The chain rule helps us find a derivative of a composition of functions. It turns out that it's the product of the derivatives of the composed functions.
The chain rule helps us find a derivative of a composition of functions. It turns out that it's the product of the derivatives of the composed functions.
The inverse of a function "undoes" the effect of the function. We look at the implications of that property in the derivative, as well as logarithmic functions, which are inverses of exponential functions.
The inverse of a function "undoes" the effect of the function. We look at the implications of that property in the derivative, as well as logarithmic functions, which are inverses of exponential functions.
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
The chain rule helps us find a derivative of a composition of functions. It turns out that it's the product of the derivatives of the composed functions.
The chain rule helps us find a derivative of a composition of functions. It turns out that it's the product of the derivatives of the composed functions.
The inverse of a function "undoes" the effect of the function. We look at the implications of that property in the derivative, as well as logarithmic functions, which are inverses of exponential functions.
The inverse of a function "undoes" the effect of the function. We look at the implications of that property in the derivative, as well as logarithmic functions, which are inverses of exponential functions.
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
We define what it means for a function to have a maximum or minimum value, and explain the Extreme Value Theorem, which indicates these maxima and minima must be there under certain conditions.
Fermat's Theorem says that at differentiable extreme points, the derivative should be zero, and thus we arrive at a technique for finding extrema: look among the endpoints of the domain of definition and the critical points of the function.
There's also a little digression on Fermat's Last theorem, which is not related to calculus but is a big deal in recent mathematical history.
We define what it means for a function to have a maximum or minimum value, and explain the Extreme Value Theorem, which indicates these maxima and minima must be there under certain conditions.
Fermat's Theorem says that at differentiable extreme points, the derivative should be zero, and thus we arrive at a technique for finding extrema: look among the endpoints of the domain of definition and the critical points of the function.
There's also a little digression on Fermat's Last theorem, which is not related to calculus but is a big deal in recent mathematical history.
We define what it means for a function to have a maximum or minimum value, and explain the Extreme Value Theorem, which indicates these maxima and minima must be there under certain conditions.
Fermat's Theorem says that at differentiable extreme points, the derivative should be zero, and thus we arrive at a technique for finding extrema: look among the endpoints of the domain of definition and the critical points of the function.
There's also a little digression on Fermat's Last theorem, which is not related to calculus but is a big deal in recent mathematical history.
We define what it means for a function to have a maximum or minimum value, and explain the Extreme Value Theorem, which indicates these maxima and minima must be there under certain conditions.
Fermat's Theorem says that at differentiable extreme points, the derivative should be zero, and thus we arrive at a technique for finding extrema: look among the endpoints of the domain of definition and the critical points of the function.
There's also a little digression on Fermat's Last theorem, which is not related to calculus but is a big deal in recent mathematical history.
Using the Mean Value Theorem, we can show the a function is increasing on an interval when its derivative is positive on the interval. Changes in the sign of the derivative detect local extrema. We also can use the second derivative to detect concavity and inflection points. This means that the first and second derivative can be used to classify critical points as local maxima or minima
There's an obvious guess for the rule about the derivative of a product—which happens to be wrong. We explain the correct product rule and why it has to be true, along with the quotient rule. We show how to compute derivatives of additional trigonometric functions and new proofs of the Power Rule.
A functional relationship between y and x can be made explicit. Even if the relation is not functional, however, we can assume the relation usually defines y as a function.
The Mean Value Theorem is the Most Important Theorem in Calculus. It allows us to relate information about the derivative of a function to information about the function itself.
The chain rule helps us find a derivative of a composition of functions. It turns out that it's the product of the derivatives of the composed functions.
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
Computing integrals with Riemann sums is like computing derivatives with limits. The calculus of integrals turns out to come from antidifferentiation. This startling fact is the Second Fundamental Theorem of Calculus!
Computing integrals with Riemann sums is like computing derivatives with limits. The calculus of integrals turns out to come from antidifferentiation. This startling fact is the Second Fundamental Theorem of Calculus!
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
The derivative of a composition of functions is the product of the derivatives of those functions. This rule is important because compositions are so powerful.
Streamlining assessment, feedback, and archival with auto-multiple-choiceMatthew Leingang
Auto-multiple-choice (AMC) is an open-source optical mark recognition software package built with Perl, LaTeX, XML, and sqlite. I use it for all my in-class quizzes and exams. Unique papers are created for each student, fixed-response items are scored automatically, and free-response problems, after manual scoring, have marks recorded in the same process. In the first part of the talk I will discuss AMC’s many features and why I feel it’s ideal for a mathematics course. My contributions to the AMC workflow include some scripts designed to automate the process of returning scored papers
back to students electronically. AMC provides an email gateway, but I have written programs to return graded papers via the DAV protocol to student’s dropboxes on our (Sakai) learning management systems. I will also show how graded papers can be archived, with appropriate metadata tags, into an Evernote notebook.
Integration by substitution is the chain rule in reverse.
NOTE: the final location is section specific. Section 1 (morning) is in SILV 703, Section 11 (afternoon) is in CANT 200
Lesson 24: Areas and Distances, The Definite Integral (handout)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
Lesson 24: Areas and Distances, The Definite Integral (slides)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
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.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Digital Tools and AI for Teaching Learning and Research
Lesson 11: The Chain Rule
1. Section 2.5
The Chain Rule
V63.0121, Calculus I
February 19, 2009
Announcements
Midterm is March 4/5 (75 min., in class, covers 1.1–2.4)
ALEKS is due February 27, 11:59pm
. . . . . .
2. Outline
Compositions
Heuristics
Analogy
The Linear Case
The chain rule
Examples
Related rates of change
. . . . . .
3. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
.
. . . . . .
4. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
. (x)
g
x
. .
g
.
. . . . . .
5. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
. (x)
g
x
. .
g
. f
.
. . . . . .
6. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
. (x) . (g(x))
g f
x
. .
g
. f
.
. . . . . .
7. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
f .g(x) . (g(x))
f
. ◦g
x
. .
g
. f
.
. . . . . .
8. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
f .g(x) . (g(x))
f
. ◦g
x
. .
g
. f
.
Our goal for the day is to understand how the derivative of the
composition of two functions depends on the derivatives of the
individual functions.
. . . . . .
9. Outline
Compositions
Heuristics
Analogy
The Linear Case
The chain rule
Examples
Related rates of change
. . . . . .
10. Analogy
Think about riding a bike. To
go faster you can either:
.
.
Image credit: SpringSun
. . . . . .
11. Analogy
Think about riding a bike. To
go faster you can either:
pedal faster
.
.
Image credit: SpringSun
. . . . . .
12. Analogy
Think about riding a bike. To
go faster you can either:
pedal faster
change gears
.
.
Image credit: SpringSun
. . . . . .
13. Analogy
Think about riding a bike. To
go faster you can either:
pedal faster
change gears
.
The angular position (φ) of the back wheel depends on the position
of the front sprocket (θ):
Rθ
φ(θ) =
r
And so the angular speed of the back wheel depends on the
derivative of this function and the speed of the front wheel.
.
Image credit: SpringSun
. . . . . .
14. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
. . . . . .
15. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
Answer
f(g(x)) = m(m′ x + b′ ) + b = (mm′ )x + (mb′ + b)
. . . . . .
16. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
Answer
f(g(x)) = m(m′ x + b′ ) + b = (mm′ )x + (mb′ + b)
The composition is also linear
. . . . . .
17. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
Answer
f(g(x)) = m(m′ x + b′ ) + b = (mm′ )x + (mb′ + b)
The composition is also linear
The slope of the composition is the product of the slopes of the
two functions.
. . . . . .
18. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
Answer
f(g(x)) = m(m′ x + b′ ) + b = (mm′ )x + (mb′ + b)
The composition is also linear
The slope of the composition is the product of the slopes of the
two functions.
. . . . . .
19. The Linear Case
Question
Let f(x) = mx + b and g(x) = m′ x + b′ . What can you say about the
composition?
Answer
f(g(x)) = m(m′ x + b′ ) + b = (mm′ )x + (mb′ + b)
The composition is also linear
The slope of the composition is the product of the slopes of the
two functions.
The derivative is supposed to be a local linearization of a function. So
there should be an analog of this property in derivatives.
. . . . . .
20. Outline
Compositions
Heuristics
Analogy
The Linear Case
The chain rule
Examples
Related rates of change
. . . . . .
21. Theorem of the day: The chain rule
Theorem
Let f and g be functions, with g differentiable at x and f differentiable at
g(x). Then f ◦ g is differentiable at x and
(f ◦ g)′ (x) = f′ (g(x))g′ (x)
In Leibnizian notation, let y = f(u) and u = g(x). Then
dy dy du
=
dx du dx
. . . . . .
22. Observations
Succinctly, the derivative of a
composition is the product of
the derivatives
.
.
Image credit: ooOJasonOoo
. . . . . .
23. Theorem of the day: The chain rule
Theorem
Let f and g be functions, with g differentiable at x and f differentiable at
g(x). Then f ◦ g is differentiable at x and
(f ◦ g)′ (x) = f′ (g(x))g′ (x)
In Leibnizian notation, let y = f(u) and u = g(x). Then
dy dy du
=
dx du dx
. . . . . .
24. Observations
Succinctly, the derivative of a
composition is the product of
the derivatives
The only complication is
where these derivatives are
evaluated: at the same point
the functions are
.
.
Image credit: ooOJasonOoo
. . . . . .
25. Compositions
See Section 1.2 for review
Definition
If f and g are functions, the composition (f ◦ g)(x) = f(g(x)) means
“do g first, then f.”
f .g(x) . (g(x))
f
. ◦g
x
. .
g
. f
.
. . . . . .
26. Observations
Succinctly, the derivative of a
composition is the product of
the derivatives
The only complication is
where these derivatives are
evaluated: at the same point
the functions are
In Leibniz notation, the Chain
Rule looks like cancellation of
(fake) fractions
.
.
Image credit: ooOJasonOoo
. . . . . .
27. Theorem of the day: The chain rule
Theorem
Let f and g be functions, with g differentiable at x and f differentiable at
g(x). Then f ◦ g is differentiable at x and
(f ◦ g)′ (x) = f′ (g(x))g′ (x)
In Leibnizian notation, let y = f(u) and u = g(x). Then
dy dy du
=
dx du dx
. . . . . .
28. Theorem of the day: The chain rule
Theorem
Let f and g be functions, with g differentiable at x and f differentiable at
g(x). Then f ◦ g is differentiable at x and
(f ◦ g)′ (x) = f′ (g(x))g′ (x)
dy du
In Leibnizian notation, let y = f(u) and u = g(x). .Then
.
du dx
dy dy du
=
dx du dx
. . . . . .
29. Outline
Compositions
Heuristics
Analogy
The Linear Case
The chain rule
Examples
Related rates of change
. . . . . .
30. Example
Example √
3x2 + 1. Find h′ (x).
let h(x) =
. . . . . .
31. Example
Example √
3x2 + 1. Find h′ (x).
let h(x) =
Solution
First, write h as f ◦ g.
. . . . . .
32. Example
Example √
3x2 + 1. Find h′ (x).
let h(x) =
Solution √
u and g(x) = 3x2 + 1.
First, write h as f ◦ g. Let f(u) =
. . . . . .
33. Example
Example √
3x2 + 1. Find h′ (x).
let h(x) =
Solution √
First, write h as f ◦ g. Let f(u) = u and g(x) = 3x2 + 1. Then
f′ (u) = 1 u−1/2 , and g′ (x) = 6x. So
2
h′ (x) = 1 u−1/2 (6x)
2
. . . . . .
34. Example
Example √
3x2 + 1. Find h′ (x).
let h(x) =
Solution √
First, write h as f ◦ g. Let f(u) = u and g(x) = 3x2 + 1. Then
f′ (u) = 1 u−1/2 , and g′ (x) = 6x. So
2
3x
h′ (x) = 1 u−1/2 (6x) = 1 (3x2 + 1)−1/2 (6x) = √
2 2
3x2 + 1
. . . . . .
35. Does order matter?
Example
d d
(sin 4x) and compare it to (4 sin x).
Find
dx dx
. . . . . .
36. Does order matter?
Example
d d
(sin 4x) and compare it to (4 sin x).
Find
dx dx
Solution
For the first, let u = 4x and y = sin(u). Then
dy dy du
· = cos(u) · 4 = 4 cos 4x.
=
dx du dx
. . . . . .
37. Does order matter?
Example
d d
(sin 4x) and compare it to (4 sin x).
Find
dx dx
Solution
For the first, let u = 4x and y = sin(u). Then
dy dy du
· = cos(u) · 4 = 4 cos 4x.
=
dx du dx
For the second, let u = sin x and y = 4u. Then
dy dy du
· = 4 · sin x
=
dx du dx
. . . . . .
38. Order matters!
Example
d d
(sin 4x) and compare it to (4 sin x).
Find
dx dx
Solution
For the first, let u = 4x and y = sin(u). Then
dy dy du
· = cos(u) · 4 = 4 cos 4x.
=
dx du dx
For the second, let u = sin x and y = 4u. Then
dy dy du
· = 4 · sin x
=
dx du dx
. . . . . .
47. Combining techniques
Example
d( 3 )
(x + 1)10 sin(4x2 − 7)
Find
dx
Solution
The “last” part of the function is the product, so we apply the product rule.
Each factor’s derivative requires the chain rule:
. . . . . .
48. Combining techniques
Example
d( 3 )
(x + 1)10 sin(4x2 − 7)
Find
dx
Solution
The “last” part of the function is the product, so we apply the product rule.
Each factor’s derivative requires the chain rule:
d( 3 )
(x + 1)10 · sin(4x2 − 7)
dx
( ) ( )
d3 d
· sin(4x − 7) + (x + 1) · sin(4x − 7)
10 2 3 10 2
= (x + 1)
dx dx
. . . . . .
49. Combining techniques
Example
d( 3 )
(x + 1)10 sin(4x2 − 7)
Find
dx
Solution
The “last” part of the function is the product, so we apply the product rule.
Each factor’s derivative requires the chain rule:
d( 3 )
(x + 1)10 · sin(4x2 − 7)
dx
( ) ( )
d3 d
· sin(4x − 7) + (x + 1) · sin(4x − 7)
10 2 3 10 2
= (x + 1)
dx dx
= 10(x3 + 1)9 (3x2 ) sin(4x2 − 7) + (x3 + 1)10 · cos(4x2 − 7)(8x)
. . . . . .
50. Outline
Compositions
Heuristics
Analogy
The Linear Case
The chain rule
Examples
Related rates of change
. . . . . .
51. Related rates of change
Question
The area of a circle, A = πr2 ,
changes as its radius changes. If
the radius changes with
respect to time, the change in
area with respect to time is
dA
= 2πr
A.
dr
dA dr
= 2πr +
B. .
dt dt
dA dr
= 2πr
C.
dt dt
D. not enough information
.
Image credit: Jim Frazier
. . . . . .
52. Related rates of change
Question
The area of a circle, A = πr2 ,
changes as its radius changes. If
the radius changes with
respect to time, the change in
area with respect to time is
dA
= 2πr
A.
dr
dA dr
= 2πr +
B. .
dt dt
dA dr
= 2πr
C.
dt dt
D. not enough information
.
Image credit: Jim Frazier
. . . . . .