Operator Norm and Cauchy Sequence .... Browder, Proposition 8.7 ....

In summary: R}^n). This is done by using the definition of a convergent sequence in a metric space, which states that a sequence \{x_n\} in a metric space is convergent if there exists an x in the space such that d(x_n, x) < \epsilon for all n \geq N for some positive integer N. In this case, the limit of the sequence is the linear map T defined by T(x) = \lim_{m \to \infty} S_m(x). Browder then shows that this limit is well-defined and that it satisfies the properties of a linear map.In summary, Proposition 8.7 shows that the sequence \{S_m\} is a Cauch
  • #1
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I am reading 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 the proof of Proposition 8.7 ...Proposition 8.7 and its proof reads as follows:
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In the above proof by Browder we read the following:"... ... Thus, \(\displaystyle \{ S_m \}\) is a Cauchy sequence in \(\displaystyle \mathscr{L} ( \mathbb{R}^n )\)... ... My question is as follows:

Can someone please demonstrate formally and rigorously that \(\displaystyle \{ S_m \}\) is a Cauchy sequence in \(\displaystyle \mathscr{L} ( \mathbb{R}^n )\)... ...
Help will be much appreciated ...

Peter===============================================================================Note: Browder defines a Cauchy Sequence in a metric space as follows:
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Hope that helps ...

Peter
 

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Peter said:
Can someone please demonstrate formally and rigorously that \(\displaystyle \{ S_m \}\) is a Cauchy sequence in \(\displaystyle \mathscr{L} ( \mathbb{R}^n )\)... ...
From Browder's definition, to prove that a sequence $(x_n)$ is Cauchy you have to show that given $\varepsilon>0$ there exists $n_0$ such that $\rho(x_m,x_n)<\varepsilon$ whenever $m$ and $n$ are greater than $n_0$. By taking $m$ to be the smaller of those two numbers, you can write $n=m+p$, where $p>0$. So you need to find $n_0$ such that $\rho(x_m,x_{m+p})<\varepsilon$ whenever $m\geqslant n_0$ and $p\geqslant1$.

In this example, the sequence $(x_n)$ becomes $(S_m)$ and the metric is given by $\rho(S_m,S_n) = \|S_m-s_n\|$. So we want to show that given $\varepsilon>0$ there exists $n_0$ such that $\|S_m - S_{m+p}\| < \varepsilon$ whenever $m\geqslant n_0$ and $p\geqslant1$. But Browder shows that $\|S_m - S_{m+p}\| < \frac{t^m}{1-t}$. Since $t<1$, the sequence $\left(\frac{t^m}{1-t}\right)$ converges to $0$. Therefore, given $\varepsilon>0$ there exists $n_0$ such that $\frac{t^m}{1-t} < \varepsilon$ whenever $m\geqslant n_0$, from which the rquired result immediately follows.
 
  • #3

Hello Peter,

I can assist you in understanding the proof of Proposition 8.7. First, let's review the definition of a Cauchy sequence in a metric space. A sequence \{x_n\} in a metric space is called a Cauchy sequence if for every positive real number \epsilon, there exists an index N such that for all n,m \geq N, we have d(x_n, x_m) < \epsilon. In simpler terms, this means that the terms of the sequence get closer and closer together as the sequence progresses.

Now, let's look at the proof of Proposition 8.7. In the proof, Browder is showing that the sequence \{S_m\} is a Cauchy sequence in the space \mathscr{L}(\mathbb{R}^n). This means that for any positive real number \epsilon, there exists an index N such that for all n,m \geq N, we have d(S_n, S_m) < \epsilon.

Let's break down the proof step by step. First, Browder defines the sequence \{S_m\} as a sequence of linear maps from \mathbb{R}^n to \mathbb{R}^n. This means that for each m, S_m is a linear map from \mathbb{R}^n to itself.

Next, Browder shows that \{S_m\} is a Cauchy sequence in \mathscr{L}(\mathbb{R}^n). This is done by showing that for any positive real number \epsilon, there exists an index N such that for all n,m \geq N, we have d(S_n, S_m) < \epsilon. This is done by using the definition of a Cauchy sequence in a metric space. Since \{S_m\} is a sequence of linear maps, we can use the metric d(S_n, S_m) = \sup_{\|x\| = 1} \|S_n(x) - S_m(x)\| to show that the terms of the sequence get closer and closer together as the sequence progresses.

Finally, Browder concludes that \{S_m\} is a Cauchy sequence in \mathscr{L}(\mathbb{R}^n) by showing that the sequence is convergent in \mathscr{L}(\mathbb
 

1. What is the definition of operator norm?

The operator norm is a way to measure the size or magnitude of a linear operator on a vector space. It is the maximum possible value of the operator's effect on a unit vector in the vector space.

2. How is operator norm related to Cauchy sequences?

In the context of functional analysis, operator norm is used to define the concept of a Cauchy sequence of operators. This means that the sequence of operators converges to a limit operator in the operator norm.

3. What is Proposition 8.7 in Browder's book?

Proposition 8.7 in Browder's book is a mathematical statement that relates to operator norm and Cauchy sequences. It states that if a sequence of operators is Cauchy, then it converges to a limit operator in the operator norm.

4. How is Proposition 8.7 useful in functional analysis?

Proposition 8.7 is useful in functional analysis because it provides a way to prove the convergence of Cauchy sequences of operators. This is important in studying the behavior and properties of linear operators on vector spaces.

5. Can Proposition 8.7 be applied to any type of operator?

Yes, Proposition 8.7 can be applied to any type of operator, as long as the vector space and the norm used to define the operator norm are appropriate for the type of operator being studied.

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