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Quick description
In some situations the existence of a limit can be derived by using inferior or superior limit and suitably dividing a domain to use self-similarity.
Prerequisites
Basic real analysis.
Example 1: Maximal density of a packing
Fix a compact domain
in Euclidean space
(for example, a ball). A packing
is then a union of domains congruents to
, with disjoint interiors. The density of a packing is defined as
where
is the volume and
is the square
, when the limit exists.
The present trick shows that if
is a packing contained in
of maximal volume, then
exists.
First, since
, the superior limit
exists. We want to prove that the inferior limit equals the superior one. Given any
, there is an
such that
. Now for any integer
the square
can be divided into
translates of
. Each of these contains a packing of density greater than
, so that
for all
. This rewrites as
. By maximality,
is non-decreasing in
; for all
one can introduce
and gets
. It follows that
so that as soon as
is large enough,
. As a consequence,
and we are done.
Example 2: Weyl's inequality and polynomial equidistribution
This example is taken from a mathoverflow question and answer.
Let
be a polynomial with real coefficients, and
irrational. Let
Weyl's Equidistribution theorem for polynomials is equivalent to the claim that
as
. Though it is not the most easy way to prove this, let us deduce this theorem from the following Weyl's inequality.
Let
be a rational number in lowest terms with
. Weyl's Inequality is the bound:
If
and
are both large enough, and of the same order of magnitude, then the right-hand side gets small. The point is that the conditions on
prevent one to apply this to arbitrary
. However, Dirichlet's theorem tells us that arbitrary high
satisfy the needed condition, so that Weyl's inequality implies
.
Now the trick comes into play: the right-hand side in Weyl's inequality does not depend on
, but only on
. It therefore gives a uniform bound simultaneously for the sum
and the sums
computing using
instead of
.
For all \varepsilon
N_0
t
S_{N_0}^t<\varepsilon N_0
K
N
K=\lfloor N/N_0 \rfloor
S_N
\limsup S_N/N=0$.
Tricki