Addition of several numbers or other values
In
mathematics , summation is the
addition of a
sequence of any kind of
numbers , called addends or summands ; the result is their sum or total . Beside numbers, other types of values can be summed as well:
functions ,
vectors ,
matrices ,
polynomials and, in general, elements of any type of
mathematical objects on which an
operation denoted "+" is defined.
Summations of
infinite sequences are called
series . They involve the concept of
limit , and are not considered in this article.
The summation of an explicit sequence is denoted as a succession of additions. For example, summation of [1, 2, 4, 2] is denoted 1 + 2 + 4 + 2 , and results in 9, that is, 1 + 2 + 4 + 2 = 9 . Because addition is
associative and
commutative , there is no need of parentheses, and the result is the same irrespective of the order of the summands. Summation of a sequence of only one element results in this element itself. Summation of an empty sequence (a sequence with no elements), by convention, results in 0.
Very often, the elements of a sequence are defined, through a regular pattern, as a
function of their place in the sequence. For simple patterns, summation of long sequences may be represented with most summands replaced by ellipses. For example, summation of the first 100
natural numbers may be written as 1 + 2 + 3 + 4 + ⋯ + 99 + 100 . Otherwise, summation is denoted by using
Σ notation , where
∑
{\textstyle \sum }
is an enlarged capital
Greek letter
sigma . For example, the sum of the first n natural numbers can be denoted as
∑
i
=
1
n
i
.
{\textstyle \sum _{i=1}^{n}i.}
For long summations, and summations of variable length (defined with ellipses or Σ notation), it is a common problem to find
closed-form expressions for the result. For example,
[a]
∑
i
=
1
n
i
=
n
(
n
+
1
)
2
.
{\displaystyle \sum _{i=1}^{n}i={\frac {n(n+1)}{2}}.}
Although such formulas do not always exist, many summation formulas have been discovered—with some of the most common and elementary ones being listed in the remainder of this article.
Notation
Capital-sigma notation
The summation symbol
Mathematical notation uses a symbol that compactly represents summation of many similar terms: the summation symbol ,
∑
{\textstyle \sum }
, an enlarged form of the upright capital Greek letter
sigma . This is defined as
∑
i
=
m
n
a
i
=
a
m
+
a
m
+
1
+
a
m
+
2
+
⋯
+
a
n
−
1
+
a
n
{\displaystyle \sum _{i\mathop {=} m}^{n}a_{i}=a_{m}+a_{m+1}+a_{m+2}+\cdots +a_{n-1}+a_{n}}
where i is the index of summation ; ai is an indexed variable representing each term of the sum; m is the lower bound of summation , and n is the upper bound of summation . The "i = m " under the summation symbol means that the index i starts out equal to m . The index, i , is incremented by one for each successive term, stopping when i = n .
[b]
This is read as "sum of ai , from i = m to n ".
Here is an example showing the summation of squares:
∑
i
=
3
6
i
2
=
3
2
+
4
2
+
5
2
+
6
2
=
86.
{\displaystyle \sum _{i=3}^{6}i^{2}=3^{2}+4^{2}+5^{2}+6^{2}=86.}
In general, while any variable can be used as the index of summation (provided that no ambiguity is incurred), some of the most common ones include letters such as
i
{\displaystyle i}
,
[c]
j
{\displaystyle j}
,
k
{\displaystyle k}
, and
n
{\displaystyle n}
; the latter is also often used for the upper bound of a summation.
Alternatively, index and bounds of summation are sometimes omitted from the definition of summation if the context is sufficiently clear. This applies particularly when the index runs from 1 to n .
[1] For example, one might write that:
∑
a
i
2
=
∑
i
=
1
n
a
i
2
.
{\displaystyle \sum a_{i}^{2}=\sum _{i=1}^{n}a_{i}^{2}.}
Generalizations of this notation are often used, in which an arbitrary logical condition is supplied, and the sum is intended to be taken over all values satisfying the condition. For example:
∑
0
≤
k
<
100
f
(
k
)
{\displaystyle \sum _{0\leq k<100}f(k)}
is an alternative notation for
∑
k
=
0
99
f
(
k
)
,
{\textstyle \sum _{k=0}^{99}f(k),}
the sum of
f
(
k
)
{\displaystyle f(k)}
over all (
integers )
k
{\displaystyle k}
in the specified range. Similarly,
∑
x
∈
S
f
(
x
)
{\displaystyle \sum _{x\mathop {\in } S}f(x)}
is the sum of
f
(
x
)
{\displaystyle f(x)}
over all elements
x
{\displaystyle x}
in the set
S
{\displaystyle S}
, and
∑
d
|
n
μ
(
d
)
{\displaystyle \sum _{d\,|\,n}\;\mu (d)}
is the sum of
μ
(
d
)
{\displaystyle \mu (d)}
over all positive integers
d
{\displaystyle d}
dividing
n
{\displaystyle n}
.
[d]
There are also ways to generalize the use of many sigma signs. For example,
∑
i
,
j
{\displaystyle \sum _{i,j}}
is the same as
∑
i
∑
j
.
{\displaystyle \sum _{i}\sum _{j}.}
A similar notation is used for the
product of a sequence , where
∏
{\textstyle \prod }
, an enlarged form of the Greek capital letter
pi , is used instead of
∑
.
{\textstyle \sum .}
Special cases
It is possible to sum fewer than 2 numbers:
If the summation has one summand
x
{\displaystyle x}
, then the evaluated sum is
x
{\displaystyle x}
.
If the summation has no summands, then the evaluated sum is
zero , because zero is the
identity for addition. This is known as the
empty sum .
These degenerate cases are usually only used when the summation notation gives a degenerate result in a special case.
For example, if
n
=
m
{\displaystyle n=m}
in the definition above, then there is only one term in the sum; if
n
=
m
−
1
{\displaystyle n=m-1}
, then there is none.
Formal definition
Summation may be defined recursively as follows:
∑
i
=
a
b
g
(
i
)
=
0
{\displaystyle \sum _{i=a}^{b}g(i)=0}
, for
b
<
a
{\displaystyle b<a}
;
∑
i
=
a
b
g
(
i
)
=
g
(
b
)
+
∑
i
=
a
b
−
1
g
(
i
)
{\displaystyle \sum _{i=a}^{b}g(i)=g(b)+\sum _{i=a}^{b-1}g(i)}
, for
b
⩾
a
{\displaystyle b\geqslant a}
.
Measure theory notation
In the notation of
measure and
integration theory, a sum can be expressed as a
definite integral ,
∑
k
=
a
b
f
(
k
)
=
∫
a
,
b
f
d
μ
{\displaystyle \sum _{k\mathop {=} a}^{b}f(k)=\int _{[a,b]}f\,d\mu }
where
a
,
b
{\displaystyle [a,b]}
is the
subset of the integers from
a
{\displaystyle a}
to
b
{\displaystyle b}
, and where
μ
{\displaystyle \mu }
is the
counting measure .
Calculus of finite differences
Given a function f that is defined over the integers in the
interval m , n , the following equation holds:
f
(
n
)
−
f
(
m
)
=
∑
i
=
m
n
−
1
(
f
(
i
+
1
)
−
f
(
i
)
)
.
{\displaystyle f(n)-f(m)=\sum _{i=m}^{n-1}(f(i+1)-f(i)).}
This is known as a
telescoping series and is the analogue of the
fundamental theorem of calculus in
calculus of finite differences , which states that:
f
(
n
)
−
f
(
m
)
=
∫
m
n
f
′
(
x
)
d
x
,
{\displaystyle f(n)-f(m)=\int _{m}^{n}f'(x)\,dx,}
where
f
′
(
x
)
=
lim
h
→
0
f
(
x
+
h
)
−
f
(
x
)
h
{\displaystyle f'(x)=\lim _{h\to 0}{\frac {f(x+h)-f(x)}{h}}}
is the
derivative of f .
An example of application of the above equation is the following:
n
k
=
∑
i
=
0
n
−
1
(
(
i
+
1
)
k
−
i
k
)
.
{\displaystyle n^{k}=\sum _{i=0}^{n-1}\left((i+1)^{k}-i^{k}\right).}
Using
binomial theorem , this may be rewritten as:
n
k
=
∑
i
=
0
n
−
1
(
∑
j
=
0
k
−
1
(
k
j
)
i
j
)
.
{\displaystyle n^{k}=\sum _{i=0}^{n-1}\left(\sum _{j=0}^{k-1}{\binom {k}{j}}i^{j}\right).}
The above formula is more commonly used for inverting of the
difference operator
Δ
{\displaystyle \Delta }
, defined by:
Δ
(
f
)
(
n
)
=
f
(
n
+
1
)
−
f
(
n
)
,
{\displaystyle \Delta (f)(n)=f(n+1)-f(n),}
where f is a function defined on the nonnegative integers.
Thus, given such a function f , the problem is to compute the
antidifference of f , a function
F
=
Δ
−
1
f
{\displaystyle F=\Delta ^{-1}f}
such that
Δ
F
=
f
{\displaystyle \Delta F=f}
. That is,
F
(
n
+
1
)
−
F
(
n
)
=
f
(
n
)
.
{\displaystyle F(n+1)-F(n)=f(n).}
This function is defined up to the addition of a constant, and may be chosen as
[2]
F
(
n
)
=
∑
i
=
0
n
−
1
f
(
i
)
.
{\displaystyle F(n)=\sum _{i=0}^{n-1}f(i).}
There is not always a
closed-form expression for such a summation, but
Faulhaber's formula provides a closed form in the case where
f
(
n
)
=
n
k
{\displaystyle f(n)=n^{k}}
and, by
linearity , for every
polynomial function of n .
Approximation by definite integrals
Many such approximations can be obtained by the following connection between sums and
integrals , which holds for any
increasing function f :
∫
s
=
a
−
1
b
f
(
s
)
d
s
≤
∑
i
=
a
b
f
(
i
)
≤
∫
s
=
a
b
+
1
f
(
s
)
d
s
.
{\displaystyle \int _{s=a-1}^{b}f(s)\ ds\leq \sum _{i=a}^{b}f(i)\leq \int _{s=a}^{b+1}f(s)\ ds.}
and for any
decreasing function f :
∫
s
=
a
b
+
1
f
(
s
)
d
s
≤
∑
i
=
a
b
f
(
i
)
≤
∫
s
=
a
−
1
b
f
(
s
)
d
s
.
{\displaystyle \int _{s=a}^{b+1}f(s)\ ds\leq \sum _{i=a}^{b}f(i)\leq \int _{s=a-1}^{b}f(s)\ ds.}
For more general approximations, see the
Euler–Maclaurin formula .
For summations in which the summand is given (or can be interpolated) by an
integrable function of the index, the summation can be interpreted as a
Riemann sum occurring in the definition of the corresponding definite integral. One can therefore expect that for instance
b
−
a
n
∑
i
=
0
n
−
1
f
(
a
+
i
b
−
a
n
)
≈
∫
a
b
f
(
x
)
d
x
,
{\displaystyle {\frac {b-a}{n}}\sum _{i=0}^{n-1}f\left(a+i{\frac {b-a}{n}}\right)\approx \int _{a}^{b}f(x)\ dx,}
since the right-hand side is by definition the limit for
n
→
∞
{\displaystyle n\to \infty }
of the left-hand side. However, for a given summation n is fixed, and little can be said about the error in the above approximation without additional assumptions about f : it is clear that for wildly oscillating functions the Riemann sum can be arbitrarily far from the Riemann integral.
Identities
The formulae below involve finite sums; for infinite summations or finite summations of expressions involving
trigonometric functions or other
transcendental functions , see
list of mathematical series .
General identities
∑
n
=
s
t
C
⋅
f
(
n
)
=
C
⋅
∑
n
=
s
t
f
(
n
)
{\displaystyle \sum _{n=s}^{t}C\cdot f(n)=C\cdot \sum _{n=s}^{t}f(n)\quad }
(
distributivity )
[3]
∑
n
=
s
t
f
(
n
)
±
∑
n
=
s
t
g
(
n
)
=
∑
n
=
s
t
(
f
(
n
)
±
g
(
n
)
)
{\displaystyle \sum _{n=s}^{t}f(n)\pm \sum _{n=s}^{t}g(n)=\sum _{n=s}^{t}\left(f(n)\pm g(n)\right)\quad }
(
commutativity and
associativity )
[3]
∑
n
=
s
t
f
(
n
)
=
∑
n
=
s
+
p
t
+
p
f
(
n
−
p
)
{\displaystyle \sum _{n=s}^{t}f(n)=\sum _{n=s+p}^{t+p}f(n-p)\quad }
(index shift)
∑
n
∈
B
f
(
n
)
=
∑
m
∈
A
f
(
σ
(
m
)
)
,
{\displaystyle \sum _{n\in B}f(n)=\sum _{m\in A}f(\sigma (m)),\quad }
for a
bijection σ from a finite set A onto a set B (index change); this generalizes the preceding formula.
∑
n
=
s
t
f
(
n
)
=
∑
n
=
s
j
f
(
n
)
+
∑
n
=
j
+
1
t
f
(
n
)
{\displaystyle \sum _{n=s}^{t}f(n)=\sum _{n=s}^{j}f(n)+\sum _{n=j+1}^{t}f(n)\quad }
(splitting a sum, using
associativity )
∑
n
=
a
b
f
(
n
)
=
∑
n
=
0
b
f
(
n
)
−
∑
n
=
0
a
−
1
f
(
n
)
{\displaystyle \sum _{n=a}^{b}f(n)=\sum _{n=0}^{b}f(n)-\sum _{n=0}^{a-1}f(n)\quad }
(a variant of the preceding formula)
∑
n
=
s
t
f
(
n
)
=
∑
n
=
0
t
−
s
f
(
t
−
n
)
{\displaystyle \sum _{n=s}^{t}f(n)=\sum _{n=0}^{t-s}f(t-n)\quad }
(the sum from the first term up to the last is equal to the sum from the last down to the first)
∑
n
=
0
t
f
(
n
)
=
∑
n
=
0
t
f
(
t
−
n
)
{\displaystyle \sum _{n=0}^{t}f(n)=\sum _{n=0}^{t}f(t-n)\quad }
(a particular case of the formula above)
∑
i
=
k
0
k
1
∑
j
=
l
0
l
1
a
i
,
j
=
∑
j
=
l
0
l
1
∑
i
=
k
0
k
1
a
i
,
j
{\displaystyle \sum _{i=k_{0}}^{k_{1}}\sum _{j=l_{0}}^{l_{1}}a_{i,j}=\sum _{j=l_{0}}^{l_{1}}\sum _{i=k_{0}}^{k_{1}}a_{i,j}\quad }
(commutativity and associativity, again)
∑
k
≤
j
≤
i
≤
n
a
i
,
j
=
∑
i
=
k
n
∑
j
=
k
i
a
i
,
j
=
∑
j
=
k
n
∑
i
=
j
n
a
i
,
j
=
∑
j
=
0
n
−
k
∑
i
=
k
n
−
j
a
i
+
j
,
i
{\displaystyle \sum _{k\leq j\leq i\leq n}a_{i,j}=\sum _{i=k}^{n}\sum _{j=k}^{i}a_{i,j}=\sum _{j=k}^{n}\sum _{i=j}^{n}a_{i,j}=\sum _{j=0}^{n-k}\sum _{i=k}^{n-j}a_{i+j,i}\quad }
(another application of commutativity and associativity)
∑
n
=
2
s
2
t
+
1
f
(
n
)
=
∑
n
=
s
t
f
(
2
n
)
+
∑
n
=
s
t
f
(
2
n
+
1
)
{\displaystyle \sum _{n=2s}^{2t+1}f(n)=\sum _{n=s}^{t}f(2n)+\sum _{n=s}^{t}f(2n+1)\quad }
(splitting a sum into its
odd and
even parts, for even indexes)
∑
n
=
2
s
+
1
2
t
f
(
n
)
=
∑
n
=
s
+
1
t
f
(
2
n
)
+
∑
n
=
s
+
1
t
f
(
2
n
−
1
)
{\displaystyle \sum _{n=2s+1}^{2t}f(n)=\sum _{n=s+1}^{t}f(2n)+\sum _{n=s+1}^{t}f(2n-1)\quad }
(splitting a sum into its odd and even parts, for odd indexes)
(
∑
i
=
0
n
a
i
)
(
∑
j
=
0
n
b
j
)
=
∑
i
=
0
n
∑
j
=
0
n
a
i
b
j
{\displaystyle \left(\sum _{i=0}^{n}a_{i}\right)\left(\sum _{j=0}^{n}b_{j}\right)=\sum _{i=0}^{n}\sum _{j=0}^{n}a_{i}b_{j}\quad }
(
distributivity )
∑
i
=
s
m
∑
j
=
t
n
a
i
c
j
=
(
∑
i
=
s
m
a
i
)
(
∑
j
=
t
n
c
j
)
{\displaystyle \sum _{i=s}^{m}\sum _{j=t}^{n}{a_{i}}{c_{j}}=\left(\sum _{i=s}^{m}a_{i}\right)\left(\sum _{j=t}^{n}c_{j}\right)\quad }
(distributivity allows factorization)
∑
n
=
s
t
log
b
f
(
n
)
=
log
b
∏
n
=
s
t
f
(
n
)
{\displaystyle \sum _{n=s}^{t}\log _{b}f(n)=\log _{b}\prod _{n=s}^{t}f(n)\quad }
(the
logarithm of a product is the sum of the logarithms of the factors)
C
∑
n
=
s
t
f
(
n
)
=
∏
n
=
s
t
C
f
(
n
)
{\displaystyle C^{\sum \limits _{n=s}^{t}f(n)}=\prod _{n=s}^{t}C^{f(n)}\quad }
(the
exponential of a sum is the product of the exponential of the summands)
Powers and logarithm of arithmetic progressions
∑
i
=
1
n
c
=
n
c
{\displaystyle \sum _{i=1}^{n}c=nc\quad }
for every c that does not depend on i
∑
i
=
0
n
i
=
∑
i
=
1
n
i
=
n
(
n
+
1
)
2
{\displaystyle \sum _{i=0}^{n}i=\sum _{i=1}^{n}i={\frac {n(n+1)}{2}}\qquad }
(Sum of the simplest
arithmetic progression , consisting of the first n natural numbers.)
[2] : 52
∑
i
=
1
n
(
2
i
−
1
)
=
n
2
{\displaystyle \sum _{i=1}^{n}(2i-1)=n^{2}\qquad }
(Sum of first odd natural numbers)
∑
i
=
0
n
2
i
=
n
(
n
+
1
)
{\displaystyle \sum _{i=0}^{n}2i=n(n+1)\qquad }
(Sum of first even natural numbers)
∑
i
=
1
n
log
i
=
log
n
!
{\displaystyle \sum _{i=1}^{n}\log i=\log n!\qquad }
(A sum of
logarithms is the logarithm of the product)
∑
i
=
0
n
i
2
=
∑
i
=
1
n
i
2
=
n
(
n
+
1
)
(
2
n
+
1
)
6
=
n
3
3
+
n
2
2
+
n
6
{\displaystyle \sum _{i=0}^{n}i^{2}=\sum _{i=1}^{n}i^{2}={\frac {n(n+1)(2n+1)}{6}}={\frac {n^{3}}{3}}+{\frac {n^{2}}{2}}+{\frac {n}{6}}\qquad }
(Sum of the first
squares , see
square pyramidal number .)
[2] : 52
∑
i
=
0
n
i
3
=
(
∑
i
=
0
n
i
)
2
=
(
n
(
n
+
1
)
2
)
2
=
n
4
4
+
n
3
2
+
n
2
4
{\displaystyle \sum _{i=0}^{n}i^{3}=\left(\sum _{i=0}^{n}i\right)^{2}=\left({\frac {n(n+1)}{2}}\right)^{2}={\frac {n^{4}}{4}}+{\frac {n^{3}}{2}}+{\frac {n^{2}}{4}}\qquad }
(
Nicomachus's theorem )
[2] : 52
More generally, one has
Faulhaber's formula for
p
>
1
{\displaystyle p>1}
∑
k
=
1
n
k
p
=
n
p
+
1
p
+
1
+
1
2
n
p
+
∑
k
=
2
p
(
p
k
)
B
k
p
−
k
+
1
n
p
−
k
+
1
,
{\displaystyle \sum _{k=1}^{n}k^{p}={\frac {n^{p+1}}{p+1}}+{\frac {1}{2}}n^{p}+\sum _{k=2}^{p}{\binom {p}{k}}{\frac {B_{k}}{p-k+1}}\,n^{p-k+1},}
where
B
k
{\displaystyle B_{k}}
denotes a
Bernoulli number , and
(
p
k
)
{\displaystyle {\binom {p}{k}}}
is a
binomial coefficient .
Summation index in exponents
In the following summations, a is assumed to be different from 1.
∑
i
=
0
n
−
1
a
i
=
1
−
a
n
1
−
a
{\displaystyle \sum _{i=0}^{n-1}a^{i}={\frac {1-a^{n}}{1-a}}}
(sum of a
geometric progression )
∑
i
=
0
n
−
1
1
2
i
=
2
−
1
2
n
−
1
{\displaystyle \sum _{i=0}^{n-1}{\frac {1}{2^{i}}}=2-{\frac {1}{2^{n-1}}}}
(special case for a = 1/2 )
∑
i
=
0
n
−
1
i
a
i
=
a
−
n
a
n
+
(
n
−
1
)
a
n
+
1
(
1
−
a
)
2
{\displaystyle \sum _{i=0}^{n-1}ia^{i}={\frac {a-na^{n}+(n-1)a^{n+1}}{(1-a)^{2}}}}
(a times the derivative with respect to a of the geometric progression)
∑
i
=
0
n
−
1
(
b
+
i
d
)
a
i
=
b
∑
i
=
0
n
−
1
a
i
+
d
∑
i
=
0
n
−
1
i
a
i
=
b
(
1
−
a
n
1
−
a
)
+
d
(
a
−
n
a
n
+
(
n
−
1
)
a
n
+
1
(
1
−
a
)
2
)
=
b
(
1
−
a
n
)
−
(
n
−
1
)
d
a
n
1
−
a
+
d
a
(
1
−
a
n
−
1
)
(
1
−
a
)
2
{\displaystyle {\begin{aligned}\sum _{i=0}^{n-1}\left(b+id\right)a^{i}&=b\sum _{i=0}^{n-1}a^{i}+d\sum _{i=0}^{n-1}ia^{i}\\&=b\left({\frac {1-a^{n}}{1-a}}\right)+d\left({\frac {a-na^{n}+(n-1)a^{n+1}}{(1-a)^{2}}}\right)\\&={\frac {b(1-a^{n})-(n-1)da^{n}}{1-a}}+{\frac {da(1-a^{n-1})}{(1-a)^{2}}}\end{aligned}}}
(sum of an
arithmetico–geometric sequence )
Binomial coefficients and factorials
There exist very many summation identities involving binomial coefficients (a whole chapter of
Concrete Mathematics is devoted to just the basic techniques). Some of the most basic ones are the following.
Involving the binomial theorem
∑
i
=
0
n
(
n
i
)
a
n
−
i
b
i
=
(
a
+
b
)
n
,
{\displaystyle \sum _{i=0}^{n}{n \choose i}a^{n-i}b^{i}=(a+b)^{n},}
the
binomial theorem
∑
i
=
0
n
(
n
i
)
=
2
n
,
{\displaystyle \sum _{i=0}^{n}{n \choose i}=2^{n},}
the special case where a = b = 1
∑
i
=
0
n
(
n
i
)
p
i
(
1
−
p
)
n
−
i
=
1
{\displaystyle \sum _{i=0}^{n}{n \choose i}p^{i}(1-p)^{n-i}=1}
, the special case where p = a = 1 − b , which, for
0
≤
p
≤
1
,
{\displaystyle 0\leq p\leq 1,}
expresses the sum of the
binomial distribution
∑
i
=
0
n
i
(
n
i
)
=
n
(
2
n
−
1
)
,
{\displaystyle \sum _{i=0}^{n}i{n \choose i}=n(2^{n-1}),}
the value at a = b = 1 of the
derivative with respect to a of the binomial theorem
∑
i
=
0
n
(
n
i
)
i
+
1
=
2
n
+
1
−
1
n
+
1
,
{\displaystyle \sum _{i=0}^{n}{\frac {n \choose i}{i+1}}={\frac {2^{n+1}-1}{n+1}},}
the value at a = b = 1 of the
antiderivative with respect to a of the binomial theorem
Involving permutation numbers
In the following summations,
n
P
k
{\displaystyle {}_{n}P_{k}}
is the number of
k -permutations of n .
∑
i
=
0
n
i
P
k
(
n
i
)
=
n
P
k
(
2
n
−
k
)
{\displaystyle \sum _{i=0}^{n}{}_{i}P_{k}{n \choose i}={}_{n}P_{k}(2^{n-k})}
∑
i
=
1
n
i
+
k
P
k
+
1
=
∑
i
=
1
n
∏
j
=
0
k
(
i
+
j
)
=
(
n
+
k
+
1
)
!
(
n
−
1
)
!
(
k
+
2
)
{\displaystyle \sum _{i=1}^{n}{}_{i+k}P_{k+1}=\sum _{i=1}^{n}\prod _{j=0}^{k}(i+j)={\frac {(n+k+1)!}{(n-1)!(k+2)}}}
∑
i
=
0
n
i
!
⋅
(
n
i
)
=
∑
i
=
0
n
n
P
i
=
⌊
n
!
⋅
e
⌋
,
n
∈
Z
+
{\displaystyle \sum _{i=0}^{n}i!\cdot {n \choose i}=\sum _{i=0}^{n}{}_{n}P_{i}=\lfloor n!\cdot e\rfloor ,\quad n\in \mathbb {Z} ^{+}}
, where and
⌊
x
⌋
{\displaystyle \lfloor x\rfloor }
denotes the
floor function .
Others
∑
k
=
0
m
(
n
+
k
n
)
=
(
n
+
m
+
1
n
+
1
)
{\displaystyle \sum _{k=0}^{m}{\binom {n+k}{n}}={\binom {n+m+1}{n+1}}}
∑
i
=
k
n
(
i
k
)
=
(
n
+
1
k
+
1
)
{\displaystyle \sum _{i=k}^{n}{i \choose k}={n+1 \choose k+1}}
∑
i
=
0
n
i
⋅
i
!
=
(
n
+
1
)
!
−
1
{\displaystyle \sum _{i=0}^{n}i\cdot i!=(n+1)!-1}
∑
i
=
0
n
(
m
+
i
−
1
i
)
=
(
m
+
n
n
)
{\displaystyle \sum _{i=0}^{n}{m+i-1 \choose i}={m+n \choose n}}
∑
i
=
0
n
(
n
i
)
2
=
(
2
n
n
)
{\displaystyle \sum _{i=0}^{n}{n \choose i}^{2}={2n \choose n}}
∑
i
=
0
n
1
i
!
=
⌊
n
!
e
⌋
n
!
{\displaystyle \sum _{i=0}^{n}{\frac {1}{i!}}={\frac {\lfloor n!\;e\rfloor }{n!}}}
Harmonic numbers
∑
i
=
1
n
1
i
=
H
n
{\displaystyle \sum _{i=1}^{n}{\frac {1}{i}}=H_{n}\quad }
(the n th
harmonic number )
∑
i
=
1
n
1
i
k
=
H
n
k
{\displaystyle \sum _{i=1}^{n}{\frac {1}{i^{k}}}=H_{n}^{k}\quad }
(a
generalized harmonic number )
Growth rates
The following are useful
approximations (using
theta notation ):
∑
i
=
1
n
i
c
∈
Θ
(
n
c
+
1
)
{\displaystyle \sum _{i=1}^{n}i^{c}\in \Theta (n^{c+1})}
for real c greater than −1
∑
i
=
1
n
1
i
∈
Θ
(
log
e
n
)
{\displaystyle \sum _{i=1}^{n}{\frac {1}{i}}\in \Theta (\log _{e}n)}
(See
Harmonic number )
∑
i
=
1
n
c
i
∈
Θ
(
c
n
)
{\displaystyle \sum _{i=1}^{n}c^{i}\in \Theta (c^{n})}
for real c greater than 1
∑
i
=
1
n
log
(
i
)
c
∈
Θ
(
n
⋅
log
(
n
)
c
)
{\displaystyle \sum _{i=1}^{n}\log(i)^{c}\in \Theta (n\cdot \log(n)^{c})}
for
non-negative real c
∑
i
=
1
n
log
(
i
)
c
⋅
i
d
∈
Θ
(
n
d
+
1
⋅
log
(
n
)
c
)
{\displaystyle \sum _{i=1}^{n}\log(i)^{c}\cdot i^{d}\in \Theta (n^{d+1}\cdot \log(n)^{c})}
for non-negative real c , d
∑
i
=
1
n
log
(
i
)
c
⋅
i
d
⋅
b
i
∈
Θ
(
n
d
⋅
log
(
n
)
c
⋅
b
n
)
{\displaystyle \sum _{i=1}^{n}\log(i)^{c}\cdot i^{d}\cdot b^{i}\in \Theta (n^{d}\cdot \log(n)^{c}\cdot b^{n})}
for non-negative real b > 1, c , d
History
Σ
(
2
w
x
+
w
2
)
=
x
2
{\displaystyle \Sigma \ (2wx+w^{2})=x^{2}}
In 1772, usage of Σ and Σn is attested by
Lagrange .
[9]
In 1823, the capital letter S is attested as a summation symbol for series. This usage was apparently widespread.
In 1829, the summation symbol Σ is attested by
Fourier and
C. G. J. Jacobi . Fourier's use includes lower and upper bounds, for example:
[10]
[11]
∑
i
=
1
∞
e
−
i
2
t
…
{\displaystyle \sum _{i=1}^{\infty }e^{-i^{2}t}\ldots }
See also
Notes
^ For details, see
Triangular number .
^ For a detailed exposition on summation notation, and arithmetic with sums, see Graham, Ronald L.; Knuth, Donald E.; Patashnik, Oren (1994). "Chapter 2: Sums".
Concrete Mathematics: A Foundation for Computer Science (PDF) (2nd ed.). Addison-Wesley Professional.
ISBN
978-0201558029 . [
permanent dead link ]
^ in contexts where there is no possibility of confusion with the
imaginary unit
i
{\displaystyle i}
^ Although the name of the
dummy variable does not matter (by definition), one usually uses letters from the middle of the alphabet (
i
{\displaystyle i}
through
q
{\displaystyle q}
) to denote integers, if there is a risk of confusion. For example, even if there should be no doubt about the interpretation, it could look slightly confusing to many mathematicians to see
x
{\displaystyle x}
instead of
k
{\displaystyle k}
in the above formulae involving
k
{\displaystyle k}
.
References
^
"Summation Notation" . www.columbia.edu . Retrieved 2020-08-16 .
^
a
b
c
d Handbook of Discrete and Combinatorial Mathematics , Kenneth H. Rosen, John G. Michaels, CRC Press, 1999,
ISBN
0-8493-0149-1 .
^
a
b
"Calculus I - Summation Notation" . tutorial.math.lamar.edu . Retrieved 2020-08-16 .
^ Burton, David M. (2011). The History of Mathematics: An Introduction (7th ed.). McGraw-Hill. p. 414.
ISBN
978-0-07-338315-6 .
^
Leibniz, Gottfried Wilhelm (1899). Gerhardt, Karl Immanuel (ed.).
Der Briefwechsel von Gottfried Wilhelm Leibniz mit Mathematikern. Erster Band . Berlin: Mayer & Müller. p.
154 .
^
Euler, Leonhard (1755).
Institutiones Calculi differentialis (in Latin). Petropolis. p.
27 .
^
Lagrange, Joseph-Louis (1867–1892).
Oeuvres de Lagrange. Tome 3 (in French). Paris. p.
451 . {{
cite book }}
: CS1 maint: location missing publisher (
link )
^
Mémoires de l'Académie royale des sciences de l'Institut de France pour l'année 1825, tome VIII (in French). Paris: Didot. 1829. pp.
581-622 .
^
Fourier, Jean-Baptiste Joseph (1888–1890).
Oeuvres de Fourier. Tome 2 (in French). Paris: Gauthier-Villars. p.
149 .
Bibliography
External links
Media related to
Summation at Wikimedia Commons