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In mathematics , a 1-unconditional normed space (also unconditional normed space ) is a specific type of normed vector space with a norm that remains invariant under the arbitrary sign changes of the coordinates of any vector. These spaces play a significant role in functional analysis , particularly in the study of Banach spaces .
A normed space
(
X
,
‖
⋅
‖
)
{\displaystyle (X,\|\cdot \|)}
is said to be 1-unconditional if for every vector
x
∈
X
{\displaystyle x\in X}
and for every choice of signs
ϵ
i
∈
{
−
1
,
1
}
{\displaystyle \epsilon _{i}\in \{-1,1\}}
, the holds:
‖
∑
i
=
1
U
s
e
r
s
a
n
d
b
o
x
n
ϵ
i
x
i
e
i
‖
=
‖
∑
i
=
1
n
x
i
e
i
‖
{\displaystyle \left\|\sum _{i=1}^{Usersandbox}n\epsilon _{i}x_{i}e_{i}\right\|=\left\|\sum _{i=1}^{n}x_{i}e_{i}\right\|}
where
{
e
i
}
{\displaystyle \{e_{i}\}}
is a basis for
X
{\displaystyle X}
, and
x
i
{\displaystyle x_{i}}
are the coordinates of
x
{\displaystyle x}
with respect to this basis.
Equivalently, it holds
‖
∑
i
=
1
n
x
i
e
i
‖
=
‖
∑
i
=
1
n
|
x
i
|
e
i
‖
{\displaystyle \left\|\sum _{i=1}^{n}x_{i}e_{i}\right\|=\left\|\sum _{i=1}^{n}|x_{i}|e_{i}\right\|}
Stability Under Coordinate Changes : The norm in a 1-unconditional normed space does not change if we alter the signs of the coordinates of any vector.
Example in
R
n
{\displaystyle \mathbb {R} ^{n}}
: The
ℓ
p
{\displaystyle \ell ^{p}}
-norms (for
1
≤
p
≤
∞
{\displaystyle 1\leq p\leq \infty }
) on
R
n
{\displaystyle \mathbb {R} ^{n}}
are examples of 1-unconditional norms. Specifically, for
x
∈
R
n
{\displaystyle x\in \mathbb {R} ^{n}}
, the norm
‖
x
‖
p
=
(
∑
i
=
1
n
|
x
i
|
p
)
1
/
p
{\displaystyle \|x\|_{p}=\left(\sum _{i=1}^{n}|x_{i}|^{p}\right)^{1/p}}
is 1-unconditional.
Banach Lattices : If
X
{\displaystyle X}
is a Banach lattice , it is often equipped with a 1-unconditional norm, which aligns with the lattice operations.
Functional Analysis : 1-unconditional norms are used to study the geometric properties of Banach spaces.
Approximation Theory : These norms are important in the context of best approximation and in the study of basis properties in Banach spaces.
Signal Processing : They can be applied in signal processing where stability under sign changes is crucial.
ℓ
p
{\displaystyle \ell ^{p}}
-spaces[ edit ]
The space
ℓ
p
{\displaystyle \ell ^{p}}
(for
1
≤
p
≤
∞
{\displaystyle 1\leq p\leq \infty }
) is defined as the set of all sequences
x
=
(
x
i
)
{\displaystyle x=(x_{i})}
such that
‖
x
‖
p
{\displaystyle \|x\|_{p}}
is finite. The norm in these spaces is given by:
For
1
≤
p
<
∞
{\displaystyle 1\leq p<\infty }
:
‖
x
‖
p
=
(
∑
i
=
1
n
|
x
i
|
p
)
1
/
p
{\displaystyle \|x\|_{p}=\left(\sum _{i=1}^{n}|x_{i}|^{p}\right)^{1/p}}
For
p
=
∞
{\displaystyle p=\infty }
:
‖
x
‖
∞
=
max
1
≤
i
≤
n
|
x
i
|
{\displaystyle \|x\|_{\infty }=\max _{1\leq i\leq n}|x_{i}|}
These norms are 1-unconditional because changing the signs of the coordinates does not affect the magnitude of the norm.
c
0
{\displaystyle c_{0}}
-space[ edit ]
The space
c
0
{\displaystyle c_{0}}
consists of all sequences
x
=
(
x
i
)
{\displaystyle x=(x_{i})}
that converge to zero, equipped with the norm:
‖
x
‖
∞
=
max
i
∈
N
|
x
i
|
{\displaystyle \|x\|_{\infty }=\max _{i\in \mathbb {N} }|x_{i}|}
This norm is also 1-unconditional.
[ 1]
[ 2]
[ 3]
^ Lindenstrauss, Joram; Tzafriri, Lior (1977). Classical Banach Spaces I, Sequence Spaces . Springer-Verlag.
^ Albiac, Fernando; Kalton, Nigel J. (2006). Topics in Banach Space Theory . Springer.
^ Megginson, Robert E. (1998). An Introduction to Banach Space Theory . Springer-Verlag.