SlideShare a Scribd company logo
Section 4.4
Optimization Problems
V63.0121.041, Calculus I
New York University
November 22, 2010
Announcements
There is class on Wednesday, November 24
Announcements
There is class on
Wednesday, November 24
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 2 / 31
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 4.4 Optimization Problems November 22, 2010 3 / 31
Notes
Notes
Notes
1
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Outline
Leading by Example
The Text in the Box
More Examples
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 4 / 31
Leading by Example
Example
What is the rectangle of fixed perimeter with maximum area?
Solution
Draw a rectangle.
w
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 5 / 31
Solution Continued
Let its length be and its width be w. The objective function is area
A = w.
This is a function of two variables, not one. But the perimeter is fixed.
Since p = 2 + 2w, we have =
p − 2w
2
, so
A = w =
p − 2w
2
· w =
1
2
(p − 2w)(w) =
1
2
pw − w2
Now we have A as a function of w alone (p is constant).
The natural domain of this function is [0, p/2] (we want to make sure
A(w) ≥ 0).
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 6 / 31
Notes
Notes
Notes
2
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Solution Concluded
We use the Closed Interval Method for A(w) =
1
2
pw − w2
on [0, p/2].
At the endpoints, A(0) = A(p/2) = 0.
To find the critical points, we find
dA
dw
=
1
2
p − 2w.
The critical points are when
0 =
1
2
p − 2w =⇒ w =
p
4
Since this is the only critical point, it must be the maximum. In this
case =
p
4
as well.
We have a square! The maximal area is A(p/4) = p2
/16.
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 7 / 31
Outline
Leading by Example
The Text in the Box
More Examples
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 8 / 31
Strategies for Problem Solving
1. Understand the problem
2. Devise a plan
3. Carry out the plan
4. Review and extend
Gy¨orgy P´olya
(Hungarian, 1887–1985)
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 9 / 31
Notes
Notes
Notes
3
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
The Text in the Box
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 4.4 Optimization Problems November 22, 2010 10 / 31
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 4.4 Optimization Problems November 22, 2010 12 / 31
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 4.4 Optimization Problems November 22, 2010 13 / 31
Notes
Notes
Notes
4
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
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
fV63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 14 / 31
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 4.4 Optimization Problems November 22, 2010 15 / 31
Outline
Leading by Example
The Text in the Box
More Examples
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 16 / 31
Notes
Notes
Notes
5
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Another Example
Example (The Best Fencing Plan)
A rectangular plot of farmland will be bounded on one side by a river and
on the other three sides by a single-strand electric fence. With 800m of
wire at your disposal, what is the largest area you can enclose, and what
are its dimensions?
Known: amount of fence used
Unknown: area enclosed
Objective: maximize area
Constraint: fixed fence length
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 17 / 31
Solution
1. Everybody understand?
2. Draw a diagram.
3. Length and width are and w. Length of wire used is p.
4. Q = area = w.
5. Since p = + 2w, we have = p − 2w and so
Q(w) = (p − 2w)(w) = pw − 2w2
The domain of Q is [0, p/2]
6.
dQ
dw
= p − 4w, which is zero when w =
p
4
. Q(0) = Q(p/2) = 0, but
Q
p
4
= p ·
p
4
− 2 ·
p2
16
=
p2
8
= 80, 000m2
so the critical point is the absolute maximum.
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 18 / 31
Diagram
A rectangular plot of farmland will be bounded on one side by a river and
on the other three sides by a single-strand electric fence. With 800 m of
wire at your disposal, what is the largest area you can enclose, and what
are its dimensions?
w
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 21 / 31
Notes
Notes
Notes
6
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Your turn
Example (The shortest fence)
A 216m2
rectangular pea patch is to be enclosed by a fence and divided
into two equal parts by another fence parallel to one of its sides. What
dimensions for the outer rectangle will require the smallest total length of
fence? How much fence will be needed?
Solution
Let the length and width of the pea patch be and w. The amount of
fence needed is f = 2 + 3w. Since w = A, a constant, we have
f (w) = 2
A
w
+ 3w.
The domain is all positive numbers.
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 25 / 31
Diagram
w
f = 2 + 3w A = w ≡ 216
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 26 / 31
Solution (Continued)
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 27 / 31
Notes
Notes
Notes
7
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Try this one
Example
An advertisement consists of a rectangular printed region plus 1 in margins
on the sides and 1.5 in margins on the top and bottom. If the total area of
the advertisement is to be 120 in2
, what dimensions should the
advertisement be to maximize the area of the printed region?
Answer
The optimal paper dimensions are 4
√
5 in by 6
√
5 in.
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 28 / 31
Solution
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 29 / 31
Solution (Concluded)
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 30 / 31
Notes
Notes
Notes
8
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
Summary
Remember the checklist
Ask yourself: what is the
objective?
Remember your geometry:
similar triangles
right triangles
trigonometric functions
V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 31 / 31
Notes
Notes
Notes
9
Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010

More Related Content

PDF
Lesson 22: Optimization (Section 021 handout)
PDF
0580 w11 qp_21
PDF
0580 w11 qp_21
PDF
0580 s11 qp_22
PDF
Lesson 22: Optimization II (Section 041 slides)
PDF
Lesson 22: Optimization II (Section 021 slides)
PDF
Lindley smith 1972
PDF
0580 w11 qp_12
Lesson 22: Optimization (Section 021 handout)
0580 w11 qp_21
0580 w11 qp_21
0580 s11 qp_22
Lesson 22: Optimization II (Section 041 slides)
Lesson 22: Optimization II (Section 021 slides)
Lindley smith 1972
0580 w11 qp_12

What's hot (17)

PDF
Lesson 21: Curve Sketching (Section 041 handout)
PDF
0580 w11 qp_22
PDF
0580 w08 qp_02
PDF
0580 w10 qp_23
PDF
0580 s08 qp_2
PDF
Igcse core papers 2002 2014
PPT
5.2 divede and conquer 03
PDF
Analysis and design of algorithms part 4
PDF
0580 w11 qp_11
PDF
0580_w13_qp_41
DOCX
Banco de preguntas para el ap
PDF
0580 s11 qp_21
PDF
Lesson 21: Curve Sketching (Section 021 handout)
PDF
Solving Problems Involving Radicals
PPTX
Aieee Mathematics 2006
PDF
Chapter 7 statistics
DOCX
 Igcse international mathematics extended standards
Lesson 21: Curve Sketching (Section 041 handout)
0580 w11 qp_22
0580 w08 qp_02
0580 w10 qp_23
0580 s08 qp_2
Igcse core papers 2002 2014
5.2 divede and conquer 03
Analysis and design of algorithms part 4
0580 w11 qp_11
0580_w13_qp_41
Banco de preguntas para el ap
0580 s11 qp_21
Lesson 21: Curve Sketching (Section 021 handout)
Solving Problems Involving Radicals
Aieee Mathematics 2006
Chapter 7 statistics
 Igcse international mathematics extended standards
Ad

Viewers also liked (7)

PPT
Aggregat planning stevenson
PPT
Optimizing Sterile Processing Workflow
PDF
PPT
ITIL Practical Guide - Service Operation
PDF
Functions in Service Operation
PPTX
Continuous process improvement (4)
PDF
Service Operation Processes
Aggregat planning stevenson
Optimizing Sterile Processing Workflow
ITIL Practical Guide - Service Operation
Functions in Service Operation
Continuous process improvement (4)
Service Operation Processes
Ad

Similar to Lesson 22: Optimization (Section 041 handout) (20)

PDF
Lesson 22: Optimization (Section 041 slides)
PDF
Lesson 22: Optimization (Section 021 slides)
PDF
Lesson 22: Optimization II (Section 041 handout)
PDF
Lesson 18: Maximum and Minimum Values (Section 021 handout)
PDF
Lesson 22: Optimization (Section 021 slides)
PDF
Lesson 22: Optimization (Section 041 slides)
PDF
Lesson 19: The Mean Value Theorem (Section 021 handout)
PDF
Lesson 19: The Mean Value Theorem (Section 041 handout)
PDF
Lesson 18: Maximum and Minimum Values (Section 041 handout)
PDF
Lesson22 -optimization_problems_slides
PDF
Lesson22 -optimization_problems_slides
PDF
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
PDF
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
PDF
Lesson 19: The Mean Value Theorem (Section 021 slides)
PDF
Lesson 18: Maximum and Minimum Values (Section 021 slides)
PDF
Lesson 24: Areas, Distances, the Integral (Section 041 handout)
PDF
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
PDF
Lesson 26: Integration by Substitution (handout)
PDF
Lesson 23: Antiderivatives (Section 041 handout)
PDF
Lesson 25: The Fundamental Theorem of Calculus
Lesson 22: Optimization (Section 041 slides)
Lesson 22: Optimization (Section 021 slides)
Lesson 22: Optimization II (Section 041 handout)
Lesson 18: Maximum and Minimum Values (Section 021 handout)
Lesson 22: Optimization (Section 021 slides)
Lesson 22: Optimization (Section 041 slides)
Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 18: Maximum and Minimum Values (Section 041 handout)
Lesson22 -optimization_problems_slides
Lesson22 -optimization_problems_slides
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 18: Maximum and Minimum Values (Section 021 slides)
Lesson 24: Areas, Distances, the Integral (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 26: Integration by Substitution (handout)
Lesson 23: Antiderivatives (Section 041 handout)
Lesson 25: The Fundamental Theorem of Calculus

More from Matthew Leingang (20)

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

Recently uploaded (20)

PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PDF
MIND Revenue Release Quarter 2 2025 Press Release
PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
PPTX
Cloud computing and distributed systems.
PDF
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
PDF
Encapsulation_ Review paper, used for researhc scholars
PDF
Review of recent advances in non-invasive hemoglobin estimation
PDF
Profit Center Accounting in SAP S/4HANA, S4F28 Col11
PPT
“AI and Expert System Decision Support & Business Intelligence Systems”
PDF
Spectral efficient network and resource selection model in 5G networks
PPTX
MYSQL Presentation for SQL database connectivity
PDF
cuic standard and advanced reporting.pdf
PPTX
VMware vSphere Foundation How to Sell Presentation-Ver1.4-2-14-2024.pptx
PDF
Unlocking AI with Model Context Protocol (MCP)
PDF
Empathic Computing: Creating Shared Understanding
PDF
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
PPTX
Programs and apps: productivity, graphics, security and other tools
PPTX
ACSFv1EN-58255 AWS Academy Cloud Security Foundations.pptx
PPTX
sap open course for s4hana steps from ECC to s4
PPT
Teaching material agriculture food technology
Reach Out and Touch Someone: Haptics and Empathic Computing
MIND Revenue Release Quarter 2 2025 Press Release
The Rise and Fall of 3GPP – Time for a Sabbatical?
Cloud computing and distributed systems.
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
Encapsulation_ Review paper, used for researhc scholars
Review of recent advances in non-invasive hemoglobin estimation
Profit Center Accounting in SAP S/4HANA, S4F28 Col11
“AI and Expert System Decision Support & Business Intelligence Systems”
Spectral efficient network and resource selection model in 5G networks
MYSQL Presentation for SQL database connectivity
cuic standard and advanced reporting.pdf
VMware vSphere Foundation How to Sell Presentation-Ver1.4-2-14-2024.pptx
Unlocking AI with Model Context Protocol (MCP)
Empathic Computing: Creating Shared Understanding
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
Programs and apps: productivity, graphics, security and other tools
ACSFv1EN-58255 AWS Academy Cloud Security Foundations.pptx
sap open course for s4hana steps from ECC to s4
Teaching material agriculture food technology

Lesson 22: Optimization (Section 041 handout)

  • 1. Section 4.4 Optimization Problems V63.0121.041, Calculus I New York University November 22, 2010 Announcements There is class on Wednesday, November 24 Announcements There is class on Wednesday, November 24 V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 2 / 31 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 4.4 Optimization Problems November 22, 2010 3 / 31 Notes Notes Notes 1 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 2. Outline Leading by Example The Text in the Box More Examples V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 4 / 31 Leading by Example Example What is the rectangle of fixed perimeter with maximum area? Solution Draw a rectangle. w V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 5 / 31 Solution Continued Let its length be and its width be w. The objective function is area A = w. This is a function of two variables, not one. But the perimeter is fixed. Since p = 2 + 2w, we have = p − 2w 2 , so A = w = p − 2w 2 · w = 1 2 (p − 2w)(w) = 1 2 pw − w2 Now we have A as a function of w alone (p is constant). The natural domain of this function is [0, p/2] (we want to make sure A(w) ≥ 0). V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 6 / 31 Notes Notes Notes 2 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 3. Solution Concluded We use the Closed Interval Method for A(w) = 1 2 pw − w2 on [0, p/2]. At the endpoints, A(0) = A(p/2) = 0. To find the critical points, we find dA dw = 1 2 p − 2w. The critical points are when 0 = 1 2 p − 2w =⇒ w = p 4 Since this is the only critical point, it must be the maximum. In this case = p 4 as well. We have a square! The maximal area is A(p/4) = p2 /16. V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 7 / 31 Outline Leading by Example The Text in the Box More Examples V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 8 / 31 Strategies for Problem Solving 1. Understand the problem 2. Devise a plan 3. Carry out the plan 4. Review and extend Gy¨orgy P´olya (Hungarian, 1887–1985) V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 9 / 31 Notes Notes Notes 3 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 4. The Text in the Box 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 4.4 Optimization Problems November 22, 2010 10 / 31 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 4.4 Optimization Problems November 22, 2010 12 / 31 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 4.4 Optimization Problems November 22, 2010 13 / 31 Notes Notes Notes 4 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 5. 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 fV63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 14 / 31 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 4.4 Optimization Problems November 22, 2010 15 / 31 Outline Leading by Example The Text in the Box More Examples V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 16 / 31 Notes Notes Notes 5 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 6. Another Example Example (The Best Fencing Plan) A rectangular plot of farmland will be bounded on one side by a river and on the other three sides by a single-strand electric fence. With 800m of wire at your disposal, what is the largest area you can enclose, and what are its dimensions? Known: amount of fence used Unknown: area enclosed Objective: maximize area Constraint: fixed fence length V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 17 / 31 Solution 1. Everybody understand? 2. Draw a diagram. 3. Length and width are and w. Length of wire used is p. 4. Q = area = w. 5. Since p = + 2w, we have = p − 2w and so Q(w) = (p − 2w)(w) = pw − 2w2 The domain of Q is [0, p/2] 6. dQ dw = p − 4w, which is zero when w = p 4 . Q(0) = Q(p/2) = 0, but Q p 4 = p · p 4 − 2 · p2 16 = p2 8 = 80, 000m2 so the critical point is the absolute maximum. V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 18 / 31 Diagram A rectangular plot of farmland will be bounded on one side by a river and on the other three sides by a single-strand electric fence. With 800 m of wire at your disposal, what is the largest area you can enclose, and what are its dimensions? w V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 21 / 31 Notes Notes Notes 6 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 7. Your turn Example (The shortest fence) A 216m2 rectangular pea patch is to be enclosed by a fence and divided into two equal parts by another fence parallel to one of its sides. What dimensions for the outer rectangle will require the smallest total length of fence? How much fence will be needed? Solution Let the length and width of the pea patch be and w. The amount of fence needed is f = 2 + 3w. Since w = A, a constant, we have f (w) = 2 A w + 3w. The domain is all positive numbers. V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 25 / 31 Diagram w f = 2 + 3w A = w ≡ 216 V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 26 / 31 Solution (Continued) V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 27 / 31 Notes Notes Notes 7 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 8. Try this one Example An advertisement consists of a rectangular printed region plus 1 in margins on the sides and 1.5 in margins on the top and bottom. If the total area of the advertisement is to be 120 in2 , what dimensions should the advertisement be to maximize the area of the printed region? Answer The optimal paper dimensions are 4 √ 5 in by 6 √ 5 in. V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 28 / 31 Solution V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 29 / 31 Solution (Concluded) V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 30 / 31 Notes Notes Notes 8 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010
  • 9. Summary Remember the checklist Ask yourself: what is the objective? Remember your geometry: similar triangles right triangles trigonometric functions V63.0121.041, Calculus I (NYU) Section 4.4 Optimization Problems November 22, 2010 31 / 31 Notes Notes Notes 9 Section 4.4 : Optimization ProblemsV63.0121.041, Calculus I November 22, 2010