MHB Norm of a Linear Transformation .... Junnheng Proposition 9.2.3 .... ....

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The discussion centers on understanding the proof of Proposition 9.2.3 from Junghenn's "A Course in Real Analysis," specifically regarding the properties of linear transformations under norms. Participants clarify that the equation involving the norm of a linear transformation, T, holds true when substituting a normalized vector. It is established that if the vector has a norm of 1, then the transformation's output is bounded by the operator norm of T. The conversation emphasizes the importance of recognizing that the norm of the transformed vector relates directly to the operator norm when the input vector is appropriately scaled. Overall, the thread provides insights into the mathematical reasoning behind linear transformations and their norms.
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I am reading Hugo D. Junghenn's book: "A Course in Real Analysis" ...

I am currently focused on Chapter 9: "Differentiation on $$\mathbb{R}^n$$"

I need some help with the proof of Proposition 9.2.3 ...

Proposition 9.2.3 and the preceding relevant Definition 9.2.2 read as follows:
https://www.physicsforums.com/attachments/7889
View attachment 7890
In the above proof we read the following:

" ... ... If $$\mathbf{x} \neq \mathbf{0}$$ then $$\| \mathbf{x} \|^{-1} \mathbf{x}$$ has a norm $$1$$, hence


$$\| \mathbf{x} \|^{-1} \| T \mathbf{x} \| = \| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le 1$$ ... ... "
Now I know that $$T( c \mathbf{x} ) = c T( \mathbf{x} ) $$... BUT ...... how do we know that this works "under the norm sign" ...... that is, how do we know ...$$\| \mathbf{x} \|^{-1} \| T \mathbf{x} \| = \| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| $$
... and further ... how do we know that ...
$$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le 1 $$Help will be appreciated ...

Peter
 
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Peter said:
I know that $$T( c \mathbf{x} ) = c T( \mathbf{x} ) $$

... BUT ...

... how do we know that this works "under the norm sign" ...

... that is, how do we know ...

$$\| \mathbf{x} \|^{-1} \| T \mathbf{x} \| = \| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| $$
If you make the substitution $c = \| \mathbf{x} \|^{-1}$ in the equation $\| c \mathbf{y}\| = c\| \mathbf{y}\|$ (where $c$ is a positive constant), then it becomes $\| \| \mathbf{x} \|^{-1} \mathbf{y}\| = \| \mathbf{x} \|^{-1}\| \mathbf{y}\|$. Do that when $\mathbf{y} = T\mathbf{x}$, to get $\| \|T( \mathbf{x} \|^{-1} \mathbf{x})\| = \| \| \mathbf{x} \|^{-1}(T \mathbf{x})\| = \| \mathbf{x} \|^{-1}\|T \mathbf{x}\|$.

Peter said:
... and further ... how do we know that ...

$$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le 1 $$
That is a mistake. It should read $$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le \|T\| $$.
 
Opalg said:
If you make the substitution $c = \| \mathbf{x} \|^{-1}$ in the equation $\| c \mathbf{y}\| = c\| \mathbf{y}\|$ (where $c$ is a positive constant), then it becomes $\| \| \mathbf{x} \|^{-1} \mathbf{y}\| = \| \mathbf{x} \|^{-1}\| \mathbf{y}\|$. Do that when $\mathbf{y} = T\mathbf{x}$, to get $\| \|T( \mathbf{x} \|^{-1} \mathbf{x})\| = \| \| \mathbf{x} \|^{-1}(T \mathbf{x})\| = \| \mathbf{x} \|^{-1}\|T \mathbf{x}\|$.That is a mistake. It should read $$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le \|T\| $$.
Thanks Opalg ...

Appreciate your help ...

Peter
 
Opalg said:
If you make the substitution $c = \| \mathbf{x} \|^{-1}$ in the equation $\| c \mathbf{y}\| = c\| \mathbf{y}\|$ (where $c$ is a positive constant), then it becomes $\| \| \mathbf{x} \|^{-1} \mathbf{y}\| = \| \mathbf{x} \|^{-1}\| \mathbf{y}\|$. Do that when $\mathbf{y} = T\mathbf{x}$, to get $\| \|T( \mathbf{x} \|^{-1} \mathbf{x})\| = \| \| \mathbf{x} \|^{-1}(T \mathbf{x})\| = \| \mathbf{x} \|^{-1}\|T \mathbf{x}\|$.That is a mistake. It should read $$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le \|T\| $$.

Hi Opalg ...

Just realized that I don't fully understand why $$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le \|T\| $$ ...

Can you please help further and demonstrate why this is the case ...

Peter
 
Peter said:
Hi Opalg ...

Just realized that I don't fully understand why $$\| T ( \| \mathbf{x} \|^{-1} \mathbf{x} ) \| \le \|T\| $$ ...

Can you please help further and demonstrate why this is the case ...

Peter
The definition $\|T\| = \sup\{\|T \mathbf{y} \| : \| \mathbf{y} \| = 1\}$ says that if $\| \mathbf{y} \| = 1$ then $\|T \mathbf{y} \| \leqslant \|T\|$. But $ \| \mathbf{x} \|^{-1} \mathbf{x} $ has norm $1$, so you can substitute that vector for $ \mathbf{y}$, to get $\bigl\|T( \| \mathbf{x} \|^{-1} \mathbf{x} )\bigr\| \leqslant \|T\|$.
 
Opalg said:
The definition $\|T\| = \sup\{\|T \mathbf{y} \| : \| \mathbf{y} \| = 1\}$ says that if $\| \mathbf{y} \| = 1$ then $\|T \mathbf{y} \| \leqslant \|T\|$. But $ \| \mathbf{x} \|^{-1} \mathbf{x} $ has norm $1$, so you can substitute that vector for $ \mathbf{y}$, to get $\bigl\|T( \| \mathbf{x} \|^{-1} \mathbf{x} )\bigr\| \leqslant \|T\|$.
Hi Opalg ... thanks again for the help ...

Peter
 
We all know the definition of n-dimensional topological manifold uses open sets and homeomorphisms onto the image as open set in ##\mathbb R^n##. It should be possible to reformulate the definition of n-dimensional topological manifold using closed sets on the manifold's topology and on ##\mathbb R^n## ? I'm positive for this. Perhaps the definition of smooth manifold would be problematic, though.

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