Operator Norm for Linear Transformations: Browder Ch. 8, Section 8.1, Page 179

In summary: C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{2}\label{eq2}$$ By inequalities \eqref{eq1} and \eqref{eq2}, the result follows.
  • #1
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The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

I am reader Andrew Browder's book: "Mathematical Analysis: An Introduction" ... ...

I am currently reading Chapter 8: Differentiable Maps and am specifically focused on Section 8.1 Linear Algebra ...

I need some help in fully understanding some remarks by Browder concerning the "operator norm" for linear transformations ...

The relevant notes form Browder read as follows:https://www.physicsforums.com/attachments/7451
My questions regarding the above text by Browder are as follows:
Question 1

In the above notes from Browder we read the following:

" ... ... A perhaps more natural way to define the distance between linear transformations is by using the so-called "operator norm" defined by

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ sup} \{ \lvert Tv \rvert \ : \ v \in \mathbb{R}^n , \ \lvert v \rvert \le 1 \}
\)It is not hard to verify that this definition is equivalent to \(\displaystyle \lvert \lvert T \rvert \rvert = \text{ inf} \{ C \ge 0 \ : \ \lvert Tv \rvert \le C \lvert v \rvert \text{ for all } v \in \mathbb{R}^n \} \)

... ... "
Can someone please demonstrate rigorously exactly why/how

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ sup} \{ \lvert Tv \rvert \ : \ v \in \mathbb{R}^n , \ \lvert v \rvert \le 1 \}
\)

is equivalent to

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ inf} \{ C \ge 0 \ : \ \lvert Tv \rvert \le C \lvert v \rvert \text{ for all } v \in \mathbb{R}^n \}
\)

... ... ... ?

Question 2


In the above notes from Browder we read the following:

" ... ... The finiteness of \(\displaystyle \lvert \lvert T \rvert \rvert\) is easy to see ... "Can someone please rigorously demonstrate why/how \(\displaystyle \lvert \lvert T \rvert \rvert\) is necessarily finite ... ?
Help will be much appreciated ... ...

Peter
 
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  • #2
Re: The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

Hi Peter,
Peter said:
Question 1

In the above notes from Browder we read the following:

" ... ... A perhaps more natural way to define the distance between linear transformations is by using the so-called "operator norm" defined by

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ sup} \{ \lvert Tv \rvert \ : \ v \in \mathbb{R}^n , \ \lvert v \rvert \le 1 \}
\)It is not hard to verify that this definition is equivalent to \(\displaystyle \lvert \lvert T \rvert \rvert = \text{ inf} \{ C \ge 0 \ : \ \lvert Tv \rvert \le C \lvert v \rvert \text{ for all } v \in \mathbb{R}^n \} \)

... ... "
Can someone please demonstrate rigorously exactly why/how

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ sup} \{ \lvert Tv \rvert \ : \ v \in \mathbb{R}^n , \ \lvert v \rvert \le 1 \}
\)

is equivalent to

\(\displaystyle \lvert \lvert T \rvert \rvert = \text{ inf} \{ C \ge 0 \ : \ \lvert Tv \rvert \le C \lvert v \rvert \text{ for all } v \in \mathbb{R}^n \}
\)

... ... ... ?
If $v\in \Bbb R^n - \{\bf 0\}$, then $\dfrac{v}{\lvert v\rvert}$ has norm $1$, so $\left\lvert T\left(\dfrac{v}{\lvert v\rvert}\right)\right\rvert \le \|T\|$. Since $T$ is linear, $T\left(\dfrac{v}{\lvert v\rvert}\right) = \dfrac{1}{\lvert v\rvert}T(v)$. Thus $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$. Since $T$ is linear, $T(0) = 0$, so the inequality $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$ holds for $v = 0$. Since $\lvert Tv\rvert \le \|T\|\lvert v\rvert$ for all $v\in \Bbb R^n$, then $\|T\|$ is an element of the set $\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Therefore,
$$\|T\| \ge \inf\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{1}\label{eq1}$$
On the other hand, if $C > 0$ such that $\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$. In particular, for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$. Hence, $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$, i.e., $\|T\| \le C$. This shows that $\|T\|$ is a lower bound for the set $\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Hence, $$\|T\| \le \inf\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{2}\label{eq2}$$ By inequalities \eqref{eq1} and \eqref{eq2}, the result follows.
Peter said:
Question 2


In the above notes from Browder we read the following:

" ... ... The finiteness of \(\displaystyle \lvert \lvert T \rvert \rvert\) is easy to see ... "Can someone please rigorously demonstrate why/how \(\displaystyle \lvert \lvert T \rvert \rvert\) is necessarily finite ... ?

Let $\mathbf{e}_1,\ldots, \mathbf{e}_n$ be the standard basis of $\Bbb R^n$. For all $\mathbf{v} = (v_1,\ldots, v_n)\in \Bbb R^n$, $$T(\mathbf{v}) = T\left(\sum_{i = 1}^n v_i \mathbf{e}_i\right) = \sum_{i = 1}^n v_i T(\mathbf{e}_i) = \mathbf{v}\cdot (T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$$ The Cauchy-Schwarz inequality gives $\lvert Tv\rvert \le C\lvert \mathbf{v}\rvert$ where $C$ is the norm of the vector $(T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$. Note the $C$ a constant independent of $\mathbf{v}$. Since $\mathbf{v}$ was arbitrary, $\|T\| \le C$. Therefore, $\|T\|$ is finite.
 
  • #3
Re: The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

Euge said:
Hi Peter,If $v\in \Bbb R^n - \{\bf 0\}$, then $\dfrac{v}{\lvert v\rvert}$ has norm $1$, so $\left\lvert T\left(\dfrac{v}{\lvert v\rvert}\right)\right\rvert \le \|T\|$. Since $T$ is linear, $T\left(\dfrac{v}{\lvert v\rvert}\right) = \dfrac{1}{\lvert v\rvert}T(v)$. Thus $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$. Since $T$ is linear, $T(0) = 0$, so the inequality $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$ holds for $v = 0$. Since $\lvert Tv\rvert \le \|T\|\lvert v\rvert$ for all $v\in \Bbb R^n$, then $\|T\|$ is an element of the set $\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Therefore,
$$\|T\| \ge \inf\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{1}\label{eq1}$$
On the other hand, if $C > 0$ such that $\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$. In particular, for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$. Hence, $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$, i.e., $\|T\| \le C$. This shows that $\|T\|$ is a lower bound for the set $\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Hence, $$\|T\| \le \inf\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{2}\label{eq2}$$ By inequalities \eqref{eq1} and \eqref{eq2}, the result follows.Let $\mathbf{e}_1,\ldots, \mathbf{e}_n$ be the standard basis of $\Bbb R^n$. For all $\mathbf{v} = (v_1,\ldots, v_n)\in \Bbb R^n$, $$T(\mathbf{v}) = T\left(\sum_{i = 1}^n v_i \mathbf{e}_i\right) = \sum_{i = 1}^n v_i T(\mathbf{e}_i) = \mathbf{v}\cdot (T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$$ The Cauchy-Schwarz inequality gives $\lvert Tv\rvert \le C\lvert \mathbf{v}\rvert$ where $C$ is the norm of the vector $(T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$. Note the $C$ a constant independent of $\mathbf{v}$. Since $\mathbf{v}$ was arbitrary, $\|T\| \le C$. Therefore, $\|T\|$ is finite.

Thanks so much Euge ...

Really appreciate your help on this matter ...

Just working through what you have written ...

Thanks again ...

Peter
 
  • #4
Re: The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

Euge said:
Hi Peter,If $v\in \Bbb R^n - \{\bf 0\}$, then $\dfrac{v}{\lvert v\rvert}$ has norm $1$, so $\left\lvert T\left(\dfrac{v}{\lvert v\rvert}\right)\right\rvert \le \|T\|$. Since $T$ is linear, $T\left(\dfrac{v}{\lvert v\rvert}\right) = \dfrac{1}{\lvert v\rvert}T(v)$. Thus $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$. Since $T$ is linear, $T(0) = 0$, so the inequality $\lvert T(v)\rvert \le \|T\|\lvert v\rvert$ holds for $v = 0$. Since $\lvert Tv\rvert \le \|T\|\lvert v\rvert$ for all $v\in \Bbb R^n$, then $\|T\|$ is an element of the set $\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Therefore,
$$\|T\| \ge \inf\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{1}\label{eq1}$$
On the other hand, if $C > 0$ such that $\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$. In particular, for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$. Hence, $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$, i.e., $\|T\| \le C$. This shows that $\|T\|$ is a lower bound for the set $\{C\ge 0: \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}$. Hence, $$\|T\| \le \inf\{C\ge 0 : \text{$\lvert Tv\rvert \le C\lvert v\rvert$ for all $v\in \Bbb R^n$}\}\tag{2}\label{eq2}$$ By inequalities \eqref{eq1} and \eqref{eq2}, the result follows.Let $\mathbf{e}_1,\ldots, \mathbf{e}_n$ be the standard basis of $\Bbb R^n$. For all $\mathbf{v} = (v_1,\ldots, v_n)\in \Bbb R^n$, $$T(\mathbf{v}) = T\left(\sum_{i = 1}^n v_i \mathbf{e}_i\right) = \sum_{i = 1}^n v_i T(\mathbf{e}_i) = \mathbf{v}\cdot (T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$$ The Cauchy-Schwarz inequality gives $\lvert Tv\rvert \le C\lvert \mathbf{v}\rvert$ where $C$ is the norm of the vector $(T(\mathbf{e}_1),\ldots, T(\mathbf{e}_n))$. Note the $C$ a constant independent of $\mathbf{v}$. Since $\mathbf{v}$ was arbitrary, $\|T\| \le C$. Therefore, $\|T\|$ is finite.
I am revising question 1 above ... where Euge is proving that \(\displaystyle \lvert \lvert T \rvert \rvert = \text{ sup} \{ \lvert Tv \rvert \ : \ v \in \mathbb{R}^n , \ \lvert v \rvert \le 1 \}
\)is equivalent to \(\displaystyle \lvert \lvert T \rvert \rvert = \text{ inf} \{ C \ge 0 \ : \ \lvert Tv \rvert \le C \lvert v \rvert \text{ for all } v \in \mathbb{R}^n \}
\)In the above proof by Euge ... Euge writes the following:" ... ... In particular, for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$. Hence, $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$, i.e., $\|T\| \le C$. ... ... "

Can someone please formally and rigorously demonstrate that ...if ... for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$ ... ...... it then follows that ... $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$ ... ... (It seems highly plausible ... but how do you rigorously prove it ... ? )
... help will be much appreciated ...

Peter
 
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  • #5
Re: The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

Peter said:
Can someone please formally and rigorously demonstrate that ...if ... for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$ ... ...... it then follows that ... $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \le C$ .
The statement "for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$" says that $C$ is an upper bound for the set $\{|Tv| : v\in \Bbb R^n,|v|\leqslant1\}$. The number $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert$ is the supremum, or least upper bound, of that same set. By definition, the least upper bound is less than or equal to any other upper bound. Therefore $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \leqslant C$.
 
  • #6
Re: The "Operator Norm" for Linear Transfomations ... Browder, page 179, Section 8.1, Ch. 8 ... ...

Opalg said:
The statement "for all $v\in \Bbb R^n$ with $\lvert v\rvert \le 1$, $\lvert Tv\rvert \le C$" says that $C$ is an upper bound for the set $\{|Tv| : v\in \Bbb R^n,|v|\leqslant1\}$. The number $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert$ is the supremum, or least upper bound, of that same set. By definition, the least upper bound is less than or equal to any other upper bound. Therefore $\sup_{\lvert v\rvert \le 1} \lvert Tv\rvert \leqslant C$.

Thanks Opalg ...

That certainly clears up the issue ...

Peter
 

FAQ: Operator Norm for Linear Transformations: Browder Ch. 8, Section 8.1, Page 179

What is the operator norm for linear transformations?

The operator norm for linear transformations is a measure of the size or magnitude of a linear transformation. It is defined as the maximum value of the transformation applied to a unit vector. This can be thought of as the largest amount by which the transformation can stretch or compress a vector.

How is the operator norm calculated?

The operator norm is calculated by taking the maximum value of the transformation applied to a unit vector. This can be found by taking the square root of the sum of the squared values of the transformation applied to each component of the unit vector.

What is the significance of the operator norm in linear algebra?

The operator norm is an important concept in linear algebra as it provides a way to measure the size or magnitude of a linear transformation. It is used in various mathematical proofs and has applications in fields such as functional analysis and numerical analysis.

How is the operator norm related to the spectral norm?

The spectral norm is a special case of the operator norm, where the transformation is represented by a matrix. In this case, the operator norm is equivalent to the spectral norm, which is the maximum singular value of the matrix.

Can the operator norm be used to compare different linear transformations?

Yes, the operator norm can be used to compare different linear transformations. It provides a measure of the size or magnitude of the transformation, allowing for comparisons between different transformations. However, it is important to note that the operator norm is not the only way to compare transformations and should be used in conjunction with other measures for a more complete understanding.

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