SlideShare a Scribd company logo
1 of 65
Download to read offline
Section 3.3
Derivatives of Exponential and
Logarithmic Functions
V63.0121.041, Calculus I
New York University
October 25, 2010
Announcements
Midterm is graded. Please see FAQ.
Quiz 3 next week on 2.6, 2.8, 3.1, 3.2
. . . . . .
. . . . . .
Announcements
Midterm is graded. Please
see FAQ.
Quiz 3 next week on 2.6,
2.8, 3.1, 3.2
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 2 / 34
. . . . . .
Objectives
Know the derivatives of the
exponential functions (with
any base)
Know the derivatives of the
logarithmic functions (with
any base)
Use the technique of
logarithmic differentiation
to find derivatives of
functions involving roducts,
quotients, and/or
exponentials.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 3 / 34
. . . . . .
Outline
Recall Section 3.1–3.2
Derivative of the natural exponential function
Exponential Growth
Derivative of the natural logarithm function
Derivatives of other exponentials and logarithms
Other exponentials
Other logarithms
Logarithmic Differentiation
The power rule for irrational powers
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 4 / 34
. . . . . .
Conventions on power expressions
Let a be a positive real number.
If n is a positive whole number, then an
= a · a · · · · · a
n factors
a0
= 1.
For any real number r, a−r
=
1
ar
.
For any positive whole number n, a1/n
= n
√
a.
There is only one continuous function which satisfies all of the above.
We call it the exponential function with base a.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 5 / 34
. . . . . .
Properties of exponential Functions
Theorem
If a > 0 and a ̸= 1, then f(x) = ax
is a continuous function with domain
(−∞, ∞) and range (0, ∞). In particular, ax
> 0 for all x. For any real
numbers x and y, and positive numbers a and b we have
ax+y
= ax
ay
ax−y
=
ax
ay
(ax
)y
= axy
(ab)x
= ax
bx
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
. . . . . .
Properties of exponential Functions
Theorem
If a > 0 and a ̸= 1, then f(x) = ax
is a continuous function with domain
(−∞, ∞) and range (0, ∞). In particular, ax
> 0 for all x. For any real
numbers x and y, and positive numbers a and b we have
ax+y
= ax
ay
ax−y
=
ax
ay
(negative exponents mean reciprocals)
(ax
)y
= axy
(ab)x
= ax
bx
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
. . . . . .
Properties of exponential Functions
Theorem
If a > 0 and a ̸= 1, then f(x) = ax
is a continuous function with domain
(−∞, ∞) and range (0, ∞). In particular, ax
> 0 for all x. For any real
numbers x and y, and positive numbers a and b we have
ax+y
= ax
ay
ax−y
=
ax
ay
(negative exponents mean reciprocals)
(ax
)y
= axy
(fractional exponents mean roots)
(ab)x
= ax
bx
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
. . . . . .
Graphs of various exponential functions
. .x
.y
.y = 1x
.y = 2x.y = 3x
.y = 10x
.y = 1.5x
.y = (1/2)x.y = (1/3)x
.y = (1/10)x
.y = (2/3)x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 7 / 34
. . . . . .
The magic number
Definition
e = lim
n→∞
(
1 +
1
n
)n
= lim
h→0+
(1 + h)1/h
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 8 / 34
. . . . . .
Existence of e
See Appendix B
We can experimentally
verify that this number
exists and is
e ≈ 2.718281828459045 . . .
e is irrational
e is transcendental
n
(
1 +
1
n
)n
1 2
2 2.25
3 2.37037
10 2.59374
100 2.70481
1000 2.71692
106
2.71828
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 9 / 34
. . . . . .
Logarithms
Definition
The base a logarithm loga x is the inverse of the function ax
y = loga x ⇐⇒ x = ay
The natural logarithm ln x is the inverse of ex
. So
y = ln x ⇐⇒ x = ey
.
Facts
(i) loga(x1 · x2) = loga x1 + loga x2
(ii) loga
(
x1
x2
)
= loga x1 − loga x2
(iii) loga(xr
) = r loga x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 10 / 34
. . . . . .
Graphs of logarithmic functions
. .x
.y
.y = 2x
.y = log2 x
. .(0, 1)
..(1, 0)
.y = 3x
.y = log3 x
.y = 10x
.y = log10 x
.y = ex
.y = ln x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 11 / 34
. . . . . .
Change of base formula for logarithms
Fact
If a > 0 and a ̸= 1, and the same for b, then
loga x =
logb x
logb a
Proof.
If y = loga x, then x = ay
So logb x = logb(ay
) = y logb a
Therefore
y = loga x =
logb x
logb a
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 12 / 34
. . . . . .
Upshot of changing base
The point of the change of base formula
loga x =
logb x
logb a
=
1
logb a
· logb x = (constant) · logb x
is that all the logarithmic functions are multiples of each other. So just
pick one and call it your favorite.
Engineers like the common logarithm log = log10
Computer scientists like the binary logarithm lg = log2
Mathematicians like natural logarithm ln = loge
Naturally, we will follow the mathematicians. Just don’t pronounce it
“lawn.”
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 13 / 34
. . . . . .
Outline
Recall Section 3.1–3.2
Derivative of the natural exponential function
Exponential Growth
Derivative of the natural logarithm function
Derivatives of other exponentials and logarithms
Other exponentials
Other logarithms
Logarithmic Differentiation
The power rule for irrational powers
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 14 / 34
. . . . . .
Derivatives of Exponential Functions
Fact
If f(x) = ax
, then f′
(x) = f′
(0)ax
.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
. . . . . .
Derivatives of Exponential Functions
Fact
If f(x) = ax
, then f′
(x) = f′
(0)ax
.
Proof.
Follow your nose:
f′
(x) = lim
h→0
f(x + h) − f(x)
h
= lim
h→0
ax+h − ax
h
= lim
h→0
axah − ax
h
= ax
· lim
h→0
ah − 1
h
= ax
· f′
(0).
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
. . . . . .
Derivatives of Exponential Functions
Fact
If f(x) = ax
, then f′
(x) = f′
(0)ax
.
Proof.
Follow your nose:
f′
(x) = lim
h→0
f(x + h) − f(x)
h
= lim
h→0
ax+h − ax
h
= lim
h→0
axah − ax
h
= ax
· lim
h→0
ah − 1
h
= ax
· f′
(0).
To reiterate: the derivative of an exponential function is a constant
times that function. Much different from polynomials!
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
. . . . . .
The funny limit in the case of e
Remember the definition of e:
e = lim
n→∞
(
1 +
1
n
)n
= lim
h→0
(1 + h)1/h
Question
What is lim
h→0
eh − 1
h
?
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
. . . . . .
The funny limit in the case of e
Remember the definition of e:
e = lim
n→∞
(
1 +
1
n
)n
= lim
h→0
(1 + h)1/h
Question
What is lim
h→0
eh − 1
h
?
Answer
If h is small enough, e ≈ (1 + h)1/h
. So
eh − 1
h
≈
[
(1 + h)1/h
]h
− 1
h
=
(1 + h) − 1
h
=
h
h
= 1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
. . . . . .
The funny limit in the case of e
Remember the definition of e:
e = lim
n→∞
(
1 +
1
n
)n
= lim
h→0
(1 + h)1/h
Question
What is lim
h→0
eh − 1
h
?
Answer
If h is small enough, e ≈ (1 + h)1/h
. So
eh − 1
h
≈
[
(1 + h)1/h
]h
− 1
h
=
(1 + h) − 1
h
=
h
h
= 1
So in the limit we get equality: lim
h→0
eh − 1
h
= 1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
. . . . . .
Derivative of the natural exponential function
From
d
dx
ax
=
(
lim
h→0
ah − 1
h
)
ax
and lim
h→0
eh − 1
h
= 1
we get:
Theorem
d
dx
ex
= ex
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 17 / 34
. . . . . .
Exponential Growth
Commonly misused term to say something grows exponentially
It means the rate of change (derivative) is proportional to the
current value
Examples: Natural population growth, compounded interest,
social networks
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 18 / 34
. . . . . .
Examples
Examples
Find derivatives of these functions:
e3x
ex2
x2
ex
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
. . . . . .
Examples
Examples
Find derivatives of these functions:
e3x
ex2
x2
ex
Solution
d
dx
e3x
= 3e3x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
. . . . . .
Examples
Examples
Find derivatives of these functions:
e3x
ex2
x2
ex
Solution
d
dx
e3x
= 3e3x
d
dx
ex2
= ex2 d
dx
(x2
) = 2xex2
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
. . . . . .
Examples
Examples
Find derivatives of these functions:
e3x
ex2
x2
ex
Solution
d
dx
e3x
= 3e3x
d
dx
ex2
= ex2 d
dx
(x2
) = 2xex2
d
dx
x2
ex
= 2xex
+ x2
ex
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
. . . . . .
Outline
Recall Section 3.1–3.2
Derivative of the natural exponential function
Exponential Growth
Derivative of the natural logarithm function
Derivatives of other exponentials and logarithms
Other exponentials
Other logarithms
Logarithmic Differentiation
The power rule for irrational powers
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 20 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
ey dy
dx
= 1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
ey dy
dx
= 1
=⇒
dy
dx
=
1
ey
=
1
x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
ey dy
dx
= 1
=⇒
dy
dx
=
1
ey
=
1
x
We have discovered:
Fact
d
dx
ln x =
1
x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
ey dy
dx
= 1
=⇒
dy
dx
=
1
ey
=
1
x
We have discovered:
Fact
d
dx
ln x =
1
x
. .x
.y
.ln x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
Derivative of the natural logarithm function
Let y = ln x. Then x = ey
so
ey dy
dx
= 1
=⇒
dy
dx
=
1
ey
=
1
x
We have discovered:
Fact
d
dx
ln x =
1
x
. .x
.y
.ln x
.
1
x
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
. . . . . .
The Tower of Powers
y y′
x3
3x2
x2
2x1
x1
1x0
x0
0
? ?
x−1
−1x−2
x−2
−2x−3
The derivative of a power
function is a power function
of one lower power
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
. . . . . .
The Tower of Powers
y y′
x3
3x2
x2
2x1
x1
1x0
x0
0
? x−1
x−1
−1x−2
x−2
−2x−3
The derivative of a power
function is a power function
of one lower power
Each power function is the
derivative of another power
function, except x−1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
. . . . . .
The Tower of Powers
y y′
x3
3x2
x2
2x1
x1
1x0
x0
0
ln x x−1
x−1
−1x−2
x−2
−2x−3
The derivative of a power
function is a power function
of one lower power
Each power function is the
derivative of another power
function, except x−1
ln x fills in this gap
precisely.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
. . . . . .
Outline
Recall Section 3.1–3.2
Derivative of the natural exponential function
Exponential Growth
Derivative of the natural logarithm function
Derivatives of other exponentials and logarithms
Other exponentials
Other logarithms
Logarithmic Differentiation
The power rule for irrational powers
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 23 / 34
. . . . . .
Other logarithms
Example
Use implicit differentiation to find
d
dx
ax
.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
. . . . . .
Other logarithms
Example
Use implicit differentiation to find
d
dx
ax
.
Solution
Let y = ax
, so
ln y = ln ax
= x ln a
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
. . . . . .
Other logarithms
Example
Use implicit differentiation to find
d
dx
ax
.
Solution
Let y = ax
, so
ln y = ln ax
= x ln a
Differentiate implicitly:
1
y
dy
dx
= ln a =⇒
dy
dx
= (ln a)y = (ln a)ax
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
. . . . . .
Other logarithms
Example
Use implicit differentiation to find
d
dx
ax
.
Solution
Let y = ax
, so
ln y = ln ax
= x ln a
Differentiate implicitly:
1
y
dy
dx
= ln a =⇒
dy
dx
= (ln a)y = (ln a)ax
Before we showed y′
= y′
(0)y, so now we know that
ln a = lim
h→0
ah − 1
h
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
. . . . . .
Other logarithms
Example
Find
d
dx
loga x.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
. . . . . .
Other logarithms
Example
Find
d
dx
loga x.
Solution
Let y = loga x, so ay
= x.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
. . . . . .
Other logarithms
Example
Find
d
dx
loga x.
Solution
Let y = loga x, so ay
= x. Now differentiate implicitly:
(ln a)ay dy
dx
= 1 =⇒
dy
dx
=
1
ay ln a
=
1
x ln a
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
. . . . . .
Other logarithms
Example
Find
d
dx
loga x.
Solution
Let y = loga x, so ay
= x. Now differentiate implicitly:
(ln a)ay dy
dx
= 1 =⇒
dy
dx
=
1
ay ln a
=
1
x ln a
Another way to see this is to take the natural logarithm:
ay
= x =⇒ y ln a = ln x =⇒ y =
ln x
ln a
So
dy
dx
=
1
ln a
1
x
.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
. . . . . .
More examples
Example
Find
d
dx
log2(x2
+ 1)
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 26 / 34
. . . . . .
More examples
Example
Find
d
dx
log2(x2
+ 1)
Answer
dy
dx
=
1
ln 2
1
x2 + 1
(2x) =
2x
(ln 2)(x2 + 1)
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 26 / 34
. . . . . .
Outline
Recall Section 3.1–3.2
Derivative of the natural exponential function
Exponential Growth
Derivative of the natural logarithm function
Derivatives of other exponentials and logarithms
Other exponentials
Other logarithms
Logarithmic Differentiation
The power rule for irrational powers
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 27 / 34
. . . . . .
A nasty derivative
Example
Let y =
(x2 + 1)
√
x + 3
x − 1
. Find y′
.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 28 / 34
. . . . . .
A nasty derivative
Example
Let y =
(x2 + 1)
√
x + 3
x − 1
. Find y′
.
Solution
We use the quotient rule, and the product rule in the numerator:
y′
=
(x − 1)
[
2x
√
x + 3 + (x2 + 1)1
2 (x + 3)−1/2
]
− (x2 + 1)
√
x + 3(1)
(x − 1)2
=
2x
√
x + 3
(x − 1)
+
(x2 + 1)
2
√
x + 3(x − 1)
−
(x2 + 1)
√
x + 3
(x − 1)2
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 28 / 34
. . . . . .
Another way
y =
(x2 + 1)
√
x + 3
x − 1
ln y = ln(x2
+ 1) +
1
2
ln(x + 3) − ln(x − 1)
1
y
dy
dx
=
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
So
dy
dx
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
y
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
(x2 + 1)
√
x + 3
x − 1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 29 / 34
. . . . . .
Compare and contrast
Using the product, quotient, and power rules:
y′
=
2x
√
x + 3
(x − 1)
+
(x2 + 1)
2
√
x + 3(x − 1)
−
(x2 + 1)
√
x + 3
(x − 1)2
Using logarithmic differentiation:
y′
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
(x2 + 1)
√
x + 3
x − 1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
. . . . . .
Compare and contrast
Using the product, quotient, and power rules:
y′
=
2x
√
x + 3
(x − 1)
+
(x2 + 1)
2
√
x + 3(x − 1)
−
(x2 + 1)
√
x + 3
(x − 1)2
Using logarithmic differentiation:
y′
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
(x2 + 1)
√
x + 3
x − 1
Are these the same?
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
. . . . . .
Compare and contrast
Using the product, quotient, and power rules:
y′
=
2x
√
x + 3
(x − 1)
+
(x2 + 1)
2
√
x + 3(x − 1)
−
(x2 + 1)
√
x + 3
(x − 1)2
Using logarithmic differentiation:
y′
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
(x2 + 1)
√
x + 3
x − 1
Are these the same?
Which do you like better?
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
. . . . . .
Compare and contrast
Using the product, quotient, and power rules:
y′
=
2x
√
x + 3
(x − 1)
+
(x2 + 1)
2
√
x + 3(x − 1)
−
(x2 + 1)
√
x + 3
(x − 1)2
Using logarithmic differentiation:
y′
=
(
2x
x2 + 1
+
1
2(x + 3)
−
1
x − 1
)
(x2 + 1)
√
x + 3
x − 1
Are these the same?
Which do you like better?
What kinds of expressions are well-suited for logarithmic
differentiation?
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
. . . . . .
Derivatives of powers
.
.
Question
Let y = xx
. Which of these is true?
(A) Since y is a power function,
y′
= x · xx−1
= xx
.
(B) Since y is an exponential
function, y′
= (ln x) · xx
(C) Neither .
.x
.y
.
.1
..1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 31 / 34
. . . . . .
Derivatives of powers
.
.
Question
Let y = xx
. Which of these is true?
(A) Since y is a power function,
y′
= x · xx−1
= xx
.
(B) Since y is an exponential
function, y′
= (ln x) · xx
(C) Neither .
.x
.y
.
.1
..1
Answer
(A) This can’t be y′
because xx
> 0 for all x > 0, and this function decreases
at some places
(B) This can’t be y′
because (ln x)xx
= 0 when x = 1, and this function does
not have a horizontal tangent at x = 1.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 31 / 34
. . . . . .
It's neither! Or both?
Solution
If y = xx
, then
ln y = x ln x
1
y
dy
dx
= x ·
1
x
+ ln x = 1 + ln x
dy
dx
= (1 + ln x)xx
= xx
+ (ln x)xx
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
. . . . . .
It's neither! Or both?
Solution
If y = xx
, then
ln y = x ln x
1
y
dy
dx
= x ·
1
x
+ ln x = 1 + ln x
dy
dx
= (1 + ln x)xx
= xx
+ (ln x)xx
Remarks
Each of these terms is one of the wrong answers!
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
. . . . . .
It's neither! Or both?
Solution
If y = xx
, then
ln y = x ln x
1
y
dy
dx
= x ·
1
x
+ ln x = 1 + ln x
dy
dx
= (1 + ln x)xx
= xx
+ (ln x)xx
Remarks
Each of these terms is one of the wrong answers!
y′
< 0 on the interval (0, e−1
)
y′
= 0 when x = e−1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
. . . . . .
Derivatives of power functions with any exponent
Fact (The power rule)
Let y = xr
. Then y′
= rxr−1
.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 33 / 34
. . . . . .
Derivatives of power functions with any exponent
Fact (The power rule)
Let y = xr
. Then y′
= rxr−1
.
Proof.
y = xr
=⇒ ln y = r ln x
Now differentiate:
1
y
dy
dx
=
r
x
=⇒
dy
dx
= r
y
x
= rxr−1
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 33 / 34
. . . . . .
Summary
Derivatives of logarithmic and exponential functions:
y y′
ex
ex
ax
(ln a) · ax
ln x
1
x
loga x
1
ln a
·
1
x
Logarithmic Differentiation can allow us to avoid the product and
quotient rules.
V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 34 / 34

More Related Content

What's hot

Numerical
NumericalNumerical
Numerical1821986
 
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
 
1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear SystemsCeni Babaoglu, PhD
 
13 recursion-120712074623-phpapp02
13 recursion-120712074623-phpapp0213 recursion-120712074623-phpapp02
13 recursion-120712074623-phpapp02Abdul Samee
 
3.2.interpolation lagrange
3.2.interpolation lagrange3.2.interpolation lagrange
3.2.interpolation lagrangeSamuelOseiAsare
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear TransformationsCeni Babaoglu, PhD
 
2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and DimensionCeni Babaoglu, PhD
 
2. polynomial interpolation
2. polynomial interpolation2. polynomial interpolation
2. polynomial interpolationEasyStudy3
 
Eigenvalue eigenvector slides
Eigenvalue eigenvector slidesEigenvalue eigenvector slides
Eigenvalue eigenvector slidesAmanSaeed11
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Pierre Jacob
 
Multiattribute Decision Making
Multiattribute Decision MakingMultiattribute Decision Making
Multiattribute Decision MakingArthur Charpentier
 
Computer Science and Information Science 4th semester (2012-June Question
Computer Science and Information Science 4th semester (2012-June Question Computer Science and Information Science 4th semester (2012-June Question
Computer Science and Information Science 4th semester (2012-June Question B G S Institute of Technolgy
 
Banco de preguntas para el ap
Banco de preguntas para el apBanco de preguntas para el ap
Banco de preguntas para el apMARCELOCHAVEZ23
 
Cbse Class 12 Maths Sample Paper 2012
Cbse Class 12 Maths Sample Paper 2012Cbse Class 12 Maths Sample Paper 2012
Cbse Class 12 Maths Sample Paper 2012Sunaina Rawat
 
Lesson 19: Curve Sketching
Lesson 19: Curve SketchingLesson 19: Curve Sketching
Lesson 19: Curve SketchingMatthew Leingang
 

What's hot (19)

Numerical
NumericalNumerical
Numerical
 
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
 
Numerical method (curve fitting)
Numerical method (curve fitting)Numerical method (curve fitting)
Numerical method (curve fitting)
 
1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems1. Linear Algebra for Machine Learning: Linear Systems
1. Linear Algebra for Machine Learning: Linear Systems
 
13 recursion-120712074623-phpapp02
13 recursion-120712074623-phpapp0213 recursion-120712074623-phpapp02
13 recursion-120712074623-phpapp02
 
3.2.interpolation lagrange
3.2.interpolation lagrange3.2.interpolation lagrange
3.2.interpolation lagrange
 
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
3. Linear Algebra for Machine Learning: Factorization and Linear Transformations
 
Lecture50
Lecture50Lecture50
Lecture50
 
2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension2. Linear Algebra for Machine Learning: Basis and Dimension
2. Linear Algebra for Machine Learning: Basis and Dimension
 
2. polynomial interpolation
2. polynomial interpolation2. polynomial interpolation
2. polynomial interpolation
 
Eigenvalue eigenvector slides
Eigenvalue eigenvector slidesEigenvalue eigenvector slides
Eigenvalue eigenvector slides
 
Lec3
Lec3Lec3
Lec3
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...
 
Es272 ch5b
Es272 ch5bEs272 ch5b
Es272 ch5b
 
Multiattribute Decision Making
Multiattribute Decision MakingMultiattribute Decision Making
Multiattribute Decision Making
 
Computer Science and Information Science 4th semester (2012-June Question
Computer Science and Information Science 4th semester (2012-June Question Computer Science and Information Science 4th semester (2012-June Question
Computer Science and Information Science 4th semester (2012-June Question
 
Banco de preguntas para el ap
Banco de preguntas para el apBanco de preguntas para el ap
Banco de preguntas para el ap
 
Cbse Class 12 Maths Sample Paper 2012
Cbse Class 12 Maths Sample Paper 2012Cbse Class 12 Maths Sample Paper 2012
Cbse Class 12 Maths Sample Paper 2012
 
Lesson 19: Curve Sketching
Lesson 19: Curve SketchingLesson 19: Curve Sketching
Lesson 19: Curve Sketching
 

Viewers also liked

Derivatives of Trig. Functions
Derivatives of Trig. FunctionsDerivatives of Trig. Functions
Derivatives of Trig. Functionscalculusgroup3
 
Basic Rules & Theorems for Differentiation
Basic Rules & Theorems for DifferentiationBasic Rules & Theorems for Differentiation
Basic Rules & Theorems for DifferentiationChristopher Gratton
 
Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Matthew Leingang
 
Lesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric FunctionsLesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric FunctionsMatthew Leingang
 
Lesson14: Derivatives of Trigonometric Functions
Lesson14: Derivatives of Trigonometric FunctionsLesson14: Derivatives of Trigonometric Functions
Lesson14: Derivatives of Trigonometric FunctionsMatthew Leingang
 
Lecture 8 derivative rules
Lecture 8   derivative rulesLecture 8   derivative rules
Lecture 8 derivative rulesnjit-ronbrown
 
Basic Rules Of Differentiation
Basic Rules Of DifferentiationBasic Rules Of Differentiation
Basic Rules Of Differentiationseltzermath
 
Lesson 16: Implicit Differentiation
Lesson 16: Implicit DifferentiationLesson 16: Implicit Differentiation
Lesson 16: Implicit DifferentiationMatthew Leingang
 
3.5 Derivatives Of Trig Functions
3.5 Derivatives Of Trig Functions3.5 Derivatives Of Trig Functions
3.5 Derivatives Of Trig Functionsricmac25
 

Viewers also liked (10)

Derivatives of Trig. Functions
Derivatives of Trig. FunctionsDerivatives of Trig. Functions
Derivatives of Trig. Functions
 
Basic Rules & Theorems for Differentiation
Basic Rules & Theorems for DifferentiationBasic Rules & Theorems for Differentiation
Basic Rules & Theorems for Differentiation
 
Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)
 
Lesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric FunctionsLesson 10: Derivatives of Trigonometric Functions
Lesson 10: Derivatives of Trigonometric Functions
 
Derivatives
DerivativesDerivatives
Derivatives
 
Lesson14: Derivatives of Trigonometric Functions
Lesson14: Derivatives of Trigonometric FunctionsLesson14: Derivatives of Trigonometric Functions
Lesson14: Derivatives of Trigonometric Functions
 
Lecture 8 derivative rules
Lecture 8   derivative rulesLecture 8   derivative rules
Lecture 8 derivative rules
 
Basic Rules Of Differentiation
Basic Rules Of DifferentiationBasic Rules Of Differentiation
Basic Rules Of Differentiation
 
Lesson 16: Implicit Differentiation
Lesson 16: Implicit DifferentiationLesson 16: Implicit Differentiation
Lesson 16: Implicit Differentiation
 
3.5 Derivatives Of Trig Functions
3.5 Derivatives Of Trig Functions3.5 Derivatives Of Trig Functions
3.5 Derivatives Of Trig Functions
 

Similar to Derivatives of Exponential and Log Functions

Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Mel Anthony Pepito
 
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...Mel Anthony Pepito
 
Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Matthew Leingang
 
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)Lesson 16: Inverse Trigonometric Functions (Section 041 handout)
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)Matthew Leingang
 
Lesson 23: Antiderivatives (Section 041 slides)
Lesson 23: Antiderivatives (Section 041 slides)Lesson 23: Antiderivatives (Section 041 slides)
Lesson 23: Antiderivatives (Section 041 slides)Matthew Leingang
 
Lesson15 -exponential_growth_and_decay_021_slides
Lesson15  -exponential_growth_and_decay_021_slidesLesson15  -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slidesMatthew Leingang
 
Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Matthew Leingang
 
Lesson16 -inverse_trigonometric_functions_021_handout
Lesson16  -inverse_trigonometric_functions_021_handoutLesson16  -inverse_trigonometric_functions_021_handout
Lesson16 -inverse_trigonometric_functions_021_handoutMatthew Leingang
 
Lesson16 -inverse_trigonometric_functions_021_handout
Lesson16  -inverse_trigonometric_functions_021_handoutLesson16  -inverse_trigonometric_functions_021_handout
Lesson16 -inverse_trigonometric_functions_021_handoutMatthew Leingang
 
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)Matthew Leingang
 
Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Mel Anthony Pepito
 
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)Lesson 16: Inverse Trigonometric Functions (Section 041 slides)
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)Matthew Leingang
 
Lesson16 -inverse_trigonometric_functions_041_slides
Lesson16  -inverse_trigonometric_functions_041_slidesLesson16  -inverse_trigonometric_functions_041_slides
Lesson16 -inverse_trigonometric_functions_041_slidesMatthew Leingang
 
Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Mel Anthony Pepito
 
Lesson15 -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slidesLesson15 -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slidesMel Anthony Pepito
 
66 calculation with log and exp
66 calculation with log and exp66 calculation with log and exp
66 calculation with log and expmath126
 
Lesson 23: Antiderivatives (Section 021 slides)
Lesson 23: Antiderivatives (Section 021 slides)Lesson 23: Antiderivatives (Section 021 slides)
Lesson 23: Antiderivatives (Section 021 slides)Matthew Leingang
 
Lesson 23: Antiderivatives (Section 021 handout)
Lesson 23: Antiderivatives (Section 021 handout)Lesson 23: Antiderivatives (Section 021 handout)
Lesson 23: Antiderivatives (Section 021 handout)Matthew Leingang
 
Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)Matthew Leingang
 

Similar to Derivatives of Exponential and Log Functions (20)

Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 041 ...
 
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...
Lesson 14: Derivatives of Exponential and Logarithmic Functions (Section 021 ...
 
Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)
 
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)Lesson 16: Inverse Trigonometric Functions (Section 041 handout)
Lesson 16: Inverse Trigonometric Functions (Section 041 handout)
 
Lesson 23: Antiderivatives (Section 041 slides)
Lesson 23: Antiderivatives (Section 041 slides)Lesson 23: Antiderivatives (Section 041 slides)
Lesson 23: Antiderivatives (Section 041 slides)
 
Lesson15 -exponential_growth_and_decay_021_slides
Lesson15  -exponential_growth_and_decay_021_slidesLesson15  -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slides
 
Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)
 
Lesson16 -inverse_trigonometric_functions_021_handout
Lesson16  -inverse_trigonometric_functions_021_handoutLesson16  -inverse_trigonometric_functions_021_handout
Lesson16 -inverse_trigonometric_functions_021_handout
 
Lesson16 -inverse_trigonometric_functions_021_handout
Lesson16  -inverse_trigonometric_functions_021_handoutLesson16  -inverse_trigonometric_functions_021_handout
Lesson16 -inverse_trigonometric_functions_021_handout
 
chapter3.ppt
chapter3.pptchapter3.ppt
chapter3.ppt
 
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)
 
Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)Lesson 15: Exponential Growth and Decay (Section 041 slides)
Lesson 15: Exponential Growth and Decay (Section 041 slides)
 
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)Lesson 16: Inverse Trigonometric Functions (Section 041 slides)
Lesson 16: Inverse Trigonometric Functions (Section 041 slides)
 
Lesson16 -inverse_trigonometric_functions_041_slides
Lesson16  -inverse_trigonometric_functions_041_slidesLesson16  -inverse_trigonometric_functions_041_slides
Lesson16 -inverse_trigonometric_functions_041_slides
 
Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)Lesson 15: Exponential Growth and Decay (Section 021 slides)
Lesson 15: Exponential Growth and Decay (Section 021 slides)
 
Lesson15 -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slidesLesson15 -exponential_growth_and_decay_021_slides
Lesson15 -exponential_growth_and_decay_021_slides
 
66 calculation with log and exp
66 calculation with log and exp66 calculation with log and exp
66 calculation with log and exp
 
Lesson 23: Antiderivatives (Section 021 slides)
Lesson 23: Antiderivatives (Section 021 slides)Lesson 23: Antiderivatives (Section 021 slides)
Lesson 23: Antiderivatives (Section 021 slides)
 
Lesson 23: Antiderivatives (Section 021 handout)
Lesson 23: Antiderivatives (Section 021 handout)Lesson 23: Antiderivatives (Section 021 handout)
Lesson 23: Antiderivatives (Section 021 handout)
 
Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)Lesson 23: Antiderivatives (Section 041 handout)
Lesson 23: Antiderivatives (Section 041 handout)
 

More from Matthew Leingang

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-choiceMatthew Leingang
 
Electronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsElectronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsMatthew Leingang
 
Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 
Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Matthew Leingang
 
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)Matthew Leingang
 
Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Matthew Leingang
 
Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Matthew Leingang
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Matthew Leingang
 
Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Matthew Leingang
 
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)Matthew Leingang
 
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)Matthew Leingang
 

More from 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)
 

Recently uploaded

Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?XfilesPro
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
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 MenDelhi Call girls
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksSoftradix Technologies
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 

Recently uploaded (20)

Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?How to Remove Document Management Hurdles with X-Docs?
How to Remove Document Management Hurdles with X-Docs?
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptxVulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
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
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
Benefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other FrameworksBenefits Of Flutter Compared To Other Frameworks
Benefits Of Flutter Compared To Other Frameworks
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 

Derivatives of Exponential and Log Functions

  • 1. Section 3.3 Derivatives of Exponential and Logarithmic Functions V63.0121.041, Calculus I New York University October 25, 2010 Announcements Midterm is graded. Please see FAQ. Quiz 3 next week on 2.6, 2.8, 3.1, 3.2 . . . . . .
  • 2. . . . . . . Announcements Midterm is graded. Please see FAQ. Quiz 3 next week on 2.6, 2.8, 3.1, 3.2 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 2 / 34
  • 3. . . . . . . Objectives Know the derivatives of the exponential functions (with any base) Know the derivatives of the logarithmic functions (with any base) Use the technique of logarithmic differentiation to find derivatives of functions involving roducts, quotients, and/or exponentials. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 3 / 34
  • 4. . . . . . . Outline Recall Section 3.1–3.2 Derivative of the natural exponential function Exponential Growth Derivative of the natural logarithm function Derivatives of other exponentials and logarithms Other exponentials Other logarithms Logarithmic Differentiation The power rule for irrational powers V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 4 / 34
  • 5. . . . . . . Conventions on power expressions Let a be a positive real number. If n is a positive whole number, then an = a · a · · · · · a n factors a0 = 1. For any real number r, a−r = 1 ar . For any positive whole number n, a1/n = n √ a. There is only one continuous function which satisfies all of the above. We call it the exponential function with base a. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 5 / 34
  • 6. . . . . . . Properties of exponential Functions Theorem If a > 0 and a ̸= 1, then f(x) = ax is a continuous function with domain (−∞, ∞) and range (0, ∞). In particular, ax > 0 for all x. For any real numbers x and y, and positive numbers a and b we have ax+y = ax ay ax−y = ax ay (ax )y = axy (ab)x = ax bx V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
  • 7. . . . . . . Properties of exponential Functions Theorem If a > 0 and a ̸= 1, then f(x) = ax is a continuous function with domain (−∞, ∞) and range (0, ∞). In particular, ax > 0 for all x. For any real numbers x and y, and positive numbers a and b we have ax+y = ax ay ax−y = ax ay (negative exponents mean reciprocals) (ax )y = axy (ab)x = ax bx V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
  • 8. . . . . . . Properties of exponential Functions Theorem If a > 0 and a ̸= 1, then f(x) = ax is a continuous function with domain (−∞, ∞) and range (0, ∞). In particular, ax > 0 for all x. For any real numbers x and y, and positive numbers a and b we have ax+y = ax ay ax−y = ax ay (negative exponents mean reciprocals) (ax )y = axy (fractional exponents mean roots) (ab)x = ax bx V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 6 / 34
  • 9. . . . . . . Graphs of various exponential functions . .x .y .y = 1x .y = 2x.y = 3x .y = 10x .y = 1.5x .y = (1/2)x.y = (1/3)x .y = (1/10)x .y = (2/3)x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 7 / 34
  • 10. . . . . . . The magic number Definition e = lim n→∞ ( 1 + 1 n )n = lim h→0+ (1 + h)1/h V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 8 / 34
  • 11. . . . . . . Existence of e See Appendix B We can experimentally verify that this number exists and is e ≈ 2.718281828459045 . . . e is irrational e is transcendental n ( 1 + 1 n )n 1 2 2 2.25 3 2.37037 10 2.59374 100 2.70481 1000 2.71692 106 2.71828 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 9 / 34
  • 12. . . . . . . Logarithms Definition The base a logarithm loga x is the inverse of the function ax y = loga x ⇐⇒ x = ay The natural logarithm ln x is the inverse of ex . So y = ln x ⇐⇒ x = ey . Facts (i) loga(x1 · x2) = loga x1 + loga x2 (ii) loga ( x1 x2 ) = loga x1 − loga x2 (iii) loga(xr ) = r loga x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 10 / 34
  • 13. . . . . . . Graphs of logarithmic functions . .x .y .y = 2x .y = log2 x . .(0, 1) ..(1, 0) .y = 3x .y = log3 x .y = 10x .y = log10 x .y = ex .y = ln x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 11 / 34
  • 14. . . . . . . Change of base formula for logarithms Fact If a > 0 and a ̸= 1, and the same for b, then loga x = logb x logb a Proof. If y = loga x, then x = ay So logb x = logb(ay ) = y logb a Therefore y = loga x = logb x logb a V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 12 / 34
  • 15. . . . . . . Upshot of changing base The point of the change of base formula loga x = logb x logb a = 1 logb a · logb x = (constant) · logb x is that all the logarithmic functions are multiples of each other. So just pick one and call it your favorite. Engineers like the common logarithm log = log10 Computer scientists like the binary logarithm lg = log2 Mathematicians like natural logarithm ln = loge Naturally, we will follow the mathematicians. Just don’t pronounce it “lawn.” V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 13 / 34
  • 16. . . . . . . Outline Recall Section 3.1–3.2 Derivative of the natural exponential function Exponential Growth Derivative of the natural logarithm function Derivatives of other exponentials and logarithms Other exponentials Other logarithms Logarithmic Differentiation The power rule for irrational powers V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 14 / 34
  • 17. . . . . . . Derivatives of Exponential Functions Fact If f(x) = ax , then f′ (x) = f′ (0)ax . V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
  • 18. . . . . . . Derivatives of Exponential Functions Fact If f(x) = ax , then f′ (x) = f′ (0)ax . Proof. Follow your nose: f′ (x) = lim h→0 f(x + h) − f(x) h = lim h→0 ax+h − ax h = lim h→0 axah − ax h = ax · lim h→0 ah − 1 h = ax · f′ (0). V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
  • 19. . . . . . . Derivatives of Exponential Functions Fact If f(x) = ax , then f′ (x) = f′ (0)ax . Proof. Follow your nose: f′ (x) = lim h→0 f(x + h) − f(x) h = lim h→0 ax+h − ax h = lim h→0 axah − ax h = ax · lim h→0 ah − 1 h = ax · f′ (0). To reiterate: the derivative of an exponential function is a constant times that function. Much different from polynomials! V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 15 / 34
  • 20. . . . . . . The funny limit in the case of e Remember the definition of e: e = lim n→∞ ( 1 + 1 n )n = lim h→0 (1 + h)1/h Question What is lim h→0 eh − 1 h ? V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
  • 21. . . . . . . The funny limit in the case of e Remember the definition of e: e = lim n→∞ ( 1 + 1 n )n = lim h→0 (1 + h)1/h Question What is lim h→0 eh − 1 h ? Answer If h is small enough, e ≈ (1 + h)1/h . So eh − 1 h ≈ [ (1 + h)1/h ]h − 1 h = (1 + h) − 1 h = h h = 1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
  • 22. . . . . . . The funny limit in the case of e Remember the definition of e: e = lim n→∞ ( 1 + 1 n )n = lim h→0 (1 + h)1/h Question What is lim h→0 eh − 1 h ? Answer If h is small enough, e ≈ (1 + h)1/h . So eh − 1 h ≈ [ (1 + h)1/h ]h − 1 h = (1 + h) − 1 h = h h = 1 So in the limit we get equality: lim h→0 eh − 1 h = 1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 16 / 34
  • 23. . . . . . . Derivative of the natural exponential function From d dx ax = ( lim h→0 ah − 1 h ) ax and lim h→0 eh − 1 h = 1 we get: Theorem d dx ex = ex V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 17 / 34
  • 24. . . . . . . Exponential Growth Commonly misused term to say something grows exponentially It means the rate of change (derivative) is proportional to the current value Examples: Natural population growth, compounded interest, social networks V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 18 / 34
  • 25. . . . . . . Examples Examples Find derivatives of these functions: e3x ex2 x2 ex V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
  • 26. . . . . . . Examples Examples Find derivatives of these functions: e3x ex2 x2 ex Solution d dx e3x = 3e3x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
  • 27. . . . . . . Examples Examples Find derivatives of these functions: e3x ex2 x2 ex Solution d dx e3x = 3e3x d dx ex2 = ex2 d dx (x2 ) = 2xex2 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
  • 28. . . . . . . Examples Examples Find derivatives of these functions: e3x ex2 x2 ex Solution d dx e3x = 3e3x d dx ex2 = ex2 d dx (x2 ) = 2xex2 d dx x2 ex = 2xex + x2 ex V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 19 / 34
  • 29. . . . . . . Outline Recall Section 3.1–3.2 Derivative of the natural exponential function Exponential Growth Derivative of the natural logarithm function Derivatives of other exponentials and logarithms Other exponentials Other logarithms Logarithmic Differentiation The power rule for irrational powers V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 20 / 34
  • 30. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 31. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so ey dy dx = 1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 32. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so ey dy dx = 1 =⇒ dy dx = 1 ey = 1 x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 33. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so ey dy dx = 1 =⇒ dy dx = 1 ey = 1 x We have discovered: Fact d dx ln x = 1 x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 34. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so ey dy dx = 1 =⇒ dy dx = 1 ey = 1 x We have discovered: Fact d dx ln x = 1 x . .x .y .ln x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 35. . . . . . . Derivative of the natural logarithm function Let y = ln x. Then x = ey so ey dy dx = 1 =⇒ dy dx = 1 ey = 1 x We have discovered: Fact d dx ln x = 1 x . .x .y .ln x . 1 x V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 21 / 34
  • 36. . . . . . . The Tower of Powers y y′ x3 3x2 x2 2x1 x1 1x0 x0 0 ? ? x−1 −1x−2 x−2 −2x−3 The derivative of a power function is a power function of one lower power V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
  • 37. . . . . . . The Tower of Powers y y′ x3 3x2 x2 2x1 x1 1x0 x0 0 ? x−1 x−1 −1x−2 x−2 −2x−3 The derivative of a power function is a power function of one lower power Each power function is the derivative of another power function, except x−1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
  • 38. . . . . . . The Tower of Powers y y′ x3 3x2 x2 2x1 x1 1x0 x0 0 ln x x−1 x−1 −1x−2 x−2 −2x−3 The derivative of a power function is a power function of one lower power Each power function is the derivative of another power function, except x−1 ln x fills in this gap precisely. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 22 / 34
  • 39. . . . . . . Outline Recall Section 3.1–3.2 Derivative of the natural exponential function Exponential Growth Derivative of the natural logarithm function Derivatives of other exponentials and logarithms Other exponentials Other logarithms Logarithmic Differentiation The power rule for irrational powers V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 23 / 34
  • 40. . . . . . . Other logarithms Example Use implicit differentiation to find d dx ax . V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
  • 41. . . . . . . Other logarithms Example Use implicit differentiation to find d dx ax . Solution Let y = ax , so ln y = ln ax = x ln a V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
  • 42. . . . . . . Other logarithms Example Use implicit differentiation to find d dx ax . Solution Let y = ax , so ln y = ln ax = x ln a Differentiate implicitly: 1 y dy dx = ln a =⇒ dy dx = (ln a)y = (ln a)ax V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
  • 43. . . . . . . Other logarithms Example Use implicit differentiation to find d dx ax . Solution Let y = ax , so ln y = ln ax = x ln a Differentiate implicitly: 1 y dy dx = ln a =⇒ dy dx = (ln a)y = (ln a)ax Before we showed y′ = y′ (0)y, so now we know that ln a = lim h→0 ah − 1 h V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 24 / 34
  • 44. . . . . . . Other logarithms Example Find d dx loga x. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
  • 45. . . . . . . Other logarithms Example Find d dx loga x. Solution Let y = loga x, so ay = x. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
  • 46. . . . . . . Other logarithms Example Find d dx loga x. Solution Let y = loga x, so ay = x. Now differentiate implicitly: (ln a)ay dy dx = 1 =⇒ dy dx = 1 ay ln a = 1 x ln a V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
  • 47. . . . . . . Other logarithms Example Find d dx loga x. Solution Let y = loga x, so ay = x. Now differentiate implicitly: (ln a)ay dy dx = 1 =⇒ dy dx = 1 ay ln a = 1 x ln a Another way to see this is to take the natural logarithm: ay = x =⇒ y ln a = ln x =⇒ y = ln x ln a So dy dx = 1 ln a 1 x . V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 25 / 34
  • 48. . . . . . . More examples Example Find d dx log2(x2 + 1) V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 26 / 34
  • 49. . . . . . . More examples Example Find d dx log2(x2 + 1) Answer dy dx = 1 ln 2 1 x2 + 1 (2x) = 2x (ln 2)(x2 + 1) V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 26 / 34
  • 50. . . . . . . Outline Recall Section 3.1–3.2 Derivative of the natural exponential function Exponential Growth Derivative of the natural logarithm function Derivatives of other exponentials and logarithms Other exponentials Other logarithms Logarithmic Differentiation The power rule for irrational powers V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 27 / 34
  • 51. . . . . . . A nasty derivative Example Let y = (x2 + 1) √ x + 3 x − 1 . Find y′ . V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 28 / 34
  • 52. . . . . . . A nasty derivative Example Let y = (x2 + 1) √ x + 3 x − 1 . Find y′ . Solution We use the quotient rule, and the product rule in the numerator: y′ = (x − 1) [ 2x √ x + 3 + (x2 + 1)1 2 (x + 3)−1/2 ] − (x2 + 1) √ x + 3(1) (x − 1)2 = 2x √ x + 3 (x − 1) + (x2 + 1) 2 √ x + 3(x − 1) − (x2 + 1) √ x + 3 (x − 1)2 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 28 / 34
  • 53. . . . . . . Another way y = (x2 + 1) √ x + 3 x − 1 ln y = ln(x2 + 1) + 1 2 ln(x + 3) − ln(x − 1) 1 y dy dx = 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 So dy dx = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) y = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) (x2 + 1) √ x + 3 x − 1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 29 / 34
  • 54. . . . . . . Compare and contrast Using the product, quotient, and power rules: y′ = 2x √ x + 3 (x − 1) + (x2 + 1) 2 √ x + 3(x − 1) − (x2 + 1) √ x + 3 (x − 1)2 Using logarithmic differentiation: y′ = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) (x2 + 1) √ x + 3 x − 1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
  • 55. . . . . . . Compare and contrast Using the product, quotient, and power rules: y′ = 2x √ x + 3 (x − 1) + (x2 + 1) 2 √ x + 3(x − 1) − (x2 + 1) √ x + 3 (x − 1)2 Using logarithmic differentiation: y′ = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) (x2 + 1) √ x + 3 x − 1 Are these the same? V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
  • 56. . . . . . . Compare and contrast Using the product, quotient, and power rules: y′ = 2x √ x + 3 (x − 1) + (x2 + 1) 2 √ x + 3(x − 1) − (x2 + 1) √ x + 3 (x − 1)2 Using logarithmic differentiation: y′ = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) (x2 + 1) √ x + 3 x − 1 Are these the same? Which do you like better? V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
  • 57. . . . . . . Compare and contrast Using the product, quotient, and power rules: y′ = 2x √ x + 3 (x − 1) + (x2 + 1) 2 √ x + 3(x − 1) − (x2 + 1) √ x + 3 (x − 1)2 Using logarithmic differentiation: y′ = ( 2x x2 + 1 + 1 2(x + 3) − 1 x − 1 ) (x2 + 1) √ x + 3 x − 1 Are these the same? Which do you like better? What kinds of expressions are well-suited for logarithmic differentiation? V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 30 / 34
  • 58. . . . . . . Derivatives of powers . . Question Let y = xx . Which of these is true? (A) Since y is a power function, y′ = x · xx−1 = xx . (B) Since y is an exponential function, y′ = (ln x) · xx (C) Neither . .x .y . .1 ..1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 31 / 34
  • 59. . . . . . . Derivatives of powers . . Question Let y = xx . Which of these is true? (A) Since y is a power function, y′ = x · xx−1 = xx . (B) Since y is an exponential function, y′ = (ln x) · xx (C) Neither . .x .y . .1 ..1 Answer (A) This can’t be y′ because xx > 0 for all x > 0, and this function decreases at some places (B) This can’t be y′ because (ln x)xx = 0 when x = 1, and this function does not have a horizontal tangent at x = 1. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 31 / 34
  • 60. . . . . . . It's neither! Or both? Solution If y = xx , then ln y = x ln x 1 y dy dx = x · 1 x + ln x = 1 + ln x dy dx = (1 + ln x)xx = xx + (ln x)xx V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
  • 61. . . . . . . It's neither! Or both? Solution If y = xx , then ln y = x ln x 1 y dy dx = x · 1 x + ln x = 1 + ln x dy dx = (1 + ln x)xx = xx + (ln x)xx Remarks Each of these terms is one of the wrong answers! V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
  • 62. . . . . . . It's neither! Or both? Solution If y = xx , then ln y = x ln x 1 y dy dx = x · 1 x + ln x = 1 + ln x dy dx = (1 + ln x)xx = xx + (ln x)xx Remarks Each of these terms is one of the wrong answers! y′ < 0 on the interval (0, e−1 ) y′ = 0 when x = e−1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 32 / 34
  • 63. . . . . . . Derivatives of power functions with any exponent Fact (The power rule) Let y = xr . Then y′ = rxr−1 . V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 33 / 34
  • 64. . . . . . . Derivatives of power functions with any exponent Fact (The power rule) Let y = xr . Then y′ = rxr−1 . Proof. y = xr =⇒ ln y = r ln x Now differentiate: 1 y dy dx = r x =⇒ dy dx = r y x = rxr−1 V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 33 / 34
  • 65. . . . . . . Summary Derivatives of logarithmic and exponential functions: y y′ ex ex ax (ln a) · ax ln x 1 x loga x 1 ln a · 1 x Logarithmic Differentiation can allow us to avoid the product and quotient rules. V63.0121.041, Calculus I (NYU) Section 3.3 Derivs of Exp and Log October 25, 2010 34 / 34