Proving Triangle Inequality for L-Normed Vector Space

In summary, the conversation is about proving the inequality |f(y1) - f(y2)| <= || y1 - y2|| for a given function f and norm || ||. The first step is to show that f(y2) <= || y1 - y2|| + f(y1) which is a sub-problem of a larger problem dealing with distance from a point to a set. It is also mentioned that f is defined as the distance from a point in R^n to a subset S of R^n. The conversation then continues with a discussion on how to prove this statement using the triangle inequality and the idea of infimum. The conversation ends with a summary of the inequalities and the conclusion that |f(y1)
  • #1
Mathman23
254
0
Hi

I'm given the following assignment which deals with to looks like an L-normed vectorspace:

Prove that,

[tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]

To prove this do I approach the above as a triangle inequality or as a cauchy-swartz inequality?

Best Regards,

Fred
 
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  • #2
What do you know about f ? How is the norm defined?
 
  • #3
Hello and thank You for Your reply,

f is defined as follows:

[tex]f: \mathbb{R}^n \rightarrow \mathbb{R}[/tex] and

[tex]y1, y2 \in \mathbb{R}^n[/tex]

Best Regards,

Fred

benorin said:
What do you know about f ? How is the norm defined?
 
  • #4
More must be given, (or I am just that tired) since

[tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]

does not, in general (under those conditions), hold for arbitrary f:R^n->R, like, say, put [tex]f(y)=2 ||y||[/tex] with y2=0 and y1=y gives

[tex]|f(y) - f(0)| = 2||y|| \not\leq || y - 0||[/tex]
 
  • #5
Hello again,

According to my textbook the first step is to show that

[tex] f(y_2) \leq || y_1 - y_2|| + f(y_1)[/tex]

The is a sub-problem of a problem which deals distance from a point to a set.

f is defined as the distance from a point in [tex] \mathbb{R}^n [/tex] to a subset S of [tex]\mathbb{R}^n[/tex]

and finally I'm suppose to conclude that f is Uniform continuites on [tex]
\mathbb{R}^n[/tex]

Any idears?

Best Regards,

Fred

benorin said:
More must be given, (or I am just that tired) since

[tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]

does not, in general (under those conditions), hold for arbitrary f:R^n->R, like, say, put [tex]f(y)=2 ||y||[/tex] with y2=0 and y1=y gives

[tex]|f(y) - f(0)| = 2||y|| \not\leq || y - 0||[/tex]
 
  • #6
Mathman23 said:
Hello again,

According to my textbook the first step is to show that

[tex] f(y_2) \leq || y_1 - y_2|| + f(y_1)[/tex]

The is a sub-problem of a problem which deals distance from a point to a set.

f is defined as the distance from a point in [tex] \mathbb{R}^n [/tex] to a subset S of [tex]\mathbb{R}^n[/tex]

and finally I'm suppose to conclude that f is Uniform continuites on [tex]
\mathbb{R}^n[/tex]

Any idears?

Best Regards,

Fred

Since f is defined as the distance from a point in [tex] \mathbb{R}^n [/tex] to a subset S of [tex]\mathbb{R}^n[/tex], we should have

[tex]f_S(y)=\mbox{inf}\left\{ \|x-y\| : x\in S\right\}[/tex] for [tex]y\in\mathbb{R}^n[/tex]

I put a subscript of S on f since f depends on S, but consider S fixed and supress the subscript from here on.

[tex] f(y_2) \leq || y_1 - y_2|| + f(y_1)[/tex] is the statement that

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex],

how could you prove that?
 
  • #7
benorin said:
Since f is defined as the distance from a point in [tex] \mathbb{R}^n [/tex] to a subset S of [tex]\mathbb{R}^n[/tex], we should have

[tex]f_S(y)=\mbox{inf}\left\{ \|x-y\| : x\in S\right\}[/tex] for [tex]y\in\mathbb{R}^n[/tex]

I put a subscript of S on f since f depends on S, but consider S fixed and supress the subscript from here on.

[tex] f(y_2) \leq || y_1 - y_2|| + f(y_1)[/tex] is the statement that

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex],

how could you prove that?



By showing that normed distance from x to y1 and from x to y2 are equal? Cause they belong to the same subset?

Best Regards
Fred
 
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  • #8
By the triangle inequality, for all [tex]x,y_1,y_2\in\mathbb{R}^n[/tex], we have

[tex]\|x-y_2\| \leq \| y_1 - y_2\| +\|x-y_1\|[/tex],

and since [tex]x\in S\mbox{ and }S\subset\mathbb{R}^n[/tex], we may restrict x to be in S and inf over x in S to obtain

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \mbox{inf}\left\{\| y_1 - y_2\| + \|x-y_1\| : x\in S\right\} = \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]
 
  • #9
Hello again and thank You for Your answer,

And this shows, that

[tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]

is true?

Best Regards

Fred

benorin said:
By the triangle inequality, for all [tex]x,y_1,y_2\in\mathbb{R}^n[/tex], we have

[tex]\|x-y_2\| \leq \| y_1 - y_2\| +\|x-y_1\|[/tex],

and since [tex]x\in S\mbox{ and }S\subset\mathbb{R}^n[/tex], we may restrict x to be in S and inf over x in S to obtain

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \mbox{inf}\left\{\| y_1 - y_2\| + \|x-y_1\| : x\in S\right\} = \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]
 
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  • #11
Hello again,


then

[tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]

since

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} - \| y_1 - y_2\| \leq \mbox{inf}\left\{\| y_1 - y_2\| + \|x-y_1\| : x\in S\right\} [/tex] ?

Best Regards

Fred

benorin said:
Swap y1 and y2, see what you get.
 
  • #12
Of this

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \mbox{inf}\left\{\| y_1 - y_2\| + \|x-y_1\| : x\in S\right\} = \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]

you need only first and last bits (including the <= sign), namely

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]

in this, swap y1 and y2, to get

[tex] \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \leq \| y_2 - y_1\| + \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\}[/tex]

and since [tex] \| y_2 - y_1\| = \| y_1 - y_2\| ,[/tex] we have this pair of inequalities

[tex]\boxed{\begin{array}{cc} \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \\ \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \end{array} } [/tex]

thinking of theses as being of the form

[tex]A\leq k+B[/tex]
[tex]B\leq k+A[/tex]

we then have

[tex]A-B\leq k[/tex] and
[tex]B-A\leq k[/tex]

thus [tex]|B-A| \leq k[/tex] i.e. [tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]
 
  • #13
Hello again, and thank You for Your answers,

I need to conclude that the function f is is uniformly continius on [tex]\mathbb{R}^n[/tex].

In order to show this by proving that definition of uniformly continious functions applies to my specific f function?

Best Regards,

Fred

benorin said:
Of this

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \mbox{inf}\left\{\| y_1 - y_2\| + \|x-y_1\| : x\in S\right\} = \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]

you need only first and last bits (including the <= sign), namely

[tex] \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\}[/tex]

in this, swap y1 and y2, to get

[tex] \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \leq \| y_2 - y_1\| + \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\}[/tex]

and since [tex] \| y_2 - y_1\| = \| y_1 - y_2\| ,[/tex] we have this pair of inequalities

[tex]\boxed{\begin{array}{cc} \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \\ \mbox{inf}\left\{ \|x-y_1\| : x\in S\right\} \leq \| y_1 - y_2\| + \mbox{inf}\left\{ \|x-y_2\| : x\in S\right\} \end{array} } [/tex]

thinking of theses as being of the form

[tex]A\leq k+B[/tex]
[tex]B\leq k+A[/tex]

we then have

[tex]A-B\leq k[/tex] and
[tex]B-A\leq k[/tex]

thus [tex]|B-A| \leq k[/tex] i.e. [tex]|f(y_1) - f(y_2)| \leq || y_1 - y_2||[/tex]
 
  • #14
What is the definition of a uniformly continuous function?
 
  • #15
Definition:uniformly continuous function

S is a subset of [tex]\mathbb{R}^n[/tex] and [tex]f: S \rightarrow \mathbb{R}[/tex] be a real valued function. The function f is said to be uniformly continuous, if there for every epsilon > 0, exists a delta > 0 such that,

|f(x) - f(y) | < epsilon

for all x,y \in S, which statisfies that ||x - y|| < delta

Do I then apply this definition to my given f(x) ? and use it to show how f(x) = 0, if x belongs to Y ?

Best Regards
Fred

benorin said:
What is the definition of a uniformly continuous function?
 
  • #16
Mathman23 said:
Do I then apply this definition to my given f(x) ? and use it to show how f(x) = 0, if x belongs to Y ?
Fred

Yes, if you wish to prove f is a uniformly continuous function do just that: the first bit is done, so just choose delta = epsilon and it is done.

And do U mean x belongs to S? Indeed it is a simple matter that f(x)=0 for every x in S, since (for fixed x) the shortest distance between x and y when y is allowed to be x is 0.
 
  • #17
benorin said:
Yes, if you wish to prove f is a uniformly continuous function do just that: the first bit is done, so just choose delta = epsilon and it is done.

And do U mean x belongs to S? Indeed it is a simple matter that f(x)=0 for every x in S, since (for fixed x) the shortest distance between x and y when y is allowed to be x is 0.

regarding uniform continuity

then according to the definition of uniformly continious functions then
[tex]|x - y| = |x| - |y|[/tex]

From here I'm a bit unsure

Do I do the following y = x + [tex]\delta[/tex] then [tex]x \geq 0 [/tex]

[tex]|x - (x+ \delta)| = \delta[/tex]

then [tex]|x - (x+ \delta)| < \epsilon[/tex]

futermore since [tex]\delta > 0[/tex] according to the definition:

[tex]\delta < \epsilon[/tex]

therefore f is uniformly continious.

Am I on the right path here?

Best Regards
Fred
 
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  • #18
Mathman23 said:
Definition:uniformly continuous function

S is a subset of [tex]\mathbb{R}^n[/tex] and [tex]f: S \rightarrow \mathbb{R}[/tex] be a real valued function. The function f is said to be uniformly continuous, if there for every epsilon > 0, exists a delta > 0 such that,

|f(x) - f(y) | < epsilon

for all x,y \in S, which statisfies that ||x - y|| < delta

Recall that we just proved that

[tex] |f(x) - f(y) | \leq \| x-y\| [/tex] for all [tex]x,y\in\mathbb{R}^n[/tex]

we now wish to show that

[tex]\mbox{ For every } \epsilon >0, \mbox{ there exists a } \delta >0\mbox{ such that }\| x-y\| < \delta \Rightarrow |f(x)-f(y)| <\epsilon [/tex]

So fix [tex]\epsilon >0[/tex]. Choose [tex] \delta = \epsilon [/tex] so that

[tex]\| x-y\| < \delta \Rightarrow |f(x)-f(y)| \leq \| x-y\| < \delta =\epsilon [/tex]

where we have used that bit of which I wrote "Recall that we just proved ..." to get the [tex] |f(x)-f(y)| \leq \| x-y\| [/tex] part of the above line.

--Ben
 
Last edited:
  • #19
Thank You again,

If have two small final followup questions:

(a) If S is closed set, and if x \notin S, then f(x) > 0

(b) if Y is closed, then

X = {x \in R^n | f(x) = 0}

I know in (a) that since x is not in S, then the metric distance between x,y is larger than zero, but does that explain f(x) > 0??

Any hints on how to solve (b) ?

Best Regards

Fred

benorin said:
Yes, if you wish to prove f is a uniformly continuous function do just that: the first bit is done, so just choose delta = epsilon and it is done.

And do U mean x belongs to S? Indeed it is a simple matter that f(x)=0 for every x in S, since (for fixed x) the shortest distance between x and y when y is allowed to be x is 0.
 
  • #20
"I know in (a) that since x is not in S, then the metric distance between x,y is larger than zero, but does that explain f(x) > 0??"

How do you know this is so? If had, say, S=(0,1) (the segment of the real line strictly between 0 and 1) what than is f(0) ?
 
  • #21
benorin said:
"I know in (a) that since x is not in S, then the metric distance between x,y is larger than zero, but does that explain f(x) > 0??"

How do you know this is so? If had, say, S=(0,1) (the segment of the real line strictly between 0 and 1) what than is f(0) ?

If S = (x=1, y = 0) then f(x) > 0 ??

I'm a bit lost here, any hints?

Sincerely
Fred
 
  • #22
This is a trick question:

benorin said:
How do you know this is so? If had, say, S=(0,1) (the segment of the real line strictly between 0 and 1) what then is f(0) ?

I was trying to ensure you understood why "I know in (a) that since x is not in S, then the metric distance between x,y is larger than zero..."

Understand that if y is the particular point in S whose distance to x is a minimum, then f(x)-||x-y||, that is to say the value of f(x) is that minimum distance between x and y.

A geometric example will help: We're in [tex]\mathbb{R}^2[/tex]. Let S be the unit square [0,1]x[0,1]. Now fix a point x in [tex]\mathbb{R}^2[/tex], I'll pick x=(2,1/2). Now f[(2,1/2)] can be determined as follows:

first, find the point in S (the unit square) which is closest to (2,1/2) (which is x in our example), and denote this "closest point" by y. So what is y? Well clearly y is on the right-hand edge of the unit square S since were it not, we could easily find a point in S that is closer to (2,1/2). Every point on the right-hand edge of S is of the form (1,t), where [tex]0\leq t\leq 1[/tex]. The distance [tex]d[/tex] between (1,t) and (2,1/2) is given by the usual distance formula

[tex]d=\sqrt{(2-1)^2+(1/2 -t)^2}=\sqrt{1+(1/2 -t)^2}[/tex]

and we seek the value of t with [tex]0\leq t\leq 1[/tex] that minimizes [tex]d[/tex]. We reason that the minimum occurs for t=1/2 and hence determine that y=(1,1/2).

Second, we recall that f(x) is the distance between x and y, i.e. f(x)=||x-y||, and, in this example, the distance is given by the usual formula, so we have

[tex]f[(2,1/2)] = ||(2,1/2)-(1,1/2)||=\sqrt{(1-2)^2+(1/2 -1/2)^2}=1[/tex].

Which makes sense, if you think about the diagram (you should draw one). End Geometric Example.

Notice that in the above example S was a closed set. In the so-called trick question I posed at the top of this post, S was (0,1) on the real line (which is not a closed set) and I had asked what then is f(0)? We would first determine the point y in (0,1) whose distance to 0 is a minimum, at least we would if there were such a point, but 0 is a limit point of (0,1) so there are infinitely many points in (0,1) infinitely close to 0, but there is always one closer... strictly speaking, there is no point y in (0,1) of minimum distance to 0, but there is an infimum (the inf) of the set of such distances, which is 0 (this is the value of the distance, not the point y at which it occurs), and thus f(0)=0. But notice that 0 (the point) is not in S, and yet we have f(0)=0 and not f(0)>0. Why?


Answer: S is not closed. In the example where S was the unit square in R^2, S was closed. Can you find a point z in R^2 that is not is S such that f(z)=0? Why not?
 
Last edited:
  • #23
Hi all,

I been thinking regarding:

(a) If S is closed set, and if [tex]x \notin S[/tex], then f(x) > 0

(b) if Y is closed, then

[tex]X = \{x \in R^n | f(x) = 0 \}[/tex]

I know that according to my textbook the definition of a closed set is a as follows:

A subset F of [tex]\mathbb{R}^n[/tex] is closed if its complement:

\mathbb{R}^n \mathrm{\} [tex] F = \{ x \in R^n | x \notin F \}[/tex] is open.

Do I then need to show that x is an internal point of [tex]\mathbb{R}^n[/tex]


Sincerely

Fred
 
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1. What is the "Triangle Inequality" in a L-Normed Vector Space?

The Triangle Inequality in a L-Normed Vector Space 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 other words, the shortest distance between two points is a straight line.

2. Why is it important to prove the Triangle Inequality in a L-Normed Vector Space?

Proving the Triangle Inequality in a L-Normed Vector Space is important because it is a fundamental property of vector spaces. It helps us understand the relationship between distance and direction in a vector space and is essential for understanding many mathematical concepts, such as convergence and continuity.

3. How is the Triangle Inequality proved in a L-Normed Vector Space?

The Triangle Inequality can be proved using the properties of a L-Normed Vector Space, such as the triangle inequality for scalars and the properties of L-norms. By carefully applying these properties, we can show that the sum of the lengths of two vectors in a L-Normed Vector Space is always greater than or equal to the length of the third vector, which proves the Triangle Inequality.

4. What are some real-world applications of the Triangle Inequality in a L-Normed Vector Space?

The Triangle Inequality in a L-Normed Vector Space has many real-world applications, including optimization problems in economics and engineering, distance calculations in physics and computer science, and error analysis in statistics and data analysis. It is also used in various areas of mathematics, such as functional analysis and differential geometry.

5. Are there any generalizations of the Triangle Inequality in a L-Normed Vector Space?

Yes, there are several generalizations of the Triangle Inequality in a L-Normed Vector Space, such as the reverse triangle inequality, which states that the length of any side of a triangle must be greater than or equal to the absolute value of the difference between the lengths of the other two sides. There are also generalizations for other types of norms, such as p-norms and infinity-norms.

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