 900 ≤ r ≤ 1900.




                                                     .   .   .   .   .   .
Economics	Problem
Finishing	the	model	and	maximizing


          The	profit	per	unit	rented	is r − 100, so
                                                (          )
                                                   1
                P(r) = (r − 100)n(r) = (r − 100) − r + 190
                                                  10
                           1 2
                     = − r + 200r − 19000
                          10

          We	want	to	maximize P on	the	interval 900 ≤ r ≤ 1900.
          A(900) = $800 × 100 = $80, 000, A(1900) = 0




                                                     .   .   .   .   .   .
Economics	Problem
Finishing	the	model	and	maximizing


          The	profit	per	unit	rented	is r − 100, so
                                                (          )
                                                   1
                P(r) = (r − 100)n(r) = (r − 100) − r + 190
                                                  10
                           1 2
                     = − r + 200r − 19000
                          10

          We	want	to	maximize P on	the	interval 900 ≤ r ≤ 1900.
          A(900) = $800 × 100 = $80, 000, A(1900) = 0
                    1
          A′ (x) = − r + 200, which	is	zero	when r = 1000.
                    5




                                                     .   .   .   .   .   .
Economics	Problem
Finishing	the	model	and	maximizing


          The	profit	per	unit	rented	is r − 100, so
                                                (          )
                                                   1
                P(r) = (r − 100)n(r) = (r − 100) − r + 190
                                                  10
                           1 2
                     = − r + 200r − 19000
                          10

          We	want	to	maximize P on	the	interval 900 ≤ r ≤ 1900.
          A(900) = $800 × 100 = $80, 000, A(1900) = 0
                    1
          A′ (x) = − r + 200, which	is	zero	when r = 1000.
                    5
          n(1000) = 90, so P(r) = $900 × 90 = $81, 000. This	is	the
          maximum	intake.

                                                     .   .   .   .   .   .
The	Statue	of	Liberty
   The	Statue	of	Liberty	stands	on	top	of	a	pedestal	which	is	on	top
   of	on	old	fort. The	top	of	the	pedestal	is	47 m	above	ground	level.
   The	statue	itself	measures	46 m	from	the	top	of	the	pedestal	to	the
   tip	of	the	torch.




   What	distance	should	one	stand	away	from	the	statue	in	order	to
   maximize	the	view	of	the	statue? That	is, what	distance	will
   maximize	the	portion	of	the	viewer’s	vision	taken	up	by	the
   statue?
                                                .   .    .    .   .      .
The	Statue	of	Liberty
Seting	up	the	model




   The	angle	subtended	by	the
   statue	in	the	viewer’s	eye	can                                         a
   be	expressed	as
               (      )         ( )                                       b
                 a+b              b                   θ
   θ = arctan           −arctan     .
                   x              x
                                                          x
    The	domain	of θ is	all	positive	real	numbers x.




                                                 .    .       .   .   .       .
The	Statue	of	Liberty
Finding	the	derivative


                                          (          )            ( )
                                               a+b                 b
                         θ = arctan                      − arctan
                                                x                  x
     So
                dθ             1               −(a + b)            1     −b
                   =          (         )2 ·            −          ( )2 · 2
                dx                a+b             x2                 b    x
                         1+        x                         1+     x
                              b       a+b
                    =             2
                                      −
                         x2 +b            x2
                                     + (a + b ) 2
                         [ 2          ]           [        ]
                          x + (a + b)2 b − (a + b) x2 + b2
                    =
                                  (x2 + b2 ) [x2 + (a + b)2 ]



                                                               .        .   .   .   .   .
The	Statue	of	Liberty
Finding	the	critical	points


                              [                ]           [        ]
                   dθ             x2 + (a + b)2 b − (a + b) x2 + b2
                      =
                   dx                  (x2 + b2 ) [x2 + (a + b)2 ]




                                                             .       .   .   .   .   .
The	Statue	of	Liberty
Finding	the	critical	points


                              [                ]           [        ]
                   dθ             x2 + (a + b)2 b − (a + b) x2 + b2
                      =
                   dx                  (x2 + b2 ) [x2 + (a + b)2 ]

           This	derivative	is	zero	if	and	only	if	the	numerator	is	zero, so
           we	seek x such	that
                [              ]             [         ]
           0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 )




                                                             .       .   .   .   .   .
The	Statue	of	Liberty
Finding	the	critical	points


                              [                ]           [        ]
                   dθ             x2 + (a + b)2 b − (a + b) x2 + b2
                      =
                   dx                  (x2 + b2 ) [x2 + (a + b)2 ]

           This	derivative	is	zero	if	and	only	if	the	numerator	is	zero, so
           we	seek x such	that
                [              ]             [         ]
           0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 )

                                                      √
           The	only	positive	solution	is x =              b(a + b).




                                                              .      .   .   .   .   .
The	Statue	of	Liberty
Finding	the	critical	points


                              [                ]           [        ]
                   dθ             x2 + (a + b)2 b − (a + b) x2 + b2
                      =
                   dx                  (x2 + b2 ) [x2 + (a + b)2 ]

           This	derivative	is	zero	if	and	only	if	the	numerator	is	zero, so
           we	seek x such	that
                [              ]             [         ]
           0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 )

                                                      √
           The	only	positive	solution	is x =              b(a + b).
           Using	the	first	derivative	test, we	see	that dθ/dx > 0 if
                   √                                   √
           0 < x < b(a + b) and dθ/dx < 0 if x > b(a + b).
           So	this	is	definitely	the	absolute	maximum	on (0, ∞).

                                                              .      .   .   .   .   .
The	Statue	of	Liberty
Final	answer


    If	we	substitute	in	the	numerical	dimensions	given, we	have
                            √
                       x = (46)(93) ≈ 66.1 meters

    This	distance	would	put	you	pretty	close	to	the	front	of	the	old
    fort	which	lies	at	the	base	of	the	island.




    Unfortunately, you’re	not	allowed	to	walk	on	this	part	of	the	lawn.

                                                 .   .    .    .   .      .
The	Statue	of	Liberty
Discussion




                     √
         The	length b(a + b) is	the geometric	mean of	the	two
         distances	measured	from	the	ground—to	the	top	of	the
         pedestal	(a)	and	the	top	of	the	statue	(a + b).
         The	geometric	mean	is	of	two	numbers	is	always	between
         them	and	greater	than	or	equal	to	their	average.




                                             .    .   .   .     .   .

Más contenido relacionado

La actualidad más candente

Lesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesLesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesMatthew Leingang
 
Expresiones Algebraicas, Factorización y Radicación.
Expresiones Algebraicas, Factorización y Radicación.Expresiones Algebraicas, Factorización y Radicación.
Expresiones Algebraicas, Factorización y Radicación.AnmyAbileneSiviraMic
 
Difrentiation
DifrentiationDifrentiation
Difrentiationlecturer
 
Lesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesLesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesMatthew Leingang
 
Lesson 1: Functions and their Representations
Lesson 1: Functions and their RepresentationsLesson 1: Functions and their Representations
Lesson 1: Functions and their RepresentationsMatthew Leingang
 
Lesson03 The Concept Of Limit 027 Slides
Lesson03   The Concept Of Limit 027 SlidesLesson03   The Concept Of Limit 027 Slides
Lesson03 The Concept Of Limit 027 SlidesMatthew Leingang
 
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)Matthew Leingang
 
11X1 T13 01 polynomial definitions
11X1 T13 01 polynomial definitions11X1 T13 01 polynomial definitions
11X1 T13 01 polynomial definitionsNigel Simmons
 
Integral (area)
Integral (area)Integral (area)
Integral (area)Asef Thea
 
Actividad 4 calculo diferencial
Actividad 4 calculo diferencialActividad 4 calculo diferencial
Actividad 4 calculo diferencialSIGIFREDO12222
 
Computer graphics question for exam solved
Computer graphics question for exam solvedComputer graphics question for exam solved
Computer graphics question for exam solvedKuntal Bhowmick
 

La actualidad más candente (18)

Lesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesLesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of Curves
 
Expresiones Algebraicas, Factorización y Radicación.
Expresiones Algebraicas, Factorización y Radicación.Expresiones Algebraicas, Factorización y Radicación.
Expresiones Algebraicas, Factorización y Radicación.
 
Difrentiation
DifrentiationDifrentiation
Difrentiation
 
M8AL- IIf-2
M8AL- IIf-2M8AL- IIf-2
M8AL- IIf-2
 
Lesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of CurvesLesson 21: Derivatives and the Shapes of Curves
Lesson 21: Derivatives and the Shapes of Curves
 
M8 al if-1
M8 al if-1M8 al if-1
M8 al if-1
 
Lesson 1: Functions and their Representations
Lesson 1: Functions and their RepresentationsLesson 1: Functions and their Representations
Lesson 1: Functions and their Representations
 
Lesson03 The Concept Of Limit 027 Slides
Lesson03   The Concept Of Limit 027 SlidesLesson03   The Concept Of Limit 027 Slides
Lesson03 The Concept Of Limit 027 Slides
 
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)
 
Ch 2
Ch 2Ch 2
Ch 2
 
11X1 T13 01 polynomial definitions
11X1 T13 01 polynomial definitions11X1 T13 01 polynomial definitions
11X1 T13 01 polynomial definitions
 
Integral (area)
Integral (area)Integral (area)
Integral (area)
 
Lecture 1
Lecture 1Lecture 1
Lecture 1
 
Actividad 4 calculo diferencial
Actividad 4 calculo diferencialActividad 4 calculo diferencial
Actividad 4 calculo diferencial
 
Computer graphics question for exam solved
Computer graphics question for exam solvedComputer graphics question for exam solved
Computer graphics question for exam solved
 
Lecture 3
Lecture 3Lecture 3
Lecture 3
 
Day 01
Day 01Day 01
Day 01
 
Math 21a Midterm I Review
Math 21a Midterm I ReviewMath 21a Midterm I Review
Math 21a Midterm I Review
 

Destacado

Lesson 29: Integration by Substition (worksheet solutions)
Lesson 29: Integration by Substition (worksheet solutions)Lesson 29: Integration by Substition (worksheet solutions)
Lesson 29: Integration by Substition (worksheet solutions)Matthew Leingang
 
Lesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremLesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremMatthew Leingang
 
Lesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsLesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsMatthew Leingang
 
Lesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusLesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusMatthew Leingang
 
Lesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusLesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusMatthew Leingang
 
Lesson 29: Integration by Substition
Lesson 29: Integration by SubstitionLesson 29: Integration by Substition
Lesson 29: Integration by SubstitionMatthew Leingang
 
Lesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralLesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralMatthew Leingang
 
Lesson 23: Antiderivatives
Lesson 23: AntiderivativesLesson 23: Antiderivatives
Lesson 23: AntiderivativesMatthew Leingang
 
Lesson19 Maximum And Minimum Values 034 Slides
Lesson19   Maximum And Minimum Values 034 SlidesLesson19   Maximum And Minimum Values 034 Slides
Lesson19 Maximum And Minimum Values 034 SlidesMatthew Leingang
 
A Multiformat Document Workflow With Docutils
A Multiformat Document Workflow With DocutilsA Multiformat Document Workflow With Docutils
A Multiformat Document Workflow With DocutilsMatthew Leingang
 
Lesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsLesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsMatthew Leingang
 
Lesson 23: Antiderivatives
Lesson 23: AntiderivativesLesson 23: Antiderivatives
Lesson 23: AntiderivativesMatthew Leingang
 
Lesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremLesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremMatthew Leingang
 
Lesson 3: The Concept of Limit
Lesson 3: The Concept of LimitLesson 3: The Concept of Limit
Lesson 3: The Concept of LimitMatthew Leingang
 
Lesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralLesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralMatthew Leingang
 
F and G Taylor Series Solutions to the Circular Restricted Three-Body Problem
F and G Taylor Series Solutions to the Circular Restricted Three-Body ProblemF and G Taylor Series Solutions to the Circular Restricted Three-Body Problem
F and G Taylor Series Solutions to the Circular Restricted Three-Body ProblemEtienne Pellegrini
 

Destacado (18)

Lesson 29: Integration by Substition (worksheet solutions)
Lesson 29: Integration by Substition (worksheet solutions)Lesson 29: Integration by Substition (worksheet solutions)
Lesson 29: Integration by Substition (worksheet solutions)
 
Lesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremLesson 20: The Mean Value Theorem
Lesson 20: The Mean Value Theorem
 
Lesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsLesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite Integrals
 
Lesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusLesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of Calculus
 
Lesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of CalculusLesson 28: The Fundamental Theorem of Calculus
Lesson 28: The Fundamental Theorem of Calculus
 
Lesson 29: Integration by Substition
Lesson 29: Integration by SubstitionLesson 29: Integration by Substition
Lesson 29: Integration by Substition
 
Lesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralLesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite Integral
 
Lesson 23: Antiderivatives
Lesson 23: AntiderivativesLesson 23: Antiderivatives
Lesson 23: Antiderivatives
 
Lesson 24: Optimization
Lesson 24: OptimizationLesson 24: Optimization
Lesson 24: Optimization
 
Lesson19 Maximum And Minimum Values 034 Slides
Lesson19   Maximum And Minimum Values 034 SlidesLesson19   Maximum And Minimum Values 034 Slides
Lesson19 Maximum And Minimum Values 034 Slides
 
A Multiformat Document Workflow With Docutils
A Multiformat Document Workflow With DocutilsA Multiformat Document Workflow With Docutils
A Multiformat Document Workflow With Docutils
 
Lesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite IntegralsLesson 27: Evaluating Definite Integrals
Lesson 27: Evaluating Definite Integrals
 
Lesson 23: Antiderivatives
Lesson 23: AntiderivativesLesson 23: Antiderivatives
Lesson 23: Antiderivatives
 
Lesson 20: The Mean Value Theorem
Lesson 20: The Mean Value TheoremLesson 20: The Mean Value Theorem
Lesson 20: The Mean Value Theorem
 
Lesson 24: Optimization
Lesson 24: OptimizationLesson 24: Optimization
Lesson 24: Optimization
 
Lesson 3: The Concept of Limit
Lesson 3: The Concept of LimitLesson 3: The Concept of Limit
Lesson 3: The Concept of Limit
 
Lesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite IntegralLesson 25: Areas and Distances; The Definite Integral
Lesson 25: Areas and Distances; The Definite Integral
 
F and G Taylor Series Solutions to the Circular Restricted Three-Body Problem
F and G Taylor Series Solutions to the Circular Restricted Three-Body ProblemF and G Taylor Series Solutions to the Circular Restricted Three-Body Problem
F and G Taylor Series Solutions to the Circular Restricted Three-Body Problem
 

Similar a Lesson 24: Optimization II

Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesMatthew Leingang
 
Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesMatthew Leingang
 
Lesson 19: Maximum and Minimum Values
Lesson 19: Maximum and Minimum ValuesLesson 19: Maximum and Minimum Values
Lesson 19: Maximum and Minimum ValuesMatthew Leingang
 
maxima & Minima thoeyr&solved.Module-4pdf
maxima & Minima thoeyr&solved.Module-4pdfmaxima & Minima thoeyr&solved.Module-4pdf
maxima & Minima thoeyr&solved.Module-4pdfRajuSingh806014
 
Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesMatthew Leingang
 
C2 differentiation jan 22
C2 differentiation jan 22C2 differentiation jan 22
C2 differentiation jan 22Mohammed Ahmed
 
2 1 polynomials
2 1 polynomials2 1 polynomials
2 1 polynomialshisema01
 
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 MultipliersMatthew Leingang
 
Derivative free optimization
Derivative free optimizationDerivative free optimization
Derivative free optimizationhelalmohammad2
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minimarouwejan
 
Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Glicerio Gavilan
 
Interpolation techniques - Background and implementation
Interpolation techniques - Background and implementationInterpolation techniques - Background and implementation
Interpolation techniques - Background and implementationQuasar Chunawala
 
Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Matthew Leingang
 
Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Mel Anthony Pepito
 

Similar a Lesson 24: Optimization II (20)

Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation Rules
 
Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation Rules
 
Lesson 19: Maximum and Minimum Values
Lesson 19: Maximum and Minimum ValuesLesson 19: Maximum and Minimum Values
Lesson 19: Maximum and Minimum Values
 
maxima & Minima thoeyr&solved.Module-4pdf
maxima & Minima thoeyr&solved.Module-4pdfmaxima & Minima thoeyr&solved.Module-4pdf
maxima & Minima thoeyr&solved.Module-4pdf
 
When youseeab
When youseeabWhen youseeab
When youseeab
 
Lesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation RulesLesson 8: Basic Differentiation Rules
Lesson 8: Basic Differentiation Rules
 
Differentiation.pptx
Differentiation.pptxDifferentiation.pptx
Differentiation.pptx
 
C2 differentiation jan 22
C2 differentiation jan 22C2 differentiation jan 22
C2 differentiation jan 22
 
Limits
LimitsLimits
Limits
 
2 1 polynomials
2 1 polynomials2 1 polynomials
2 1 polynomials
 
Functions
FunctionsFunctions
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
 
Derivative free optimization
Derivative free optimizationDerivative free optimization
Derivative free optimization
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minima
 
2 5 zeros of poly fn
2 5 zeros of poly fn2 5 zeros of poly fn
2 5 zeros of poly fn
 
Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721Mac2311 study guide-tcm6-49721
Mac2311 study guide-tcm6-49721
 
Interpolation techniques - Background and implementation
Interpolation techniques - Background and implementationInterpolation techniques - Background and implementation
Interpolation techniques - Background and implementation
 
Polynomials
PolynomialsPolynomials
Polynomials
 
Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)
 
Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)Lesson 8: Basic Differentation Rules (slides)
Lesson 8: Basic Differentation Rules (slides)
 

Más de Matthew Leingang

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-choiceMatthew Leingang
 
Electronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsElectronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsMatthew Leingang
 
Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 
Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Matthew Leingang
 
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
 
Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Matthew Leingang
 
Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Matthew Leingang
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Matthew Leingang
 
Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 

Más de Matthew Leingang (20)

Making Lesson Plans
Making Lesson PlansMaking Lesson Plans
Making Lesson Plans
 
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 (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)
 
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)
 

Último

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 textsMaria Levchenko
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
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 2024Rafal Los
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
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 AutomationSafe Software
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 

Último (20)

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
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
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
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
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
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 

Lesson 24: Optimization II

  • 1. Section 4.5 Optimization II V63.0121.034, Calculus I November 25, 2009 Announcements Final Exam, December 18, 2:00–3:50pm . . . . . .
  • 2. Outline Recall More examples Addition Distance Triangles Economics The Statue of Liberty . . . . . .
  • 3. Checklist for optimization problems 1. Understand the Problem What is known? What is unknown? What are the conditions? 2. Draw a diagram 3. Introduce Notation 4. Express the “objective function” Q in terms of the other symbols 5. If Q is a function of more than one “decision variable”, use the given information to eliminate all but one of them. 6. Find the absolute maximum (or minimum, depending on the problem) of the function on its domain. . . . . . .
  • 4. Recall: The Closed Interval Method See Section 4.1 To find the extreme values of a function f on [a, b], we need to: Evaluate f at the endpoints a and b Evaluate f at the critical points x where either f′ (x) = 0 or f is not differentiable at x. The points with the largest function value are the global maximum points The points with the smallest or most negative function value are the global minimum points. . . . . . .
  • 5. Recall: The First Derivative Test See Section 4.3 Theorem (The First Derivative Test) Let f be a continuous function and c a critical point of f in (a, b). If f′ (x) > 0 on (a, c) (i.e., “before c”) and f′ (x) < 0 on (c, b) (i.e., “after c”, then c is a local maximum for f. If f′ (x) < 0 on (a, c) and f′ (x) > 0 on (c, b), then c is a local minimum for f. If f′ (x) has the same sign on (a, c) and (c, b), then c is not a local extremum. . . . . . .
  • 6. Recall: The Second Derivative Test See Section 4.3 Theorem (The Second Derivative Test) Let f, f′ , and f′′ be continuous. Let c be be a point in (a, b) with f′ (c) = 0. If f′′ (c) < 0, then f(c) is a local maximum. If f′′ (c) > 0, then f(c) is a local minimum. If f′′ (c) = 0, the second derivative test is inconclusive (this does not mean c is neither; we just don’t know yet). . . . . . .
  • 7. Which to use when? The bottom line Use CIM if it applies: the domain is a closed, bounded interval If domain is not closed or not bounded, use 2DT if you like to take derivatives, or 1DT if you like to compare signs. . . . . . .
  • 8. Outline Recall More examples Addition Distance Triangles Economics The Statue of Liberty . . . . . .
  • 9. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. . . . . . .
  • 10. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. Solution Objective: minimize S = x + y subject to the constraint that xy = 16 . . . . . .
  • 11. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. Solution Objective: minimize S = x + y subject to the constraint that xy = 16 Eliminate y: y = 16/x so S = x + 16/x. The domain of consideration is (0, ∞). . . . . . .
  • 12. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. Solution Objective: minimize S = x + y subject to the constraint that xy = 16 Eliminate y: y = 16/x so S = x + 16/x. The domain of consideration is (0, ∞). Find the critical points: S′ (x) = 1 − 16/x2 , which is 0 when x = 4. . . . . . .
  • 13. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. Solution Objective: minimize S = x + y subject to the constraint that xy = 16 Eliminate y: y = 16/x so S = x + 16/x. The domain of consideration is (0, ∞). Find the critical points: S′ (x) = 1 − 16/x2 , which is 0 when x = 4. Classify the critical points: S′′ (x) = 32/x3 , which is always positive. So the graph is always concave up, 4 is a local min, and therefore the global min. . . . . . .
  • 14. Addition with a constraint Example Find two positive numbers x and y with xy = 16 and x + y as small as possible. Solution Objective: minimize S = x + y subject to the constraint that xy = 16 Eliminate y: y = 16/x so S = x + 16/x. The domain of consideration is (0, ∞). Find the critical points: S′ (x) = 1 − 16/x2 , which is 0 when x = 4. Classify the critical points: S′′ (x) = 32/x3 , which is always positive. So the graph is always concave up, 4 is a local min, and therefore the global min. So the numbers are x = y = 4, Smin = 8. . . . . . .
  • 15. Distance Example Find the point P on the parabola y = x2 closest to the point (3, 0). . . . . . .
  • 16. Distance Example Find the point P on the parabola y = x2 closest to the point (3, 0). Solution y . . x, x2 ) ( . . . x . 3 . . . . . . .
  • 17. Distance Example Find the point P on the parabola y = x2 closest to the point (3, 0). Solution y . The distance between (x, x2 ) and (3, 0) is given by √ f(x) = (x − 3)2 + (x2 − 0)2 . x, x2 ) ( . . . x . 3 . . . . . . .
  • 18. Distance Example Find the point P on the parabola y = x2 closest to the point (3, 0). Solution y . The distance between (x, x2 ) and (3, 0) is given by √ f(x) = (x − 3)2 + (x2 − 0)2 We may instead minimize the square of f: g(x) = f(x)2 = (x − 3)2 + x4 . x, x2 ) ( . . . x . 3 . . . . . . .
  • 19. Distance Example Find the point P on the parabola y = x2 closest to the point (3, 0). Solution y . The distance between (x, x2 ) and (3, 0) is given by √ f(x) = (x − 3)2 + (x2 − 0)2 We may instead minimize the square of f: g(x) = f(x)2 = (x − 3)2 + x4 . x, x2 ) ( . . . x . The domain is (−∞, ∞). 3 . . . . . . .
  • 20. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . . . . . . .
  • 21. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3) . . . . . .
  • 22. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3) If a polynomial has integer roots, they are factors of the constant term (Euler) . . . . . .
  • 23. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3) If a polynomial has integer roots, they are factors of the constant term (Euler) 1 is a root, so 2x3 + x − 3 is divisible by x − 1: f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3) The quadratic has no real roots (the discriminant b2 − 4ac < 0) . . . . . .
  • 24. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3) If a polynomial has integer roots, they are factors of the constant term (Euler) 1 is a root, so 2x3 + x − 3 is divisible by x − 1: f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3) The quadratic has no real roots (the discriminant b2 − 4ac < 0) We see f′ (1) = 0, f′ (x) < 0 if x < 1, and f′ (x) > 0 if x > 1. So 1 is the global minimum. . . . . . .
  • 25. Distance problem minimization step We want to find the global minimum of g(x) = (x − 3)2 + x4 . g′ (x) = 2(x − 3) + 4x3 = 4x3 + 2x − 6 = 2(2x3 + x − 3) If a polynomial has integer roots, they are factors of the constant term (Euler) 1 is a root, so 2x3 + x − 3 is divisible by x − 1: f′ (x) = 2(2x3 + x − 3) = 2(x − 1)(2x2 + 2x + 3) The quadratic has no real roots (the discriminant b2 − 4ac < 0) We see f′ (1) = 0, f′ (x) < 0 if x < 1, and f′ (x) > 0 if x > 1. So 1 is the global minimum. The point on the parabola closest to (3, 0) is (1, 1). The √ minimum distance is 5. . . . . . .
  • 26. Remark We’ve used each of the methods (CIM, 1DT, 2DT) so far. Notice how we argued that the critical points were absolute extremes even though 1DT and 2DT only tell you relative/local extremes. . . . . . .
  • 27. A problem with a triangle Example Find the rectangle of maximal area inscribed in a 3-4-5 right triangle as shown. Solution 5 . 4 . . 3 . . . . . . .
  • 28. A problem with a triangle Example Find the rectangle of maximal area inscribed in a 3-4-5 right triangle as shown. Solution Let the dimensions of the rectangle be x and y. 5 . x . 4 . y . . 3 . . . . . . .
  • 29. A problem with a triangle Example Find the rectangle of maximal area inscribed in a 3-4-5 right triangle as shown. Solution Let the dimensions of the rectangle be x and y. Similar triangles give y 4 5 . x . = =⇒ 3y = 4(3−x) 4 . 3−x 3 y . . 3 . . . . . . .
  • 30. A problem with a triangle Example Find the rectangle of maximal area inscribed in a 3-4-5 right triangle as shown. Solution Let the dimensions of the rectangle be x and y. Similar triangles give y 4 5 . x . = =⇒ 3y = 4(3−x) 4 . 3−x 3 4 y . So y = 4 − x and 3 ( ) . 4 4 3 . A(x) = x 4 − x = 4x− x2 3 3 . . . . . .
  • 31. Triangle Problem maximization step 4 2 We want to find the absolute maximum of A(x) = 4x − x on 3 the interval [0, 3]. . . . . . .
  • 32. Triangle Problem maximization step 4 2 We want to find the absolute maximum of A(x) = 4x − x on 3 the interval [0, 3]. A(0) = A(3) = 0 . . . . . .
  • 33. Triangle Problem maximization step 4 2 We want to find the absolute maximum of A(x) = 4x − x on 3 the interval [0, 3]. A(0) = A(3) = 0 8 12 A′ (x) = 4 − x, which is zero when x = = 1.5. 3 8 . . . . . .
  • 34. Triangle Problem maximization step 4 2 We want to find the absolute maximum of A(x) = 4x − x on 3 the interval [0, 3]. A(0) = A(3) = 0 8 12 A′ (x) = 4 − x, which is zero when x = = 1.5. 3 8 Since A(1.5) = 3, this is the absolute maximum. . . . . . .
  • 35. Triangle Problem maximization step 4 2 We want to find the absolute maximum of A(x) = 4x − x on 3 the interval [0, 3]. A(0) = A(3) = 0 8 12 A′ (x) = 4 − x, which is zero when x = = 1.5. 3 8 Since A(1.5) = 3, this is the absolute maximum. So the dimensions of the rectangle of maximal area are 1.5 × 2. . . . . . .
  • 36. An Economics problem Example Let r be the monthly rent per unit in an apartment building with 100 units. A survey reveals that all units can be rented when r = 900 and that one unit becomes vacant with each 10 increase in rent. Suppose the average monthly maintenance costs per occupied unit is $100/month. What rent should be charged to maximize profit? . . . . . .
  • 37. An Economics problem Example Let r be the monthly rent per unit in an apartment building with 100 units. A survey reveals that all units can be rented when r = 900 and that one unit becomes vacant with each 10 increase in rent. Suppose the average monthly maintenance costs per occupied unit is $100/month. What rent should be charged to maximize profit? Solution Let n be the number of units rented, depending on price (the demand function). ∆n 1 We have n(900) = 100 and = − . So ∆r 10 1 1 n − 100 = − (r − 900) =⇒ n(r) = − r + 190 10 10 . . . . . .
  • 38. Economics Problem Finishing the model and maximizing The profit per unit rented is r − 100, so ( ) 1 P(r) = (r − 100)n(r) = (r − 100) − r + 190 10 1 2 = − r + 200r − 19000 10 . . . . . .
  • 39. Economics Problem Finishing the model and maximizing The profit per unit rented is r − 100, so ( ) 1 P(r) = (r − 100)n(r) = (r − 100) − r + 190 10 1 2 = − r + 200r − 19000 10 We want to maximize P on the interval 900 ≤ r ≤ 1900. . . . . . .
  • 40. Economics Problem Finishing the model and maximizing The profit per unit rented is r − 100, so ( ) 1 P(r) = (r − 100)n(r) = (r − 100) − r + 190 10 1 2 = − r + 200r − 19000 10 We want to maximize P on the interval 900 ≤ r ≤ 1900. A(900) = $800 × 100 = $80, 000, A(1900) = 0 . . . . . .
  • 41. Economics Problem Finishing the model and maximizing The profit per unit rented is r − 100, so ( ) 1 P(r) = (r − 100)n(r) = (r − 100) − r + 190 10 1 2 = − r + 200r − 19000 10 We want to maximize P on the interval 900 ≤ r ≤ 1900. A(900) = $800 × 100 = $80, 000, A(1900) = 0 1 A′ (x) = − r + 200, which is zero when r = 1000. 5 . . . . . .
  • 42. Economics Problem Finishing the model and maximizing The profit per unit rented is r − 100, so ( ) 1 P(r) = (r − 100)n(r) = (r − 100) − r + 190 10 1 2 = − r + 200r − 19000 10 We want to maximize P on the interval 900 ≤ r ≤ 1900. A(900) = $800 × 100 = $80, 000, A(1900) = 0 1 A′ (x) = − r + 200, which is zero when r = 1000. 5 n(1000) = 90, so P(r) = $900 × 90 = $81, 000. This is the maximum intake. . . . . . .
  • 43. The Statue of Liberty The Statue of Liberty stands on top of a pedestal which is on top of on old fort. The top of the pedestal is 47 m above ground level. The statue itself measures 46 m from the top of the pedestal to the tip of the torch. What distance should one stand away from the statue in order to maximize the view of the statue? That is, what distance will maximize the portion of the viewer’s vision taken up by the statue? . . . . . .
  • 44. The Statue of Liberty Seting up the model The angle subtended by the statue in the viewer’s eye can a be expressed as ( ) ( ) b a+b b θ θ = arctan −arctan . x x x The domain of θ is all positive real numbers x. . . . . . .
  • 45. The Statue of Liberty Finding the derivative ( ) ( ) a+b b θ = arctan − arctan x x So dθ 1 −(a + b) 1 −b = ( )2 · − ( )2 · 2 dx a+b x2 b x 1+ x 1+ x b a+b = 2 − x2 +b x2 + (a + b ) 2 [ 2 ] [ ] x + (a + b)2 b − (a + b) x2 + b2 = (x2 + b2 ) [x2 + (a + b)2 ] . . . . . .
  • 46. The Statue of Liberty Finding the critical points [ ] [ ] dθ x2 + (a + b)2 b − (a + b) x2 + b2 = dx (x2 + b2 ) [x2 + (a + b)2 ] . . . . . .
  • 47. The Statue of Liberty Finding the critical points [ ] [ ] dθ x2 + (a + b)2 b − (a + b) x2 + b2 = dx (x2 + b2 ) [x2 + (a + b)2 ] This derivative is zero if and only if the numerator is zero, so we seek x such that [ ] [ ] 0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 ) . . . . . .
  • 48. The Statue of Liberty Finding the critical points [ ] [ ] dθ x2 + (a + b)2 b − (a + b) x2 + b2 = dx (x2 + b2 ) [x2 + (a + b)2 ] This derivative is zero if and only if the numerator is zero, so we seek x such that [ ] [ ] 0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 ) √ The only positive solution is x = b(a + b). . . . . . .
  • 49. The Statue of Liberty Finding the critical points [ ] [ ] dθ x2 + (a + b)2 b − (a + b) x2 + b2 = dx (x2 + b2 ) [x2 + (a + b)2 ] This derivative is zero if and only if the numerator is zero, so we seek x such that [ ] [ ] 0 = x2 + (a + b)2 b − (a + b) x2 + b2 = a(ab + b2 − x2 ) √ The only positive solution is x = b(a + b). Using the first derivative test, we see that dθ/dx > 0 if √ √ 0 < x < b(a + b) and dθ/dx < 0 if x > b(a + b). So this is definitely the absolute maximum on (0, ∞). . . . . . .
  • 50. The Statue of Liberty Final answer If we substitute in the numerical dimensions given, we have √ x = (46)(93) ≈ 66.1 meters This distance would put you pretty close to the front of the old fort which lies at the base of the island. Unfortunately, you’re not allowed to walk on this part of the lawn. . . . . . .
  • 51. The Statue of Liberty Discussion √ The length b(a + b) is the geometric mean of the two distances measured from the ground—to the top of the pedestal (a) and the top of the statue (a + b). The geometric mean is of two numbers is always between them and greater than or equal to their average. . . . . . .