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.
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.
Slides: On the Chi Square and Higher-Order Chi Distances for Approximating f-...Frank Nielsen
Slides for the paper:
On the Chi Square and Higher-Order Chi Distances for Approximating f-Divergences
published in IEEE SPL:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6654274
We provide a comprehensive convergence analysis of the asymptotic preserving implicit-explicit particle-in-cell (IMEX-PIC) methods for the Vlasov–Poisson system with a strong magnetic field. This study is of utmost importance for understanding the behavior of plasmas in magnetic fusion devices such as tokamaks, where such a large magnetic field needs to be applied in order to keep the plasma particles on desired tracks.
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.
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.
Slides: On the Chi Square and Higher-Order Chi Distances for Approximating f-...Frank Nielsen
Slides for the paper:
On the Chi Square and Higher-Order Chi Distances for Approximating f-Divergences
published in IEEE SPL:
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6654274
We provide a comprehensive convergence analysis of the asymptotic preserving implicit-explicit particle-in-cell (IMEX-PIC) methods for the Vlasov–Poisson system with a strong magnetic field. This study is of utmost importance for understanding the behavior of plasmas in magnetic fusion devices such as tokamaks, where such a large magnetic field needs to be applied in order to keep the plasma particles on desired tracks.
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...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.
Image sciences, image processing, image restoration, photo manipulation. Image and videos representation. Digital versus analog imagery. Quantization and sampling. Sources and models of noises in digital CCD imagery: photon, thermal and readout noises. Sources and models of blurs. Convolutions and point spread functions. Overview of other standard models, problems and tasks: salt-and-pepper and impulse noises, half toning, inpainting, super-resolution, compressed sensing, high dynamic range imagery, demosaicing. Short introduction to other types of imagery: SAR, Sonar, ultrasound, CT and MRI. Linear and ill-posed restoration problems.
Implicit differentiation allows us to find slopes of lines tangent to curves that are not graphs of functions. Almost all of the time (yes, that is a mathematical term!) we can assume the curve comprises the graph of a function and differentiate using the chain rule.
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...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.
Image sciences, image processing, image restoration, photo manipulation. Image and videos representation. Digital versus analog imagery. Quantization and sampling. Sources and models of noises in digital CCD imagery: photon, thermal and readout noises. Sources and models of blurs. Convolutions and point spread functions. Overview of other standard models, problems and tasks: salt-and-pepper and impulse noises, half toning, inpainting, super-resolution, compressed sensing, high dynamic range imagery, demosaicing. Short introduction to other types of imagery: SAR, Sonar, ultrasound, CT and MRI. Linear and ill-posed restoration problems.
Implicit differentiation allows us to find slopes of lines tangent to curves that are not graphs of functions. Almost all of the time (yes, that is a mathematical term!) we can assume the curve comprises the graph of a function and differentiate using the chain rule.
Lesson 12: Linear Approximation and Differentials (Section 21 handout)Matthew Leingang
The line tangent to a curve is also the line which best "fits" the curve near that point. So derivatives can be used for approximating complicated functions with simple linear ones. Differentials are another set of notation for the same problem.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
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.
An antiderivative of a function is a function whose derivative is the given function. The problem of antidifferentiation is interesting, complicated, and useful, especially when discussing motion.
This is the handout version to take notes on.
An antiderivative of a function is a function whose derivative is the given function. The problem of antidifferentiation is interesting, complicated, and useful, especially when discussing motion.
This is the slideshow version from class.
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 19: The Mean Value Theorem (Section 021 handout)Matthew Leingang
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.
Lesson 19: The Mean Value Theorem (Section 021 slides)Matthew Leingang
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.
Lesson 19: The Mean Value Theorem (Section 041 handout)Matthew Leingang
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.
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.
Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 23: Antiderivatives (Section 021 handout)
1. Section 4.7
Antiderivatives
V63.0121.021, Calculus I
New York University
November 30, 2010
Announcements
Quiz 5 in recitation this week on §§4.1–4.4
Announcements
Quiz 5 in recitation this
week on §§4.1–4.4
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 2 / 35
Objectives
Given a ”simple“ elementary
function, find a function
whose derivative is that
function.
Remember that a function
whose derivative is zero
along an interval must be
zero along that interval.
Solve problems involving
rectilinear motion.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 3 / 35
Notes
Notes
Notes
1
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
2. Outline
What is an antiderivative?
Tabulating Antiderivatives
Power functions
Combinations
Exponential functions
Trigonometric functions
Antiderivatives of piecewise functions
Finding Antiderivatives Graphically
Rectilinear motion
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 4 / 35
What is an antiderivative?
Definition
Let f be a function. An antiderivative for f is a function F such that
F = f .
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 5 / 35
Hard problem, easy check
Example
Find an antiderivative for f (x) = ln x.
Solution
???
Example
is F(x) = x ln x − x an antiderivative for f (x) = ln x?
Solution
d
dx
(x ln x − x) = 1 · ln x + x ·
1
x
− 1 = ln x "
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 6 / 35
Notes
Notes
Notes
2
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
3. Why the MVT is the MITC
Most Important Theorem In Calculus!
Theorem
Let f = 0 on an interval (a, b). Then f is constant on (a, b).
Proof.
Pick any points x and y in (a, b) with x < y. Then f is continuous on
[x, y] and differentiable on (x, y). By MVT there exists a point z in (x, y)
such that
f (y) − f (x)
y − x
= f (z) =⇒ f (y) = f (x) + f (z)(y − x)
But f (z) = 0, so f (y) = f (x). Since this is true for all x and y in (a, b),
then f is constant.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 7 / 35
When two functions have the same derivative
Theorem
Suppose f and g are two differentiable functions on (a, b) with f = g .
Then f and g differ by a constant. That is, there exists a constant C such
that f (x) = g(x) + C.
Proof.
Let h(x) = f (x) − g(x)
Then h (x) = f (x) − g (x) = 0 on (a, b)
So h(x) = C, a constant
This means f (x) − g(x) = C on (a, b)
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 8 / 35
Outline
What is an antiderivative?
Tabulating Antiderivatives
Power functions
Combinations
Exponential functions
Trigonometric functions
Antiderivatives of piecewise functions
Finding Antiderivatives Graphically
Rectilinear motion
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 9 / 35
Notes
Notes
Notes
3
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
4. Antiderivatives of power functions
Recall that the derivative of a
power function is a power
function.
Fact (The Power Rule)
If f (x) = xr
, then f (x) = rxr−1
.
So in looking for antiderivatives
of power functions, try power
functions!
x
y
f (x) = x2
f (x) = 2x
F(x) = ?
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 10 / 35
Example
Find an antiderivative for the function f (x) = x3
.
Solution
Try a power function F(x) = axr
Then F (x) = arxr−1
, so we want arxr−1
= x3
.
r − 1 = 3 =⇒ r = 4, and ar = 1 =⇒ a =
1
4
.
So F(x) =
1
4
x4
is an antiderivative.
Check:
d
dx
1
4
x4
= 4 ·
1
4
x4−1
= x3
"
Any others? Yes, F(x) =
1
4
x4
+ C is the most general form.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 11 / 35
Extrapolating to general power functions
Fact (The Power Rule for antiderivatives)
If f (x) = xr
, then
F(x) =
1
r + 1
xr+1
is an antiderivative for f . . . as long as r = −1.
Fact
If f (x) = x−1
=
1
x
, then
F(x) = ln |x| + C
is an antiderivative for f .
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 12 / 35
Notes
Notes
Notes
4
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
5. What’s with the absolute value?
F(x) = ln |x| =
ln(x) if x > 0;
ln(−x) if x < 0.
The domain of F is all nonzero numbers, while ln x is only defined on
positive numbers.
If x > 0,
d
dx
ln |x| =
d
dx
ln(x) =
1
x
"
If x < 0,
d
dx
ln |x| =
d
dx
ln(−x) =
1
−x
· (−1) =
1
x
"
We prefer the antiderivative with the larger domain.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 13 / 35
Graph of ln |x|
x
y
f (x) = 1/x
F(x) = ln |x|
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 14 / 35
Combinations of antiderivatives
Fact (Sum and Constant Multiple Rule for Antiderivatives)
If F is an antiderivative of f and G is an antiderivative of g, then
F + G is an antiderivative of f + g.
If F is an antiderivative of f and c is a constant, then cF is an
antiderivative of cf .
Proof.
These follow from the sum and constant multiple rule for derivatives:
If F = f and G = g, then
(F + G) = F + G = f + g
Or, if F = f ,
(cF) = cF = cf
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 15 / 35
Notes
Notes
Notes
5
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
6. Antiderivatives of Polynomials
Example
Find an antiderivative for f (x) = 16x + 5.
Solution
Question
Do we need two C’s or just one?
Answer
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 16 / 35
Exponential Functions
Fact
If f (x) = ax
, f (x) = (ln a)ax
.
Accordingly,
Fact
If f (x) = ax
, then F(x) =
1
ln a
ax
+ C is the antiderivative of f .
Proof.
Check it yourself.
In particular,
Fact
If f (x) = ex
, then F(x) = ex
+ C is the antiderivative of f .
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 17 / 35
Logarithmic functions?
Remember we found
F(x) = x ln x − x
is an antiderivative of f (x) = ln x.
This is not obvious. See Calc II for the full story.
However, using the fact that loga x =
ln x
ln a
, we get:
Fact
If f (x) = loga(x)
F(x) =
1
ln a
(x ln x − x) + C = x loga x −
1
ln a
x + C
is the antiderivative of f (x).
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 18 / 35
Notes
Notes
Notes
6
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
7. Trigonometric functions
Fact
d
dx
sin x = cos x
d
dx
cos x = − sin x
So to turn these around,
Fact
The function F(x) = − cos x + C is the antiderivative of f (x) = sin x.
The function F(x) = sin x + C is the antiderivative of f (x) = cos x.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 19 / 35
More Trig
Example
Find an antiderivative of f (x) = tan x.
Solution
???
Answer
F(x) = ln(sec x).
Check
d
dx
=
1
sec x
·
d
dx
sec x =
1
sec x
· sec x tan x = tan x "
More about this later.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 20 / 35
Antiderivatives of piecewise functions
Example
Let f (x) =
x if 0 ≤ x ≤ 1;
1 − x2
if 1 < x.
Find the antiderivative of f with
F(0) = 1.
Solution
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 21 / 35
Notes
Notes
Notes
7
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
8. V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 22 / 35
Outline
What is an antiderivative?
Tabulating Antiderivatives
Power functions
Combinations
Exponential functions
Trigonometric functions
Antiderivatives of piecewise functions
Finding Antiderivatives Graphically
Rectilinear motion
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 23 / 35
Finding Antiderivatives Graphically
Problem
Below is the graph of a function f . Draw the graph of an antiderivative for
f .
x
y
1 2 3 4 5 6
y = f (x)
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 24 / 35
Notes
Notes
Notes
8
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
9. Using f to make a sign chart for F
Assuming F = f , we can make a sign chart for f and f to find the
intervals of monotonicity and concavity for F:
x
y
1 2 3 4 5 6
f = F
F1 2 3 4 5 6
+ + − − +
max min
f = F
F1 2 3 4 5 6
++ −− −− ++ ++
IP IP
F
shape1 2 3 4 5 6
? ? ? ? ? ?
The only question left is: What are the function values?
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 25 / 35
Could you repeat the question?
Problem
Below is the graph of a function f . Draw the graph of the antiderivative
for f with F(1) = 0.
Solution
We start with F(1) = 0.
Using the sign chart, we
draw arcs with the specified
monotonicity and concavity
It’s harder to tell if/when F
crosses the axis; more about
that later.
x
y
1 2 3 4 5 6
f
F
shape1 2 3 4 5 6
IP
max
IP
min
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 26 / 35
Outline
What is an antiderivative?
Tabulating Antiderivatives
Power functions
Combinations
Exponential functions
Trigonometric functions
Antiderivatives of piecewise functions
Finding Antiderivatives Graphically
Rectilinear motion
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 27 / 35
Notes
Notes
Notes
9
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
10. Say what?
“Rectilinear motion” just means motion along a line.
Often we are given information about the velocity or acceleration of a
moving particle and we want to know the equations of motion.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 28 / 35
Application: Dead Reckoning
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 29 / 35
Problem
Suppose a particle of mass m is acted upon by a constant force F. Find
the position function s(t), the velocity function v(t), and the acceleration
function a(t).
Solution
By Newton’s Second Law (F = ma) a constant force induces a
constant acceleration. So a(t) = a =
F
m
.
Since v (t) = a(t), v(t) must be an antiderivative of the constant
function a. So
v(t) = at + C = at + v0
where v0 is the initial velocity.
Since s (t) = v(t), s(t) must be an antiderivative of v(t), meaning
s(t) =
1
2
at2
+ v0t + C =
1
2
at2
+ v0t + s0
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 30 / 35
Notes
Notes
Notes
10
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
11. An earlier Hatsumon
Example
Drop a ball off the roof of the Silver Center. What is its velocity when it
hits the ground?
Solution
Assume s0 = 100 m, and v0 = 0. Approximate a = g ≈ −10. Then
s(t) = 100 − 5t2
So s(t) = 0 when t =
√
20 = 2
√
5. Then
v(t) = −10t,
so the velocity at impact is v(2
√
5) = −20
√
5 m/s.
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 31 / 35
Finding initial velocity from stopping distance
Example
The skid marks made by an automobile indicate that its brakes were fully
applied for a distance of 160 ft before it came to a stop. Suppose that the
car in question has a constant deceleration of 20 ft/s2 under the conditions
of the skid. How fast was the car traveling when its brakes were first
applied?
Solution (Setup)
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 32 / 35
Implementing the Solution
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 33 / 35
Notes
Notes
Notes
11
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010
12. Solving
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 34 / 35
Summary
Antiderivatives are a useful
concept, especially in motion
We can graph an
antiderivative from the
graph of a function
We can compute
antiderivatives, but not
always
x
y
1 2 3 4 5 6
f
F
f (x) = e−x2
f (x) = ???
V63.0121.021, Calculus I (NYU) Section 4.7 Antiderivatives November 30, 2010 35 / 35
Notes
Notes
Notes
12
Section 4.7 : AntiderivativesV63.0121.021, Calculus I November 30, 2010