Integration is a mathematic operation on fuction to find area under the function curve bounded by region from a to b The integral with respect to x of a real-valued function f (x ) of a real variable x on the interval [a , b ] is written as
∫
a
b
f
(
x
)
d
x
{\displaystyle \displaystyle \int _{a}^{b}f(x)\,dx}
.
The integral sign ∫ represents integration.
The symbol dx , called the differential of the variable x , indicates that the variable of integration is x .
The function f (x ) to be integrated is called the integrand.
The symbol dx is separated from the integrand by a space (as shown). If a function has an integral, it is said to be integrable. The points a and b are called the limits of the integral. An integral where the limits are specified is called a definite integral.
The integral is said to be over the interval [a , b ] .
The fundamental theorem of calculus is a critical portion of calculus because it links the concept of a derivative to that of an integral. As a result, we can use our knowledge of derivatives to find the area under the curve, which is often quicker and simpler than using the definition of the integral .
∫
a
b
f
(
x
)
d
x
=
F
(
b
)
−
F
(
a
)
.
{\displaystyle \int _{a}^{b}\!f(x)\,dx=F(b)-F(a).}
We will need the following theorem in the discussion of the Fundamental Theorem of Calculus.
Template:Calculus/Def
f
(
x
)
{\displaystyle f(x)}
satisfies the requirements of the Extreme Value Theorem , so it has a minimum
m
{\displaystyle m}
and a maximum
M
{\displaystyle M}
in
[
a
,
b
]
{\displaystyle [a,b]}
. Since
∫
a
b
f
(
x
)
d
x
=
lim
n
→
∞
∑
k
=
1
n
f
(
x
k
∗
)
⋅
b
−
a
n
=
lim
n
→
∞
b
−
a
n
⋅
∑
k
=
1
n
f
(
x
k
∗
)
{\displaystyle \int \limits _{a}^{b}f(x)dx=\lim _{n\to \infty }\sum _{k=1}^{n}f(x_{k}^{*})\cdot {\frac {b-a}{n}}=\lim _{n\to \infty }{\frac {b-a}{n}}\cdot \sum _{k=1}^{n}f(x_{k}^{*})}
and since
m
≤
f
(
x
k
∗
)
≤
M
{\displaystyle m\leq f(x_{k}^{*})\leq M}
for all
x
k
∗
∈
[
a
,
b
]
{\displaystyle x_{k}^{*}\in [a,b]}
we have
lim
n
→
∞
b
−
a
n
⋅
∑
k
=
1
n
m
≤
lim
n
→
∞
b
−
a
n
⋅
∑
k
=
1
n
f
(
x
k
∗
)
≤
lim
n
→
∞
b
−
a
n
⋅
∑
k
=
1
n
M
lim
n
→
∞
m
n
⋅
b
−
a
n
≤
∫
a
b
f
(
x
)
d
x
≤
lim
n
→
∞
M
n
⋅
b
−
a
n
lim
n
→
∞
m
(
b
−
a
)
≤
∫
a
b
f
(
x
)
d
x
≤
lim
n
→
∞
M
(
b
−
a
)
m
(
b
−
a
)
≤
∫
a
b
f
(
x
)
d
x
≤
M
(
b
−
a
)
m
≤
1
b
−
a
∫
a
b
f
(
x
)
d
x
≤
M
{\displaystyle {\begin{aligned}&\lim _{n\to \infty }{\frac {b-a}{n}}\cdot \sum _{k=1}^{n}m\leq \lim _{n\to \infty }{\frac {b-a}{n}}\cdot \sum _{k=1}^{n}f(x_{k}^{*})\leq \lim _{n\to \infty }{\frac {b-a}{n}}\cdot \sum _{k=1}^{n}M\\&\lim _{n\to \infty }mn\cdot {\frac {b-a}{n}}\leq \int \limits _{a}^{b}f(x)dx\leq \lim _{n\to \infty }Mn\cdot {\frac {b-a}{n}}\\&\lim _{n\to \infty }m(b-a)\leq \int \limits _{a}^{b}f(x)dx\leq \lim _{n\to \infty }M(b-a)\\&m(b-a)\leq \int \limits _{a}^{b}f(x)dx\leq M(b-a)\\&m\leq {\frac {1}{b-a}}\int \limits _{a}^{b}f(x)dx\leq M\end{aligned}}}
Since
f
{\displaystyle f}
is continuous, by the Intermediate Value Theorem there is some
f
(
c
)
{\displaystyle f(c)}
with
c
∈
[
a
,
b
]
{\displaystyle c\in [a,b]}
such that
1
b
−
a
∫
a
b
f
(
x
)
d
x
=
f
(
c
)
{\displaystyle {\frac {1}{b-a}}\int \limits _{a}^{b}f(x)dx=f(c)}
Suppose that
f
{\displaystyle f}
is continuous on
[
a
,
b
]
{\displaystyle [a,b]}
. We can define a function
F
{\displaystyle F}
by
F
(
x
)
=
∫
a
x
f
(
t
)
d
t
for
x
∈
[
a
,
b
]
{\displaystyle F(x)=\int \limits _{a}^{x}f(t)dt\quad {\text{for }}x\in [a,b]}
Template:Calculus/Def
When we have such functions
F
{\displaystyle F}
and
f
{\displaystyle f}
where
F
′
(
x
)
=
f
(
x
)
{\displaystyle F'(x)=f(x)}
for every
x
{\displaystyle x}
in some interval
I
{\displaystyle I}
we say that
F
{\displaystyle F}
is the antiderivative of
f
{\displaystyle f}
on
I
{\displaystyle I}
.
Template:Calculus/Def
Figure 1
Note: a minority of mathematicians refer to part one as two and part two as one. All mathematicians refer to what is stated here as part 2 as The Fundamental Theorem of Calculus.
Suppose
x
∈
(
a
,
b
)
{\displaystyle x\in (a,b)}
. Pick
Δ
x
{\displaystyle \Delta x}
so that
x
+
Δ
x
∈
(
a
,
b
)
{\displaystyle x+\Delta x\in (a,b)}
. Then
F
(
x
)
=
∫
a
x
f
(
t
)
d
t
{\displaystyle F(x)=\int \limits _{a}^{x}f(t)dt}
and
F
(
x
+
Δ
x
)
=
∫
a
x
+
Δ
x
f
(
t
)
d
t
{\displaystyle F(x+\Delta x)=\int \limits _{a}^{x+\Delta x}f(t)dt}
Subtracting the two equations gives
F
(
x
+
Δ
x
)
−
F
(
x
)
=
∫
a
x
+
Δ
x
f
(
t
)
d
t
−
∫
a
x
f
(
t
)
d
t
{\displaystyle F(x+\Delta x)-F(x)=\int \limits _{a}^{x+\Delta x}f(t)dt-\int \limits _{a}^{x}f(t)dt}
Now
∫
a
x
+
Δ
x
f
(
t
)
d
t
=
∫
a
x
f
(
t
)
d
t
+
∫
x
x
+
Δ
x
f
(
t
)
d
t
{\displaystyle \int \limits _{a}^{x+\Delta x}f(t)dt=\int \limits _{a}^{x}f(t)dt+\int \limits _{x}^{x+\Delta x}f(t)dt}
so rearranging this we have
F
(
x
+
Δ
x
)
−
F
(
x
)
=
∫
x
x
+
Δ
x
f
(
t
)
d
t
{\displaystyle F(x+\Delta x)-F(x)=\int \limits _{x}^{x+\Delta x}f(t)dt}
According to the Mean Value Theorem for Integration, there exists a
c
∈
[
x
,
x
+
Δ
x
]
{\displaystyle c\in [x,x+\Delta x]}
such that
∫
x
x
+
Δ
x
f
(
t
)
d
t
=
f
(
c
)
⋅
Δ
x
{\displaystyle \int \limits _{x}^{x+\Delta x}f(t)dt=f(c)\cdot \Delta x}
Notice that
c
{\displaystyle c}
depends on
Δ
x
{\displaystyle \Delta x}
. Anyway what we have shown is that
F
(
x
+
Δ
x
)
−
F
(
x
)
=
f
(
c
)
⋅
Δ
x
{\displaystyle F(x+\Delta x)-F(x)=f(c)\cdot \Delta x}
and dividing both sides by
Δ
x
{\displaystyle \Delta x}
gives
F
(
x
+
Δ
x
)
−
F
(
x
)
Δ
x
=
f
(
c
)
{\displaystyle {\frac {F(x+\Delta x)-F(x)}{\Delta x}}=f(c)}
Take the limit as
Δ
x
→
0
{\displaystyle \Delta x\to 0}
we get the definition of the derivative of
F
{\displaystyle F}
at
x
{\displaystyle x}
so we have
F
′
(
x
)
=
lim
Δ
x
→
0
F
(
x
+
Δ
x
)
−
F
(
x
)
Δ
x
=
lim
Δ
x
→
0
f
(
c
)
{\displaystyle F'(x)=\lim _{\Delta x\to 0}{\frac {F(x+\Delta x)-F(x)}{\Delta x}}=\lim _{\Delta x\to 0}f(c)}
To find the other limit, we will use the squeeze theorem .
c
∈
[
x
,
x
+
Δ
x
]
{\displaystyle c\in [x,x+\Delta x]}
, so
x
≤
c
≤
x
+
Δ
x
{\displaystyle x\leq c\leq x+\Delta x}
. Hence,
lim
Δ
x
→
0
[
x
+
Δ
x
]
=
x
⇒
lim
Δ
x
→
0
c
=
x
{\displaystyle \lim _{\Delta x\to 0}{\Big [}x+\Delta x{\Big ]}=x\quad \Rightarrow \quad \lim _{\Delta x\to 0}c=x}
As
f
{\displaystyle f}
is continuous we have
F
′
(
x
)
=
lim
Δ
x
→
0
f
(
c
)
=
f
(
lim
Δ
x
→
0
c
)
=
f
(
x
)
{\displaystyle F'(x)=\lim _{\Delta x\to 0}f(c)=f\left(\lim _{\Delta x\to 0}c\right)=f(x)}
which completes the proof.
◼
{\displaystyle \blacksquare }
Define
P
(
x
)
=
∫
a
x
f
(
t
)
d
t
{\displaystyle P(x)=\int \limits _{a}^{x}f(t)dt}
. Then by the Fundamental Theorem of Calculus part I we know that
P
{\displaystyle P}
is differentiable on
(
a
,
b
)
{\displaystyle (a,b)}
and for all
x
∈
(
a
,
b
)
{\displaystyle x\in (a,b)}
P
′
(
x
)
=
f
(
x
)
{\displaystyle P'(x)=f(x)}
So
P
{\displaystyle P}
is an antiderivative of
f
{\displaystyle f}
. Since we were assuming that
F
{\displaystyle F}
was also an antiderivative for all
x
∈
(
a
,
b
)
{\displaystyle x\in (a,b)}
,
P
′
(
x
)
=
F
′
(
x
)
P
′
(
x
)
−
F
′
(
x
)
=
0
(
P
(
x
)
−
F
(
x
)
)
′
=
0
{\displaystyle {\begin{aligned}&P'(x)=F'(x)\\&P'(x)-F'(x)=0\\&{\Big (}P(x)-F(x){\Big )}'=0\end{aligned}}}
Let
g
(
x
)
=
P
(
x
)
−
F
(
x
)
{\displaystyle g(x)=P(x)-F(x)}
. The Mean Value Theorem applied to
g
(
x
)
{\displaystyle g(x)}
on
[
a
,
ξ
]
{\displaystyle [a,\xi ]}
with
a
<
ξ
<
b
{\displaystyle a<\xi <b}
says that
g
(
ξ
)
−
g
(
a
)
ξ
−
a
=
g
′
(
c
)
{\displaystyle {\frac {g(\xi )-g(a)}{\xi -a}}=g'(c)}
for some
c
{\displaystyle c}
in
(
a
,
ξ
)
{\displaystyle (a,\xi )}
. But since
g
′
(
x
)
=
0
{\displaystyle g'(x)=0}
for all
x
{\displaystyle x}
in
[
a
,
b
]
{\displaystyle [a,b]}
,
g
(
ξ
)
{\displaystyle g(\xi )}
must equal
g
(
a
)
{\displaystyle g(a)}
for all
ξ
{\displaystyle \xi }
in
(
a
,
b
)
{\displaystyle (a,b)}
, i.e. g(x) is constant on
(
a
,
b
)
{\displaystyle (a,b)}
.
This implies there is a constant
C
=
g
(
a
)
=
P
(
a
)
−
F
(
a
)
=
−
F
(
a
)
{\displaystyle C=g(a)=P(a)-F(a)=-F(a)}
such that for all
x
∈
(
a
,
b
)
{\displaystyle x\in (a,b)}
,
P
(
x
)
=
F
(
x
)
+
C
{\displaystyle P(x)=F(x)+C}
and as
g
{\displaystyle g}
is continuous we see this holds when
x
=
a
{\displaystyle x=a}
and
x
=
b
{\displaystyle x=b}
as well. And putting
x
=
b
{\displaystyle x=b}
gives
∫
a
b
f
(
t
)
d
x
=
P
(
b
)
=
F
(
b
)
+
C
=
F
(
b
)
−
F
(
a
)
{\displaystyle \int \limits _{a}^{b}f(t)dx=P(b)=F(b)+C=F(b)-F(a)}
◼
{\displaystyle \blacksquare }
The second part of the Fundamental Theorem of Calculus gives us a way to calculate definite integrals. Just find an antiderivative of the integrand, and subtract the value of the antiderivative at the lower bound from the value of the antiderivative at the upper bound. That is
∫
a
b
f
(
x
)
d
x
=
F
(
b
)
−
F
(
a
)
{\displaystyle \int \limits _{a}^{b}f(x)dx=F(b)-F(a)}
where
F
′
(
x
)
=
f
(
x
)
{\displaystyle F'(x)=f(x)}
. As a convenience, we use the notation
F
(
x
)
|
a
b
{\displaystyle F(x){\bigg |}_{a}^{b}}
to represent
F
(
b
)
−
F
(
a
)
{\displaystyle F(b)-F(a)}
Using the power rule for differentiation we can find a formula for the integral of a power using the Fundamental Theorem of Calculus. Let
f
(
x
)
=
x
n
{\displaystyle f(x)=x^{n}}
. We want to find an antiderivative for
f
{\displaystyle f}
. Since the differentiation rule for powers lowers the power by 1 we have that
d
d
x
x
n
+
1
=
(
n
+
1
)
x
n
{\displaystyle {\frac {d}{dx}}x^{n+1}=(n+1)x^{n}}
As long as
n
+
1
≠
0
{\displaystyle n+1\neq 0}
we can divide by
n
+
1
{\displaystyle n+1}
to get
d
d
x
(
x
n
+
1
n
+
1
)
=
x
n
=
f
(
x
)
{\displaystyle {\frac {d}{dx}}\left({\frac {x^{n+1}}{n+1}}\right)=x^{n}=f(x)}
So the function
F
(
x
)
=
x
n
+
1
n
+
1
{\displaystyle F(x)={\frac {x^{n+1}}{n+1}}}
is an antiderivative of
f
{\displaystyle f}
. If
0
∉
[
a
,
b
]
{\displaystyle 0\notin [a,b]}
then
F
{\displaystyle F}
is continuous on
[
a
,
b
]
{\displaystyle [a,b]}
and, by applying the Fundamental Theorem of Calculus, we can calculate the integral of
f
{\displaystyle f}
to get the following rule.
Template:Calculus/Def
Notice that we allow all values of
n
{\displaystyle n}
, even negative or fractional. If
n
>
0
{\displaystyle n>0}
then this works even if
[
a
,
b
]
{\displaystyle [a,b]}
includes
0
{\displaystyle 0}
.
Template:Calculus/Def
Examples
To find
∫
1
2
x
3
d
x
{\displaystyle \int \limits _{1}^{2}x^{3}dx}
we raise the power by 1 and have to divide by 4. So
∫
1
2
x
3
d
x
=
x
4
4
|
1
2
=
2
4
4
−
1
4
4
=
15
4
{\displaystyle \int \limits _{1}^{2}x^{3}dx={\frac {x^{4}}{4}}{\Bigg |}_{1}^{2}={\frac {2^{4}}{4}}-{\frac {1^{4}}{4}}={\frac {15}{4}}}
The power rule also works for negative powers. For instance
∫
1
3
d
x
x
3
=
∫
1
3
x
−
3
d
x
=
x
−
2
−
2
|
1
3
=
1
−
2
(
3
−
2
−
1
−
2
)
=
−
1
2
(
1
3
2
−
1
)
=
−
1
2
(
1
9
−
1
)
=
1
2
⋅
8
9
=
4
9
{\displaystyle \int \limits _{1}^{3}{\frac {dx}{x^{3}}}=\int \limits _{1}^{3}x^{-3}dx={\frac {x^{-2}}{-2}}{\Bigg |}_{1}^{3}={\frac {1}{-2}}\left(3^{-2}-1^{-2}\right)=-{\frac {1}{2}}\left({\frac {1}{3^{2}}}-1\right)=-{\frac {1}{2}}\left({\frac {1}{9}}-1\right)={\frac {1}{2}}\cdot {\frac {8}{9}}={\frac {4}{9}}}
We can also use the power rule for fractional powers. For instance
∫
0
5
x
d
x
=
∫
0
5
x
1
2
d
x
=
x
x
3
2
|
0
5
=
2
3
(
5
3
2
−
0
3
2
)
=
10
5
3
{\displaystyle \int \limits _{0}^{5}{\sqrt {x}}dx=\int \limits _{0}^{5}x^{\frac {1}{2}}dx={\frac {x{\sqrt {x}}}{\frac {3}{2}}}{\Bigg |}_{0}^{5}={\frac {2}{3}}\left(5^{\frac {3}{2}}-0^{\frac {3}{2}}\right)={\frac {10{\sqrt {5}}}{3}}}
Using linearity the power rule can also be thought of as applying to constants. For example,
=
∫
3
11
7
d
x
=
∫
3
11
7
x
0
d
x
=
7
∫
3
11
x
0
d
x
=
7
x
|
3
11
=
7
(
11
−
3
)
=
56
{\displaystyle =\int \limits _{3}^{11}7dx=\int \limits _{3}^{11}7x^{0}dx=7\int \limits _{3}^{11}x^{0}dx=7x{\Bigg |}_{3}^{11}=7(11-3)=56}
Using the linearity rule we can now integrate any polynomial. For example
∫
0
3
(
3
x
2
+
4
x
+
2
)
d
x
=
(
x
3
+
2
x
2
+
2
x
)
|
0
3
=
3
3
+
2
⋅
3
2
+
2
⋅
3
−
0
=
27
+
18
+
6
=
51
{\displaystyle \int \limits _{0}^{3}(3x^{2}+4x+2)dx=(x^{3}+2x^{2}+2x){\Bigg |}_{0}^{3}=3^{3}+2\cdot 3^{2}+2\cdot 3-0=27+18+6=51}