Examples of Diminnie orthogonality

In summary, Diminnie orthogonality extends the concept of orthogonality to more general Hilbert spaces, and is defined using the Riesz representation theorem and tensor products. It can be used to show that if two elements are Diminnie orthogonal, then their inner product is equal to 0, and conversely, if their inner product is equal to 0, then they are Diminnie orthogonal.
  • #1
PonyBarometer
3
0
Definition of this orthogonality goes like this:
## x, y \in X##, where ##X## - normed space and ##X^*## - its dual space. Then ##x## is orthogonal ##y##, if

$$
\sup\{f(x)g(y)-f(y)g(x)|, \, f,g\in X^*, \|f\|,\|g\|≤1\}=\|x\|\|y\|
$$

From what I understand ##f## and ##g## are linear functionals from the dual space.
I was wondering if someone could provide some example of Diminnie orthogonality and its usage, because I have difficulty understanding how it works.
 
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  • #2
Orthogonality is perfectly well-defined in Hilbert spaces. Indeed, we say that ##x\bot y## iff ##<x,y> = 0##. The idea of Diminnie orthogonality is to extend the notion of orthogonality to more general Hilbert spaces.

The Riesz representation theorem says that any continuous functional on Hilbert space ##f:X\rightarrow \mathbb{C}## has the form ##f(x) = <a,x>##.

So on Hilbert space, we get the following:

[tex]\textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|,\|b\|\leq 1\} = \|x\|\|y\|[/tex]

if ##x## and ##y## are Diminnie orthogonal.

Very related is the following quantity:

[tex]\textrm{sup}\{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|^2 + \|b\|^2 \leq 1\}[/tex]

This quantity is in Hilbert spaces somewhat better behaved. Indeed, we can take the Hilbert space ##X\times X## with inner product ##<(a,b),(c,d)> = <a,c> + <b,d>##. Then we can look at the following operator

[tex]\psi(a,b) = <a,x><b,y> - <b,x><a,y>[/tex]

We often use the notation ##x\otimes y## for the operator ##(x\otimes y)(a,b) = <x,a><y,b>##, so we have ##\psi = x\otimes y - y\otimes x##. The quantity

[tex]\|x\otimes y - y\otimes x\|= \textrm{sup}\{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|^2 + \|b\|^2 \leq 1\}[/tex]

is the norm of this functional.

Now, the space generated by all the ##x\otimes y## is called the tensor product ##X\otimes Y## and is a Hilbert space under the inner product ##<x\otimes y, z\otimes w> = <x,z><y,w>##. The associated norm is denoted as ##\|~\|_2## and we have ##\|~\|\leq \|~\|_2##.

In particular, if we have ##<x,x> = <y,y>=1##

[tex]\|x\otimes y - y\otimes x\|^2 \leq \|x\otimes y - y\otimes x\|_2^2 = <x\otimes y - y\otimes x, x\otimes y - y\otimes x> = 2 - 2<x,y><y,x>[/tex]

Now, what does this have to do with our quantity

[tex]\textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|,\|b\|\leq 1\} = \|x\|\|y\|[/tex]

Well, let's take ##\|x\| = \|y\|= 1## (this is the general case since we can just divide by ##\|x\|\|y\|##).

Then if we have ##\|a\|,\|b\|\leq 1##, then ##\|a\|^2 + \|b\|^2 \leq 2##. Thus we see that

[tex]
\begin{eqnarray*}
& &
\textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|,\|b\|\leq 1\}\\
& \leq & \textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|^2+\|b\|^2\leq 2\}\\
& = & \frac{1}{2}\textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|^2 + \|b\|^2 \leq 1\}\\ & = & 1 - <x,y><y,x>
\end{eqnarray*}
[/tex]

In fact, equality holds since we can take ##a=x## and ##b=y## and then

[tex]<a,x><b,y> - <b,x><a,y> = 1 - <y,x><x,y>[/tex]

Thus we get that for ##\|x\|= \|y\| = 1## that

[tex]\textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|,\|b\|\leq 1\} = 1 - <x,y><y,x>[/tex]

Thus if ##x## and ##y## are Diminnie orthogonal, then

## 1 - <x,y><y,x> = \textrm{sup} \{<a,x><b,y> - <b,x><a,y>~\vert~\|a\|,\|b\|\leq 1\} =1##

and thus easily follows that ##<x,y> = 0##.

Conversely if ##<x,y>= 0##, then we see easily that ##x## and ##y## are Diminnie orthogonal.
 

What is Diminnie orthogonality?

Diminnie orthogonality is a concept in mathematics that refers to a type of orthogonality where the vectors are mutually perpendicular but not necessarily of equal length. This means that the dot product of two Diminnie orthogonal vectors is zero, but their lengths may be different.

How is Diminnie orthogonality different from regular orthogonality?

Regular orthogonality, also known as Euclidean orthogonality, requires that the vectors are not only perpendicular but also of equal length. This means that the dot product of two regular orthogonal vectors is zero and their lengths are equal. Unlike regular orthogonality, Diminnie orthogonality does not have the requirement of equal vector lengths.

What are some examples of Diminnie orthogonal vectors?

An example of Diminnie orthogonal vectors would be [3, 4] and [-4, 3]. These vectors are mutually perpendicular, as their dot product is 0, but their lengths are different. Another example would be [1, 2, 2] and [-2, 1, 1].

Why is Diminnie orthogonality important?

Diminnie orthogonality is important in many applications, especially in physics and engineering. It allows for the decomposition of a vector into Diminnie orthogonal components, making it easier to analyze and solve complex problems.

Can Diminnie orthogonality be extended to more than two vectors?

Yes, Diminnie orthogonality can be extended to multiple vectors. In higher dimensions, it is known as general orthogonality. The concept remains the same, where the vectors are mutually perpendicular but not necessarily of equal length.

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