SlideShare una empresa de Scribd logo
1 de 11
Descargar para leer sin conexión
Section 4.1
Maximum and Minimum Values
V63.0121.021, Calculus I
New York University
November 9, 2010
Announcements
Quiz 4 on Sections 3.3, 3.4, 3.5, and 3.7 next week (November 16,
18, or 19)
Announcements
Quiz 4 on Sections 3.3, 3.4,
3.5, and 3.7 next week
(November 16, 18, or 19)
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 2 / 34
Objectives
Understand and be able to
explain the statement of the
Extreme Value Theorem.
Understand and be able to
explain the statement of
Fermat’s Theorem.
Use the Closed Interval
Method to find the extreme
values of a function defined
on a closed interval.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 3 / 34
Notes
Notes
Notes
1
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Outline
Introduction
The Extreme Value Theorem
Fermat’s Theorem (not the last one)
Tangent: Fermat’s Last Theorem
The Closed Interval Method
Examples
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 4 / 34
Optimize
Why go to the extremes?
Rationally speaking, it is
advantageous to find the
extreme values of a function
(maximize profit, minimize
costs, etc.)
Many laws of science are
derived from minimizing
principles.
Maupertuis’ principle:
“Action is minimized
through the wisdom of
God.”
Pierre-Louis Maupertuis
(1698–1759)V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 6 / 34
Notes
Notes
Notes
2
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Design
Image credit: Jason Tromm
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 7 / 34
Optics
Image credit: jacreative
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 9 / 34
Outline
Introduction
The Extreme Value Theorem
Fermat’s Theorem (not the last one)
Tangent: Fermat’s Last Theorem
The Closed Interval Method
Examples
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 11 / 34
Notes
Notes
Notes
3
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Extreme points and values
Definition
Let f have domain D.
The function f has an absolute maximum
(or global maximum) (respectively,
absolute minimum) at c if f (c) ≥ f (x)
(respectively, f (c) ≤ f (x)) for all x in D
The number f (c) is called the maximum
value (respectively, minimum value) of f
on D.
An extremum is either a maximum or a
minimum. An extreme value is either a
maximum value or minimum value.
Image credit: Patrick Q
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 12 / 34
The Extreme Value Theorem
Theorem (The Extreme Value Theorem)
Let f be a function which is continuous on the closed interval [a, b]. Then
f attains an absolute maximum value f (c) and an absolute minimum
value f (d) at numbers c and d in [a, b].
a b
c
maximum
maximum
value
f (c)
d
minimum
minimum
value
f (d)
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 13 / 34
No proof of EVT forthcoming
This theorem is very hard to prove without using technical facts
about continuous functions and closed intervals.
But we can show the importance of each of the hypotheses.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 14 / 34
Notes
Notes
Notes
4
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Bad Example #1
Example
Consider the function
f (x) =
x 0 ≤ x < 1
x − 2 1 ≤ x ≤ 2.
|
1
Then although values of f (x) get arbitrarily close to 1 and never bigger
than 1, 1 is not the maximum value of f on [0, 1] because it is never
achieved. This does not violate EVT because f is not continuous.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 15 / 34
Bad Example #2
Example
Consider the function f (x) = x restricted to the interval [0, 1).
|
1
There is still no maximum value (values get arbitrarily close to 1 but do not
achieve it). This does not violate EVT because the domain is not closed.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 16 / 34
Final Bad Example
Example
Consider the function f (x) =
1
x
is continuous on the closed interval [1, ∞).
1
There is no minimum value (values get arbitrarily close to 0 but do not
achieve it). This does not violate EVT because the domain is not bounded.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 17 / 34
Notes
Notes
Notes
5
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Outline
Introduction
The Extreme Value Theorem
Fermat’s Theorem (not the last one)
Tangent: Fermat’s Last Theorem
The Closed Interval Method
Examples
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 18 / 34
Local extrema
Definition
A function f has a local maximum or relative maximum at c if f (c) ≥ f (x)
when x is near c. This means that f (c) ≥ f (x) for all x in some open interval
containing c.
Similarly, f has a local minimum at c if f (c) ≤ f (x) when x is near c.
|
a
|
blocal
maximum
local
minimum
global
max
local and global
min
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 19 / 34
Local extrema
So a local extremum must be inside the domain of f (not on the end).
A global extremum that is inside the domain is a local extremum.
|
a
|
blocal
maximum
local
minimum
global
max
local and global
min
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 20 / 34
Notes
Notes
Notes
6
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Fermat’s Theorem
Theorem (Fermat’s Theorem)
Suppose f has a local extremum at c and f is differentiable at c. Then
f (c) = 0.
|
a
|
blocal
maximum
local
minimum
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 21 / 34
Sketch of proof of Fermat’s Theorem
Suppose that f has a local maximum at c.
If x is slightly greater than c, f (x) ≤ f (c). This means
f (x) − f (c)
x − c
≤ 0 =⇒ lim
x→c+
f (x) − f (c)
x − c
≤ 0
The same will be true on the other end: if x is slightly less than c,
f (x) ≤ f (c). This means
f (x) − f (c)
x − c
≥ 0 =⇒ lim
x→c−
f (x) − f (c)
x − c
≥ 0
Since the limit f (c) = lim
x→c
f (x) − f (c)
x − c
exists, it must be 0.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 22 / 34
Meet the Mathematician: Pierre de Fermat
1601–1665
Lawyer and number theorist
Proved many theorems,
didn’t quite prove his last
one
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 23 / 34
Notes
Notes
Notes
7
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Outline
Introduction
The Extreme Value Theorem
Fermat’s Theorem (not the last one)
Tangent: Fermat’s Last Theorem
The Closed Interval Method
Examples
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 25 / 34
Flowchart for placing extrema
Thanks to Fermat
Suppose f is a continuous function on the closed, bounded interval [a, b],
and c is a global maximum point.
start
Is c an
endpoint?
c = a or
c = b
c is a
local max
Is f
diff’ble at
c?
f is not
diff at c
f (c) = 0
no
yes
no
yes
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 26 / 34
The Closed Interval Method
This means to find the maximum value of f on [a, b], we need to:
Evaluate f at the endpoints a and b
Evaluate f at the critical points or critical numbers 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.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 27 / 34
Notes
Notes
Notes
8
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Outline
Introduction
The Extreme Value Theorem
Fermat’s Theorem (not the last one)
Tangent: Fermat’s Last Theorem
The Closed Interval Method
Examples
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 28 / 34
Extreme values of a linear function
Example
Find the extreme values of f (x) = 2x − 5 on [−1, 2].
Solution
Since f (x) = 2, which is never zero, we have no critical points and we
need only investigate the endpoints:
f (−1) = 2(−1) − 5 = −7
f (2) = 2(2) − 5 = −1
So
The absolute minimum (point) is at −1; the minimum value is −7.
The absolute maximum (point) is at 2; the maximum value is −1.
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 29 / 34
Extreme values of a quadratic function
Example
Find the extreme values of f (x) = x2
− 1 on [−1, 2].
Solution
We have f (x) = 2x, which is zero when x = 0. So our points to check
are:
f (−1) = 0
f (0) = − 1 (absolute min)
f (2) = 3 (absolute max)
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 30 / 34
Notes
Notes
Notes
9
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Extreme values of a cubic function
Example
Find the extreme values of f (x) = 2x3
− 3x2
+ 1 on [−1, 2].
Solution
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 31 / 34
Extreme values of an algebraic function
Example
Find the extreme values of f (x) = x2/3
(x + 2) on [−1, 2].
Solution
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 32 / 34
Extreme values of another algebraic function
Example
Find the extreme values of f (x) = 4 − x2 on [−2, 1].
Solution
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 33 / 34
Notes
Notes
Notes
10
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
Summary
The Extreme Value Theorem: a continuous function on a closed
interval must achieve its max and min
Fermat’s Theorem: local extrema are critical points
The Closed Interval Method: an algorithm for finding global extrema
V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 34 / 34
Notes
Notes
Notes
11
Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010

Más contenido relacionado

La actualidad más candente

Chapter3 Search
Chapter3 SearchChapter3 Search
Chapter3 Search
Khiem Ho
 
Lecture 08 uninformed search techniques
Lecture 08 uninformed search techniquesLecture 08 uninformed search techniques
Lecture 08 uninformed search techniques
Hema Kashyap
 
2 lectures 16 17-informed search algorithms ch 4.3
2 lectures 16 17-informed search algorithms ch 4.32 lectures 16 17-informed search algorithms ch 4.3
2 lectures 16 17-informed search algorithms ch 4.3
Ravi Balout
 

La actualidad más candente (19)

Uninformed search
Uninformed searchUninformed search
Uninformed search
 
Searching Algorithm
Searching AlgorithmSearching Algorithm
Searching Algorithm
 
Lesson 18: Maximum and Minimum Values (Section 021 slides)
Lesson 18: Maximum and Minimum Values (Section 021 slides)Lesson 18: Maximum and Minimum Values (Section 021 slides)
Lesson 18: Maximum and Minimum Values (Section 021 slides)
 
Graph Traversal Algorithms - Depth First Search Traversal
Graph Traversal Algorithms - Depth First Search TraversalGraph Traversal Algorithms - Depth First Search Traversal
Graph Traversal Algorithms - Depth First Search Traversal
 
DFS and BFS
DFS and BFSDFS and BFS
DFS and BFS
 
Chapter3 Search
Chapter3 SearchChapter3 Search
Chapter3 Search
 
Reading papers - survey on Non-Convex Optimization
Reading papers - survey on Non-Convex OptimizationReading papers - survey on Non-Convex Optimization
Reading papers - survey on Non-Convex Optimization
 
Lecture 08 uninformed search techniques
Lecture 08 uninformed search techniquesLecture 08 uninformed search techniques
Lecture 08 uninformed search techniques
 
AI - Backtracking vs Depth-First Search (DFS)
AI - Backtracking vs Depth-First Search (DFS)AI - Backtracking vs Depth-First Search (DFS)
AI - Backtracking vs Depth-First Search (DFS)
 
Analysis and design of algorithms part 4
Analysis and design of algorithms part 4Analysis and design of algorithms part 4
Analysis and design of algorithms part 4
 
Heuristic search
Heuristic searchHeuristic search
Heuristic search
 
Breadth first search signed
Breadth first search signedBreadth first search signed
Breadth first search signed
 
2 lectures 16 17-informed search algorithms ch 4.3
2 lectures 16 17-informed search algorithms ch 4.32 lectures 16 17-informed search algorithms ch 4.3
2 lectures 16 17-informed search algorithms ch 4.3
 
Dirichlet processes and Applications
Dirichlet processes and ApplicationsDirichlet processes and Applications
Dirichlet processes and Applications
 
Sns pre sem
Sns pre semSns pre sem
Sns pre sem
 
Assessment and linear programming under fuzzy conditions
Assessment and linear programming under fuzzy conditionsAssessment and linear programming under fuzzy conditions
Assessment and linear programming under fuzzy conditions
 
Lesson 18: Maximum and Minimum Values (Section 041 slides)
Lesson 18: Maximum and Minimum Values (Section 041 slides)Lesson 18: Maximum and Minimum Values (Section 041 slides)
Lesson 18: Maximum and Minimum Values (Section 041 slides)
 
NFM 2015 - Sum of abstract domains
NFM 2015 - Sum of abstract domainsNFM 2015 - Sum of abstract domains
NFM 2015 - Sum of abstract domains
 
Uniformed tree searching
Uniformed tree searching Uniformed tree searching
Uniformed tree searching
 

Similar a Lesson 18: Maximum and Minimum Values (Section 021 handout)

Lesson18 -maximum_and_minimum_values_slides
Lesson18  -maximum_and_minimum_values_slidesLesson18  -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slides
Matthew Leingang
 
Lesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slidesLesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slides
Mel Anthony Pepito
 
Lesson 22: Optimization (Section 041 handout)
Lesson 22: Optimization (Section 041 handout)Lesson 22: Optimization (Section 041 handout)
Lesson 22: Optimization (Section 041 handout)
Matthew Leingang
 
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20  -derivatives_and_the_shape_of_curves_021_slidesLesson20  -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
Matthew Leingang
 
Lesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of CalculusLesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of Calculus
Matthew Leingang
 

Similar a Lesson 18: Maximum and Minimum Values (Section 021 handout) (20)

Lesson 22: Optimization (Section 021 handout)
Lesson 22: Optimization (Section 021 handout)Lesson 22: Optimization (Section 021 handout)
Lesson 22: Optimization (Section 021 handout)
 
Lesson 18: Maximum and Minimum Values (Section 021 slides)
Lesson 18: Maximum and Minimum Values (Section 021 slides)Lesson 18: Maximum and Minimum Values (Section 021 slides)
Lesson 18: Maximum and Minimum Values (Section 021 slides)
 
Lesson18 -maximum_and_minimum_values_slides
Lesson18  -maximum_and_minimum_values_slidesLesson18  -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slides
 
Lesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slidesLesson18 -maximum_and_minimum_values_slides
Lesson18 -maximum_and_minimum_values_slides
 
Lesson 18: Maximum and Minimum Values (Section 041 slides)
Lesson 18: Maximum and Minimum Values (Section 041 slides)Lesson 18: Maximum and Minimum Values (Section 041 slides)
Lesson 18: Maximum and Minimum Values (Section 041 slides)
 
Lesson 22: Optimization (Section 041 handout)
Lesson 22: Optimization (Section 041 handout)Lesson 22: Optimization (Section 041 handout)
Lesson 22: Optimization (Section 041 handout)
 
Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)Lesson 19: The Mean Value Theorem (Section 021 handout)
Lesson 19: The Mean Value Theorem (Section 021 handout)
 
Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)Lesson 19: The Mean Value Theorem (Section 041 handout)
Lesson 19: The Mean Value Theorem (Section 041 handout)
 
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 021 handout)
 
Lesson 22: Optimization II (Section 041 slides)
Lesson 22: Optimization II (Section 041 slides)Lesson 22: Optimization II (Section 041 slides)
Lesson 22: Optimization II (Section 041 slides)
 
Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)Lesson 19: The Mean Value Theorem (Section 021 slides)
Lesson 19: The Mean Value Theorem (Section 021 slides)
 
Lesson 22: Optimization II (Section 041 handout)
Lesson 22: Optimization II (Section 041 handout)Lesson 22: Optimization II (Section 041 handout)
Lesson 22: Optimization II (Section 041 handout)
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
Lesson 20: Derivatives and the Shape of Curves (Section 041 handout)
 
Lesson 22: Optimization (Section 021 slides)
Lesson 22: Optimization (Section 021 slides)Lesson 22: Optimization (Section 021 slides)
Lesson 22: Optimization (Section 021 slides)
 
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
Lesson 20: Derivatives and the Shape of Curves (Section 041 slides)
 
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
Lesson20  -derivatives_and_the_shape_of_curves_021_slidesLesson20  -derivatives_and_the_shape_of_curves_021_slides
Lesson20 -derivatives_and_the_shape_of_curves_021_slides
 
3.1
3.13.1
3.1
 
Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)
 
Lesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of CalculusLesson 25: The Fundamental Theorem of Calculus
Lesson 25: The Fundamental Theorem of Calculus
 
Lesson 22: Optimization II (Section 021 slides)
Lesson 22: Optimization II (Section 021 slides)Lesson 22: Optimization II (Section 021 slides)
Lesson 22: Optimization II (Section 021 slides)
 

Más de 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

Último (20)

Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Evaluating the top large language models.pdf
Evaluating the top large language models.pdfEvaluating the top large language models.pdf
Evaluating the top large language models.pdf
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
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
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
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
 
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
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
[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
 

Lesson 18: Maximum and Minimum Values (Section 021 handout)

  • 1. Section 4.1 Maximum and Minimum Values V63.0121.021, Calculus I New York University November 9, 2010 Announcements Quiz 4 on Sections 3.3, 3.4, 3.5, and 3.7 next week (November 16, 18, or 19) Announcements Quiz 4 on Sections 3.3, 3.4, 3.5, and 3.7 next week (November 16, 18, or 19) V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 2 / 34 Objectives Understand and be able to explain the statement of the Extreme Value Theorem. Understand and be able to explain the statement of Fermat’s Theorem. Use the Closed Interval Method to find the extreme values of a function defined on a closed interval. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 3 / 34 Notes Notes Notes 1 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 2. Outline Introduction The Extreme Value Theorem Fermat’s Theorem (not the last one) Tangent: Fermat’s Last Theorem The Closed Interval Method Examples V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 4 / 34 Optimize Why go to the extremes? Rationally speaking, it is advantageous to find the extreme values of a function (maximize profit, minimize costs, etc.) Many laws of science are derived from minimizing principles. Maupertuis’ principle: “Action is minimized through the wisdom of God.” Pierre-Louis Maupertuis (1698–1759)V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 6 / 34 Notes Notes Notes 2 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 3. Design Image credit: Jason Tromm V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 7 / 34 Optics Image credit: jacreative V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 9 / 34 Outline Introduction The Extreme Value Theorem Fermat’s Theorem (not the last one) Tangent: Fermat’s Last Theorem The Closed Interval Method Examples V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 11 / 34 Notes Notes Notes 3 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 4. Extreme points and values Definition Let f have domain D. The function f has an absolute maximum (or global maximum) (respectively, absolute minimum) at c if f (c) ≥ f (x) (respectively, f (c) ≤ f (x)) for all x in D The number f (c) is called the maximum value (respectively, minimum value) of f on D. An extremum is either a maximum or a minimum. An extreme value is either a maximum value or minimum value. Image credit: Patrick Q V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 12 / 34 The Extreme Value Theorem Theorem (The Extreme Value Theorem) Let f be a function which is continuous on the closed interval [a, b]. Then f attains an absolute maximum value f (c) and an absolute minimum value f (d) at numbers c and d in [a, b]. a b c maximum maximum value f (c) d minimum minimum value f (d) V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 13 / 34 No proof of EVT forthcoming This theorem is very hard to prove without using technical facts about continuous functions and closed intervals. But we can show the importance of each of the hypotheses. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 14 / 34 Notes Notes Notes 4 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 5. Bad Example #1 Example Consider the function f (x) = x 0 ≤ x < 1 x − 2 1 ≤ x ≤ 2. | 1 Then although values of f (x) get arbitrarily close to 1 and never bigger than 1, 1 is not the maximum value of f on [0, 1] because it is never achieved. This does not violate EVT because f is not continuous. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 15 / 34 Bad Example #2 Example Consider the function f (x) = x restricted to the interval [0, 1). | 1 There is still no maximum value (values get arbitrarily close to 1 but do not achieve it). This does not violate EVT because the domain is not closed. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 16 / 34 Final Bad Example Example Consider the function f (x) = 1 x is continuous on the closed interval [1, ∞). 1 There is no minimum value (values get arbitrarily close to 0 but do not achieve it). This does not violate EVT because the domain is not bounded. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 17 / 34 Notes Notes Notes 5 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 6. Outline Introduction The Extreme Value Theorem Fermat’s Theorem (not the last one) Tangent: Fermat’s Last Theorem The Closed Interval Method Examples V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 18 / 34 Local extrema Definition A function f has a local maximum or relative maximum at c if f (c) ≥ f (x) when x is near c. This means that f (c) ≥ f (x) for all x in some open interval containing c. Similarly, f has a local minimum at c if f (c) ≤ f (x) when x is near c. | a | blocal maximum local minimum global max local and global min V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 19 / 34 Local extrema So a local extremum must be inside the domain of f (not on the end). A global extremum that is inside the domain is a local extremum. | a | blocal maximum local minimum global max local and global min V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 20 / 34 Notes Notes Notes 6 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 7. Fermat’s Theorem Theorem (Fermat’s Theorem) Suppose f has a local extremum at c and f is differentiable at c. Then f (c) = 0. | a | blocal maximum local minimum V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 21 / 34 Sketch of proof of Fermat’s Theorem Suppose that f has a local maximum at c. If x is slightly greater than c, f (x) ≤ f (c). This means f (x) − f (c) x − c ≤ 0 =⇒ lim x→c+ f (x) − f (c) x − c ≤ 0 The same will be true on the other end: if x is slightly less than c, f (x) ≤ f (c). This means f (x) − f (c) x − c ≥ 0 =⇒ lim x→c− f (x) − f (c) x − c ≥ 0 Since the limit f (c) = lim x→c f (x) − f (c) x − c exists, it must be 0. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 22 / 34 Meet the Mathematician: Pierre de Fermat 1601–1665 Lawyer and number theorist Proved many theorems, didn’t quite prove his last one V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 23 / 34 Notes Notes Notes 7 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 8. Outline Introduction The Extreme Value Theorem Fermat’s Theorem (not the last one) Tangent: Fermat’s Last Theorem The Closed Interval Method Examples V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 25 / 34 Flowchart for placing extrema Thanks to Fermat Suppose f is a continuous function on the closed, bounded interval [a, b], and c is a global maximum point. start Is c an endpoint? c = a or c = b c is a local max Is f diff’ble at c? f is not diff at c f (c) = 0 no yes no yes V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 26 / 34 The Closed Interval Method This means to find the maximum value of f on [a, b], we need to: Evaluate f at the endpoints a and b Evaluate f at the critical points or critical numbers 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. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 27 / 34 Notes Notes Notes 8 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 9. Outline Introduction The Extreme Value Theorem Fermat’s Theorem (not the last one) Tangent: Fermat’s Last Theorem The Closed Interval Method Examples V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 28 / 34 Extreme values of a linear function Example Find the extreme values of f (x) = 2x − 5 on [−1, 2]. Solution Since f (x) = 2, which is never zero, we have no critical points and we need only investigate the endpoints: f (−1) = 2(−1) − 5 = −7 f (2) = 2(2) − 5 = −1 So The absolute minimum (point) is at −1; the minimum value is −7. The absolute maximum (point) is at 2; the maximum value is −1. V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 29 / 34 Extreme values of a quadratic function Example Find the extreme values of f (x) = x2 − 1 on [−1, 2]. Solution We have f (x) = 2x, which is zero when x = 0. So our points to check are: f (−1) = 0 f (0) = − 1 (absolute min) f (2) = 3 (absolute max) V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 30 / 34 Notes Notes Notes 9 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 10. Extreme values of a cubic function Example Find the extreme values of f (x) = 2x3 − 3x2 + 1 on [−1, 2]. Solution V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 31 / 34 Extreme values of an algebraic function Example Find the extreme values of f (x) = x2/3 (x + 2) on [−1, 2]. Solution V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 32 / 34 Extreme values of another algebraic function Example Find the extreme values of f (x) = 4 − x2 on [−2, 1]. Solution V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 33 / 34 Notes Notes Notes 10 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010
  • 11. Summary The Extreme Value Theorem: a continuous function on a closed interval must achieve its max and min Fermat’s Theorem: local extrema are critical points The Closed Interval Method: an algorithm for finding global extrema V63.0121.021, Calculus I (NYU) Section 4.1 Maximum and Minimum Values November 9, 2010 34 / 34 Notes Notes Notes 11 Section 4.1 : Maximum and Minimum ValuesV63.0121.021, Calculus I November 9, 2010