R^n as a normed space .... D&K Lemma 1.1.7 ....

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In summary, the square root of a positive real number is defined as a non-negative number that when squared gives the original number. To prove that the function ##\sqrt{\cdot}## is order preserving, we must show that for any two positive real numbers ##a## and ##b##, if ##a < b##, then ##\sqrt{a} \leq \sqrt{b}##. This can be shown by using the definition of the square root and noting that if ##a < b##, then ##a^2 < b^2##, so ##\sqrt{a^2} < \sqrt{b^2}##, which simplifies to ##|a| < |b|##
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I am reading "Multidimensional Real Analysis I: Differentiation by J. J. Duistermaat and J. A. C. Kolk ...

I am focused on Chapter 1: Continuity ... ...

I need help with an aspect of Lemma 1,1,7 (iv) ...

Duistermaat and Kolk"s Lemma 1.1.7 reads as follows:
D&K - 1 -  Lemma 1.1.7 ... PART 1 ... .png

D&K - 2 -  Lemma 1.1.7 ... PART 2 ... . .png

At the start of the proof of (iv) we read the following:

" ... ...##\mid x_j \mid \le \left( \sum_{ 1 \le j \le n } \mid x_j \mid^2 \right)^{ \frac{ 1 }{ 2 } } = \| x \|## ... ... ... "
Suppose now we want to show, formally and rigorously that## \mid x_j \mid \le \left( \sum_{ 1 \le j \le n } \mid x_j \mid^2 \right)^{ \frac{ 1 }{ 2 } }##Maybe we could start with (obviously true ...)

##\mid x_j \mid = ( \mid x_j \mid^2 )^{ \frac{ 1 }{ 2 } } ## ... ... ... ... ... (1)

then we can write

##\mid x_j \mid = ( \mid x_j \mid^2 )^{ \frac{ 1 }{ 2 } } \le ( \mid x_1 \mid^2 + \mid x_2 \mid^2 + \ ... \ ... \ + \mid x_j \mid^2 + \ ... \ ... \ + \mid x_n \mid^2 )^{ \frac{ 1 }{ 2 } }##and we note that##( \mid x_1 \mid^2 + \mid x_2 \mid^2 + \ ... \ ... \ + \mid x_j \mid^2 + \ ... \ ... \ + \mid x_n \mid^2 )^{ \frac{ 1 }{ 2 } } = ( x_1^2 + x_2^2 + \ ... \ ... \ + x_j^2 + \ ... \ ... \ + x_n^2 )^{ \frac{ 1 }{ 2 } } = \| x \| ## ... ... ... ... (3)... BUT ... I worry that (formally anyway) (1) is invalid ... or compromised at least ...

... for suppose for example ##x_j = -3## then ...... we have LHS of (1) = ##\mid x_j \mid = \mid -3 \mid = 3##... BUT ...

RHS of (1) = ##( \mid x_j \mid^2 )^{ \frac{ 1 }{ 2 } } = ( \mid -3 \mid^2 )^{ \frac{ 1 }{ 2 } } = ( 3^2 )^{ \frac{ 1 }{ 2 } } = 9^{ \frac{ 1 }{ 2 } } = \pm 3##My question is as follows:

How do we deal with the above situation ... and

how do we formally and rigorously demonstrate that ##\mid x_j \mid \le \left( \sum_{ 1 \le j \le n } \mid x_j \mid^2 \right)^{ \frac{ 1 }{ 2 } }##Hope someone can help ...

Peter
 

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  • #2
I can't read your question. The latex does not compile properly.
 
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  • #3
Math_QED said:
I can't read your question. The latex does not compile properly.
Yes, I know ... just correcting it now ... my apologies ...
Peter***EDIT*** Should be OK now ... apologies for problems ...
 
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  • #4
I think you misquoted what is written.

It clearly says:

##|x_j| \leq \left(\sum |x_i|^2\right)^{1/2}##

Not:

##|x_j| \leq \sum |x_i|^2##

The former is true, the latter not (Take the vector ##(1/2,0,0, \dots, 0)##, then ##x_1 = 1/2 \geq 1/4)##
 
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  • #5
Math_QED said:
I think you misquoted what is written.

It clearly says:

##|x_j| \leq \left(\sum |x_i|^2\right)^{1/2}##

Not:

##|x_j| \leq \sum |x_i|^2##
Sorry ... just now corrected that as well ...

Peter
 
  • #6
Math Amateur said:
Sorry ... just now corrected that as well ...

Peter

Okay, now I can see what the question is haha.

Step 1: Show that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a \leq b \implies \sqrt{a} \leq \sqrt{b}##
Step 2: Note that ##|x_j|^2 \leq \sum_i |x_i|^2##
Step 3: Apply step 1: ##|x_j| = \sqrt{|x_j|^2} \leq \sqrt{\sum_i |x_i|^2}##
 
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Math_QED said:
Okay, now I can see what the question is haha.

Step 1: Show that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}##
Step 2: Note that ##|x_j|^2 \leq \sum_i |x_i|^2##
Step 3: Apply step 1: ##|x_j| = \sqrt{|x_j|^2} \leq \sqrt{\sum_i |x_i|^2}##

Thanks Math_QED ...

Reflecting on what you have said...

Peter
 
  • #8
Math_QED said:
Okay, now I can see what the question is haha.

Step 1: Show that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}##
Step 2: Note that ##|x_j|^2 \leq \sum_i |x_i|^2##
Step 3: Apply step 1: ##|x_j| = \sqrt{|x_j|^2} \leq \sqrt{\sum_i |x_i|^2}##
Hi Math_QED ...

You write:

" ... ... Step 1: Show that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}## ..."In carrying out Step 1 I am unsure of how to deal with the fact that ##\sqrt{a} = \pm a## and ##\sqrt{b} = \pm b## ... ... can you help?

(Must say that given ##\sqrt{a} = \pm a## ... it doesn't seem as if ##\sqrt{\cdot}## is order preserving ... )

Hope you can help further ...

Peter
 
  • #9
Math Amateur said:
Hi Math_QED ...

You write:

" ... ... Step 1: Show that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}## ..."In carrying out Step 1 I am unsure of how to deal with the fact that ##\sqrt{a} = \pm a## and ##\sqrt{b} = \pm b## ... ... can you help?

(Must say that given ##\sqrt{a} = \pm a## ... it doesn't seem as if ##\sqrt{\cdot}## is order preserving ... )

Hope you can help further ...

Peter

There seems to be a misconception here. ##\sqrt{x} \neq \pm x## (only whenever ##x = 0,1##). It is true that ##\sqrt{x^2} = |x|## though.

Recall that ##\sqrt{x} = y :\iff y^2 = x## by definition.
 
  • #10
Math_QED said:
There seems to be a misconception here. ##\sqrt{x} \neq \pm x## (only whenever ##x = 0,1##). It is true that ##\sqrt{x^2} = |x|## though.

Recall that ##\sqrt{x} = y :\iff y^2 = x## by definition.
Hi Math_QED,

You're right of course ... but I think we have to add to your definition that ##y## is non-negative, that is ##y \in ( 0 , \infty )## ... Is that right?

Here is the definition of the square root of a positive real number from Ethan D. Bloch's book: "The Real Numbers and Real Analysis"
Bloch -  Defn 2.6.10  only ... .png

Still not sure how to prove that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}## ...

... BUT ... if the square root of a number is positive ... then surely my equation (1) is valid ...

Peter
 

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  • #11
Math Amateur said:
Hi Math_QED,

You're right of course ... but I think we have to add to your definition that ##y## is non-negative, that is ##y \in ( 0 , \infty )## ... Is that right?

Here is the definition of the square root of a positive real number from Ethan D. Bloch's book: "The Real Numbers and Real Analysis"View attachment 221204
Still not sure how to prove that ##\sqrt{\cdot}: \mathbb{R}^+ \to \mathbb{R}## is an order preserving function, i.e. ##a < b \implies \sqrt{a} \leq \sqrt{b}## ...

... BUT ... if the square root of a number is positive ... then surely my equation (1) is valid ...

Peter

Yes, that definition is completely correct (at least in the context of real numbers), but contains something that should be proven, namely that the number x is unique.

I'm not sure either why it excludes the possibility that ##p=0##, since clearly ##0^2 = 0##

Well, let's start with the definition you provide.

Suppose ##0 \leq a \leq b##. By definition, there exist positive numbers ##x,y \in \mathbb{R}^+## such that ##a = x^2## and ##b = y^2##. That ##a \leq b## means exactly that ##x^2 \leq y^2## or equivalently ##x^2 - y^2 = (x-y)(x+y) \leq 0##

Can you proceed now? You have to show that ##x \leq y##

Hint 1: You will have to continue from ##(x-y)(x+y) \leq 0##
Hint 2: When is a product of 2 real numbers negative?

EDIT: Also, I made a small typo when writing the order preserving property:

##a < b## needs to be replaced by ##a \leq b##, so we must prove ##a \leq b \implies \sqrt{a} \leq \sqrt{b}##
 
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Hi Math_QED ,

Thanks for the hints ...

Well ... we want to show that for ##a \ge 0## and ##b \ge 0## that we have

##a \le b \Longrightarrow \sqrt{a} \le \sqrt{b}## ... ... ... ... ... (1)

Now put ##a = x^2## and ##b = y^2 ## ... ... ... then (1) becomes ... ... ...

##x^2 \le y^2 \Longrightarrow x \le y## ... ... ... ... ... (2)

Now ... ... ##x^2 \le y^2 \Longrightarrow ( x - y ) ( x + y ) \le 0## ... ... ... ... ... (3)

But ... ... ##x + y \ge 0## ... ... ... ... ... (4)

Hence we have ... ... (3), (4) ##\Longrightarrow ( x - y ) \le 0 \ \ \Longrightarrow x \le y## ...Is that correct?

Peter
 
  • #13
Math Amateur said:
Hi Math_QED ,

Thanks for the hints ...

Well ... we want to show that for ##a \ge 0## and ##b \ge 0## that we have

##a \le b \Longrightarrow \sqrt{a} \le \sqrt{b}## ... ... ... ... ... (1)

Now put ##a = x^2## and ##b = y^2 ## ... ... ... then (1) becomes ... ... ...

##x^2 \le y^2 \Longrightarrow x \le y## ... ... ... ... ... (2)

Now ... ... ##x^2 \le y^2 \Longrightarrow ( x - y ) ( x + y ) \le 0## ... ... ... ... ... (3)

But ... ... ##x + y \ge 0## ... ... ... ... ... (4)

Hence we have ... ... (3), (4) ##\Longrightarrow ( x - y ) \le 0 \ \ \Longrightarrow x \le y## ...Is that correct?

Peter

Yes, your proof is correct!

Can you proceed with the original problem now?
 
  • #14
Yes ... OK with that now ...

Thanks ...

Peter
 

FAQ: R^n as a normed space .... D&K Lemma 1.1.7 ....

1. What is R^n as a normed space?

R^n, or n-dimensional Euclidean space, is a mathematical concept that represents a set of all ordered n-tuples of real numbers. It can be thought of as a space with n axes, each representing a different dimension. As a normed space, R^n has a metric structure that allows for the measurement of distances between points.

2. What is the significance of D&K Lemma 1.1.7 in relation to R^n as a normed space?

D&K Lemma 1.1.7, also known as the "triangle inequality", is a fundamental property of normed spaces. It states that the length of any side of a triangle must be less than or equal to the sum of the lengths of the other two sides. In R^n, this lemma is important for defining the triangle inequality as a metric, which allows for the measurement of distances between points in the space.

3. How does the D&K Lemma 1.1.7 relate to the concept of a norm in R^n?

The D&K Lemma 1.1.7 forms the foundation for the definition of a norm in R^n. A norm is a mathematical function that assigns a positive length or size to each vector in the space. It must follow the properties of the D&K Lemma, as it is a necessary condition for a function to be considered a norm.

4. Can the D&K Lemma 1.1.7 be applied to any normed space, or only R^n?

The D&K Lemma 1.1.7 can be applied to any normed space, as it is a fundamental property of all metric spaces. However, it is most commonly used in the context of R^n, as it allows for the measurement of distances between points in this specific space.

5. How does the D&K Lemma 1.1.7 impact mathematical calculations in R^n?

The D&K Lemma 1.1.7 plays a crucial role in mathematical calculations in R^n, as it allows for the use of the triangle inequality to simplify and solve problems involving distances between points. It also helps to establish the properties of a norm, which is used in many mathematical operations and proofs in R^n.

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