In number theory, natural density, also referred to as asymptotic density or arithmetic density, is one method to measure how "large" a subset of the set of natural numbers is. It relies chiefly on the probability of encountering members of the desired subset when combing through the interval as grows large.
Intuitively, it is thought that there are more positive integers than perfect squares, since every perfect square is already positive, and many other positive integers exist besides. However, the set of positive integers is not in fact larger than the set of perfect squares: both sets are infinite and countable and can therefore be put in one-to-one correspondence. Nevertheless if one goes through the natural numbers, the squares become increasingly scarce. The notion of natural density makes this intuition precise for many, but not all, subsets of the naturals (see Schnirelmann density, which is similar to natural density but defined for all subsets of
N
If an integer is randomly selected from the interval, then the probability that it belongs to is the ratio of the number of elements of in to the total number of elements in . If this probability tends to some limit as tends to infinity, then this limit is referred to as the asymptotic density of . This notion can be understood as a kind of probability of choosing a number from the set . Indeed, the asymptotic density (as well as some other types of densities) is studied in probabilistic number theory.
A subset of positive integers has natural density if the proportion of elements of among all natural numbers from 1 to converges to as tends to infinity.
More explicitly, if one defines for any natural number the counting function as the number of elements of less than or equal to, then the natural density of being exactly means that
It follows from the definition that if a set has natural density then .
Let
A
N=\{1,2,\ldots\}.
n\inN
A(n)
A(n)=\{1,2,\ldots,n\}\capA,
a(n)=|A(n)|
A
n
Define the upper asymptotic density
\overline{d}(A)
A
Similarly, define the lower asymptotic density
\underline{d}(A)
A
A
d(A)
\underline{d}(A)=\overline{d}(A)
d(A)
This definition can be restated in the following way:if this limit exists.[1]
These definitions may equivalently be expressed in the following way. Given a subset
A
N
A somewhat weaker notion of density is the upper Banach density
d*(A)
A\subseteqN.
\N
F\subset\N
F=\emptyset
d(\N\setminusF)=1.
d(A),d(B),
d(A\cupB)
A=\{n2:n\in\N\}
A=\{2n:n\in\N\}
A=\{an+b:n\in\N\}
d(A)=\tfrac{1}{a}.
\tfrac{6}{\pi2}.
\tfrac{1}{\zeta(n)},
\zeta(n)
infty | |
A=cup | |
n=0 |
\left\{22n,\ldots,22n+1-1\right\}
of numbers whose binary expansion contains an odd number of digits is an example of a set which does not have an asymptotic density, since the upper density of this set is
\overlined(A)=\limm
1+22+ … +22m | |
22m+1-1 |
=\limm
22m+2-1 | |
3(22m+1-1) |
=
23, | |
whereas its lower density is
\underlined(A)=\limm
1+22+ … +22m | |
22m+2-1 |
=\limm
22m+2-1 | |
3(22m+2-1) |
=
13. | |
\{\alphan\}n\in\N
[0,1]
\{Ax\}x\in[0,1]
Ax:=\{n\in\N:\alphan<x\}.
Then, by definition,
d(Ax)=x
x
Other density functions on subsets of the natural numbers may be defined analogously. For example, the logarithmic density of a set A is defined as the limit (if it exists)
\delta(A)=\limx
1 | |
logx |
\sumn
1 | |
n |
.
Upper and lower logarithmic densities are defined analogously as well.
For the set of multiples of an integer sequence, the Davenport–Erdős theorem states that the natural density, when it exists, is equal to the logarithmic density.