Examples of Diminnie orthogonality

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SUMMARY

The discussion centers on Diminnie orthogonality, defined in the context of normed spaces and their duals. Specifically, two elements \(x\) and \(y\) in a normed space \(X\) are Diminnie orthogonal if the supremum of the expression \(\sup\{f(x)g(y)-f(y)g(x) | f,g \in X^*, \|f\|,\|g\| \leq 1\} = \|x\|\|y\|\). This concept extends traditional orthogonality found in Hilbert spaces, where \(x \bot y\) if \(\langle x,y \rangle = 0\). The Riesz representation theorem is also discussed, illustrating that any continuous functional can be expressed in terms of inner products, further linking Diminnie orthogonality to established mathematical frameworks.

PREREQUISITES
  • Understanding of normed spaces and dual spaces
  • Familiarity with Hilbert spaces and inner product definitions
  • Knowledge of linear functionals and the Riesz representation theorem
  • Basic grasp of supremum and its application in functional analysis
NEXT STEPS
  • Study the implications of the Riesz representation theorem in functional analysis
  • Explore the properties and applications of tensor products in Hilbert spaces
  • Research further examples of Diminnie orthogonality in various mathematical contexts
  • Learn about the relationship between Diminnie orthogonality and other forms of orthogonality in functional spaces
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Mathematicians, particularly those specializing in functional analysis, researchers in theoretical physics, and students studying advanced linear algebra will benefit from this discussion on Diminnie orthogonality.

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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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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:

\textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|,\|b\|\leq 1\} = \|x\|\|y\|

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

Very related is the following quantity:

\textrm{sup}\{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|^2 + \|b\|^2 \leq 1\}

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

\psi(a,b) = &lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;

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

\|x\otimes y - y\otimes x\|= \textrm{sup}\{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|^2 + \|b\|^2 \leq 1\}

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##

\|x\otimes y - y\otimes x\|^2 \leq \|x\otimes y - y\otimes x\|_2^2 = &lt;x\otimes y - y\otimes x, x\otimes y - y\otimes x&gt; = 2 - 2&lt;x,y&gt;&lt;y,x&gt;

Now, what does this have to do with our quantity

\textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|,\|b\|\leq 1\} = \|x\|\|y\|

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

<br /> \begin{eqnarray*}<br /> &amp; &amp;<br /> \textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|,\|b\|\leq 1\}\\<br /> &amp; \leq &amp; \textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|^2+\|b\|^2\leq 2\}\\<br /> &amp; = &amp; \frac{1}{2}\textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|^2 + \|b\|^2 \leq 1\}\\ &amp; = &amp; 1 - &lt;x,y&gt;&lt;y,x&gt;<br /> \end{eqnarray*}<br />

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

&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt; = 1 - &lt;y,x&gt;&lt;x,y&gt;

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

\textrm{sup} \{&lt;a,x&gt;&lt;b,y&gt; - &lt;b,x&gt;&lt;a,y&gt;~\vert~\|a\|,\|b\|\leq 1\} = 1 - &lt;x,y&gt;&lt;y,x&gt;

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.
 

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