Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few more good examples.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few more good examples.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few more good examples.
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Optimization: from mathematical tools to real applicationsPhilippe Laborie
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Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
Optimization: from mathematical tools to real applicationsPhilippe Laborie
The existence of powerful mathematical optimization engines is a necessary but not a sufficient condition for the pervasion of optimization technologies in the real world. This seminar, presented in 2013, explores some of the challenges related with the development of optimization applications as well as some general guidelines to avoid common pitfalls. It is illustrated with IBM ILOG optimization technologies and solutions.
Accelerating the Development of Efficient CP Optimizer ModelsPhilippe Laborie
The IBM Constraint Programming optimization system CP Optimizer was designed to provide automatic search and a simple modeling of discrete optimization problems, with a particular focus on scheduling applications. It is used in industry for solving operational planning and scheduling problems. We will give an overview of CP Optimizer and then describe in further detail a set of features such as input/output file format, warm-start or conflict refinement that help accelerate the development of efficient models.
IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three ProblemsPhilippe Laborie
Since version 2.0, IBM ILOG CP Optimizer provides a new
scheduling language supported by a robust and efficient automatic search. This presentation illustrates both the expressivity of the modelling language and the robustness of the automatic search on three problems recently studied in the scheduling literature. We show that all three problems
can easily be modelled with CP Optimizer in only a few dozen lines (the complete models are provided) and that on average the automatic search outperforms existing problem specific approaches.
This slide deck was presented at CP-AI-OR 2009 conference. Complete reference:
Philippe Laborie. "IBM ILOG CP Optimizer for Detailed Scheduling Illustrated on Three Problems". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR 2009). Lecture Notes in Computer Science. Volume 5547, 2009, pp 148-162.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few good examples.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few good examples.
This presentation introduces CP Optimizer a model & run optimization engine for solving discrete combinatorial problems with a particular focus on scheduling problems.
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Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few more good examples.
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The Mean Value Theorem is the most important theorem in calculus. It is the first theorem which allows us to infer information about a function from information about its derivative. From the MVT we can derive tests for the monotonicity (increase or decrease) and concavity of a function.
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Lesson 22: Optimization II (Section 041 slides)
1. Section 4.5
Optimization II
V63.0121.041, Calculus I
New York University
November 24, 2010
Announcements
No recitation this week
Quiz 5 on §§4.1–4.4 next week in recitation
Happy Thanksgiving!
. . . . . .
2. . . . . . .
Announcements
No recitation this week
Quiz 5 on §§4.1–4.4 next
week in recitation
Happy Thanksgiving!
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 2 / 25
3. . . . . . .
Objectives
Given a problem requiring
optimization, identify the
objective functions,
variables, and constraints.
Solve optimization
problems with calculus.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 3 / 25
4. . . . . . .
Outline
Recall
More examples
Addition
Distance
Triangles
Economics
The Statue of Liberty
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 4 / 25
5. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 5 / 25
6. . . . . . .
Recall: The Closed Interval Method
See Section 4.1
The Closed Interval Method
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/most negative function value are the
global minimum points.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 6 / 25
7. . . . . . .
Recall: The First Derivative Test
See Section 4.3
Theorem (The First Derivative Test)
Let f be continuous on (a, b) and c a critical point of f in (a, b).
If f′
changes from negative to positive at c, then c is a local
minimum.
If f′
changes from positive to negative at c, then c is a local
maximum.
If f′
does not change sign at c, then c is not a local extremum.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 7 / 25
8. . . . . . .
Recall: The First Derivative Test
See Section 4.3
Theorem (The First Derivative Test)
Let f be continuous on (a, b) and c a critical point of f in (a, b).
If f′
changes from negative to positive at c, then c is a local
minimum.
If f′
changes from positive to negative at c, then c is a local
maximum.
If f′
does not change sign at c, then c is not a local extremum.
Corollary
If f′
< 0 for all x < c and f′
(x) > 0 for all x > c, then c is the global
minimum of f on (a, b).
If f′
< 0 for all x > c and f′
(x) > 0 for all x < c, then c is the global
maximum of f on (a, b).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 7 / 25
9. . . . . . .
Recall: The Second Derivative Test
See Section 4.3
Theorem (The Second Derivative Test)
Let f, f′
, and f′′
be continuous on [a, b]. Let c be 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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 8 / 25
10. . . . . . .
Recall: The Second Derivative Test
See Section 4.3
Theorem (The Second Derivative Test)
Let f, f′
, and f′′
be continuous on [a, b]. Let c be 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.
Warning
If f′′
(c) = 0, the second derivative test is inconclusive (this does not
mean c is neither; we just don’t know yet).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 8 / 25
11. . . . . . .
Recall: The Second Derivative Test
See Section 4.3
Theorem (The Second Derivative Test)
Let f, f′
, and f′′
be continuous on [a, b]. Let c be 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.
Warning
If f′′
(c) = 0, the second derivative test is inconclusive (this does not
mean c is neither; we just don’t know yet).
Corollary
If f′
(c) = 0 and f′′
(x) > 0 for all x, then c is the global minimum of f
If f′
(c) = 0 and f′′
(x) < 0 for all x, then c is the global maximum of f
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 8 / 25
12. . . . . . .
Which to use when?
CIM 1DT 2DT
Pro – no need for
inequalities
– gets global
extrema
automatically
– works on
non-closed,
non-bounded
intervals
– only one derivative
– works on
non-closed,
non-bounded
intervals
– no need for
inequalities
Con – only for closed
bounded intervals
– Uses inequalities
– More work at
boundary than CIM
– More derivatives
– less conclusive
than 1DT
– more work at
boundary than CIM
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 9 / 25
13. . . . . . .
Which to use when?
CIM 1DT 2DT
Pro – no need for
inequalities
– gets global
extrema
automatically
– works on
non-closed,
non-bounded
intervals
– only one derivative
– works on
non-closed,
non-bounded
intervals
– no need for
inequalities
Con – only for closed
bounded intervals
– Uses inequalities
– More work at
boundary than CIM
– More derivatives
– less conclusive
than 1DT
– more work at
boundary than CIM
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 9 / 25
14. . . . . . .
Outline
Recall
More examples
Addition
Distance
Triangles
Economics
The Statue of Liberty
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 10 / 25
15. . . . . . .
Addition with a constraint
Example
Find two positive numbers x and y with xy = 16 and x + y as small as
possible.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
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
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
17. . . . . . .
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, ∞).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
18. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
19. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
20. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 11 / 25
21. . . . . . .
Distance
Example
Find the point P on the parabola y = x2
closest to the point (3, 0).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 12 / 25
22. . . . . . .
Distance
Example
Find the point P on the parabola y = x2
closest to the point (3, 0).
Solution
.. x.
y
..
(x, x2
)
..
3
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 12 / 25
23. . . . . . .
Distance
Example
Find the point P on the parabola y = x2
closest to the point (3, 0).
Solution
The distance between (x, x2
)
and (3, 0) is given by
f(x) =
√
(x − 3)2 + (x2 − 0)2
.. x.
y
..
(x, x2
)
..
3
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 12 / 25
24. . . . . . .
Distance
Example
Find the point P on the parabola y = x2
closest to the point (3, 0).
Solution
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.
y
..
(x, x2
)
..
3
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 12 / 25
25. . . . . . .
Distance
Example
Find the point P on the parabola y = x2
closest to the point (3, 0).
Solution
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
The domain is (−∞, ∞).
.. x.
y
..
(x, x2
)
..
3
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 12 / 25
26. . . . . . .
Distance problem
minimization step
We want to find the global minimum of g(x) = (x − 3)2
+ x4
.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
27. . . . . . .
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)
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
28. . . . . . .
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)
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
29. . . . . . .
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)
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
30. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
31. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 13 / 25
32. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 14 / 25
33. . . . . . .
A problem with a triangle
..
Example
Find the rectangle of maximal area inscribed in a 3-4-5 right triangle with two
sides on legs of the triangle and one vertex on the hypotenuse.
Solution
..
3
.
4
.
5
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 15 / 25
34. . . . . . .
A problem with a triangle
..
Example
Find the rectangle of maximal area inscribed in a 3-4-5 right triangle with two
sides on legs of the triangle and one vertex on the hypotenuse.
Solution
Let the dimensions of the
rectangle be x and y.
..
3
.
4
.
5
.
y
.
x
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 15 / 25
35. . . . . . .
A problem with a triangle
..
Example
Find the rectangle of maximal area inscribed in a 3-4-5 right triangle with two
sides on legs of the triangle and one vertex on the hypotenuse.
Solution
Let the dimensions of the
rectangle be x and y.
Similar triangles give
y
3 − x
=
4
3
=⇒ 3y = 4(3 − x)
..
3
.
4
.
5
.
y
.
x
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 15 / 25
36. . . . . . .
A problem with a triangle
..
Example
Find the rectangle of maximal area inscribed in a 3-4-5 right triangle with two
sides on legs of the triangle and one vertex on the hypotenuse.
Solution
Let the dimensions of the
rectangle be x and y.
Similar triangles give
y
3 − x
=
4
3
=⇒ 3y = 4(3 − x)
So y = 4 −
4
3
x and
A(x) = x
(
4 −
4
3
x
)
= 4x−
4
3
x2 ..
3
.
4
.
5
.
y
.
x
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 15 / 25
37. . . . . . .
Triangle Problem
maximization step
We want to find the absolute maximum of A(x) = 4x −
4
3
x2
on the
interval [0, 3].
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 16 / 25
38. . . . . . .
Triangle Problem
maximization step
We want to find the absolute maximum of A(x) = 4x −
4
3
x2
on the
interval [0, 3].
A(0) = A(3) = 0
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 16 / 25
39. . . . . . .
Triangle Problem
maximization step
We want to find the absolute maximum of A(x) = 4x −
4
3
x2
on the
interval [0, 3].
A(0) = A(3) = 0
A′
(x) = 4 −
8
3
x, which is zero when x =
12
8
= 1.5.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 16 / 25
40. . . . . . .
Triangle Problem
maximization step
We want to find the absolute maximum of A(x) = 4x −
4
3
x2
on the
interval [0, 3].
A(0) = A(3) = 0
A′
(x) = 4 −
8
3
x, which is zero when x =
12
8
= 1.5.
Since A(1.5) = 3, this is the absolute maximum.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 16 / 25
41. . . . . . .
Triangle Problem
maximization step
We want to find the absolute maximum of A(x) = 4x −
4
3
x2
on the
interval [0, 3].
A(0) = A(3) = 0
A′
(x) = 4 −
8
3
x, which is zero when x =
12
8
= 1.5.
Since A(1.5) = 3, this is the absolute maximum.
So the dimensions of the rectangle of maximal area are 1.5 × 2.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 16 / 25
42. . . . . . .
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?
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 17 / 25
43. . . . . . .
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).
We have n(900) = 100 and
∆n
∆r
= −
1
10
. So
n − 100 = −
1
10
(r − 900) =⇒ n(r) = −
1
10
r + 190
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 17 / 25
44. . . . . . .
Economics Problem
Finishing the model and maximizing
The profit per unit rented is r − 100, so
P(r) = (r − 100)n(r) = (r − 100)
(
−
1
10
r + 190
)
= −
1
10
r2
+ 200r − 19000
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 18 / 25
45. . . . . . .
Economics Problem
Finishing the model and maximizing
The profit per unit rented is r − 100, so
P(r) = (r − 100)n(r) = (r − 100)
(
−
1
10
r + 190
)
= −
1
10
r2
+ 200r − 19000
We want to maximize P on the interval 900 ≤ r ≤ 1900. (Why?)
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 18 / 25
46. . . . . . .
Economics Problem
Finishing the model and maximizing
The profit per unit rented is r − 100, so
P(r) = (r − 100)n(r) = (r − 100)
(
−
1
10
r + 190
)
= −
1
10
r2
+ 200r − 19000
We want to maximize P on the interval 900 ≤ r ≤ 1900. (Why?)
A(900) = $800 × 100 = $80, 000, A(1900) = 0
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 18 / 25
47. . . . . . .
Economics Problem
Finishing the model and maximizing
The profit per unit rented is r − 100, so
P(r) = (r − 100)n(r) = (r − 100)
(
−
1
10
r + 190
)
= −
1
10
r2
+ 200r − 19000
We want to maximize P on the interval 900 ≤ r ≤ 1900. (Why?)
A(900) = $800 × 100 = $80, 000, A(1900) = 0
A′
(x) = −
1
5
r + 200, which is zero when r = 1000.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 18 / 25
48. . . . . . .
Economics Problem
Finishing the model and maximizing
The profit per unit rented is r − 100, so
P(r) = (r − 100)n(r) = (r − 100)
(
−
1
10
r + 190
)
= −
1
10
r2
+ 200r − 19000
We want to maximize P on the interval 900 ≤ r ≤ 1900. (Why?)
A(900) = $800 × 100 = $80, 000, A(1900) = 0
A′
(x) = −
1
5
r + 200, which is zero when r = 1000.
n(1000) = 90, so P(r) = $900 × 90 = $81, 000. This is the
maximum intake.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 18 / 25
49. . . . . . .
The Statue of Liberty
Example
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?
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 19 / 25
50. . . . . . .
The Statue of Liberty
Seting up the model
Solution
The angle subtended by the
statue in the viewer’s eye can
be expressed as
θ = arctan
(
a + b
x
)
−arctan
(
b
x
)
.
a
b
θ
x
The domain of θ is all positive real numbers x.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 20 / 25
51. . . . . . .
The Statue of Liberty
Finding the derivative
θ = arctan
(
a + b
x
)
− arctan
(
b
x
)
So
dθ
dx
=
1
1 +
(
a+b
x
)2
·
−(a + b)
x2
−
1
1 +
(
b
x
)2
·
−b
x2
=
b
x2 + b2
−
a + b
x2 + (a + b)2
=
[
x2 + (a + b)2
]
b − (a + b)
[
x2 + b2
]
(x2 + b2
)
[
x2 + (a + b)2
]
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 21 / 25
52. . . . . . .
The Statue of Liberty
Finding the critical points
dθ
dx
=
[
x2 + (a + b)2
]
b − (a + b)
[
x2 + b2
]
(x2 + b2
)
[
x2 + (a + b)2
]
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 22 / 25
53. . . . . . .
The Statue of Liberty
Finding the critical points
dθ
dx
=
[
x2 + (a + b)2
]
b − (a + b)
[
x2 + b2
]
(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
)
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 22 / 25
54. . . . . . .
The Statue of Liberty
Finding the critical points
dθ
dx
=
[
x2 + (a + b)2
]
b − (a + b)
[
x2 + b2
]
(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).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 22 / 25
55. . . . . . .
The Statue of Liberty
Finding the critical points
dθ
dx
=
[
x2 + (a + b)2
]
b − (a + b)
[
x2 + b2
]
(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, ∞).
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 22 / 25
56. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 23 / 25
57. . . . . . .
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.
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 24 / 25
58. . . . . . .
Summary
Remember the checklist
Ask yourself: what is the
objective?
Remember your geometry:
similar triangles
right triangles
trigonometric functions
Name [_
Problem Solving Strategy
Draw a Picture
Kathy had a box of 8 crayons.
She gave some crayons away.
She has 5 left.
How many crayons did Kathy give away?
UNDERSTAND
•
What do you want to find out?
Draw a line under the question.
You can draw a picture
to solve the problem.
crayons
What number do I
add to 5 to get 8?
8 - = 5
5 + 3 = 8
CHECK
Does your answer make sense?
Explain.
Draw a picture to solve the problem.
Write how many were given away.
I. I had10 pencils.
I gave some away.
I have 3left. How many
pencils did I give away?
~7
What number
do I add to 3
to make 10?
13
i
ft
ill
:i
i ?
11
ft
I
'•'
«
I
I
ft A
H 11
M i l
U U U U> U U
I I
V63.0121.041, Calculus I (NYU) Section 4.5 Optimization II November 24, 2010 25 / 25