Proof of an inner product space

In summary, the normed linear space l_{\infty}^{2} is not an inner product space because there exists a counterexample, such as x=(0,3) and y=(2,5), where the parallelogram law does not hold. This is due to an incorrect definition of the space, as it should have a max norm instead of a power root norm.
  • #1
cabin5
18
0

Homework Statement


Prove that the normed linear space [tex]l_{\infty}^{2}[/tex] is not an inner product space.


Homework Equations


parallelogram law;
[tex]\left\|x+y\right\|^2+\left\|x-y\right\|^2=2\left\|x\right\|^2+2\left\|y\right\|^2[/tex]


The Attempt at a Solution


Well, I tried to apply parallelogram law to the [tex]l_{\infty}^{2}[/tex] space where

[tex]x=(\alpha^1,\alpha^2)[/tex] and [tex]y=(\beta^1,\beta^2)\in l_{\infty}^{2} [/tex] .

[tex]\left\|x\right\|=max\left\{\left|\alpha^1\right|,\left|\alpha^2\right|\right\} and

\left\|y\right\|=max\left\{\left|\beta^1\right|,\left|\beta^2\right|\right\} [/tex]

If one puts these norms into the parallelogram law equation, one gets a fuzzy expression on both sides of the equation, therefore it is important to put out expressions inside the max{} function which I could not achieve to do.

Is there another method to solve this problem or am I misapplying the law to [tex]l_{\infty}^{2}[/tex] space?
 
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  • #2
You only need a counterexample to show it's not an inner product space. Start putting some actual numbers in for the alphas and betas.
 
  • #3
Is it a mathematically correct method?
 
  • #4
cabin5 said:
Is it a mathematically correct method?

What would not be 'mathematical' or 'correct' about it? If I claim all primes are odd, and somebody points out 2 is even, that's enough to prove me wrong. Just do it.
 
  • #5
Yes, as Dick said, proving that a general statement is NOT true by counterexample is a perfectly correct method.
 
  • #6
thanks for the post!
 
  • #7
well,
I tried the parallelogram law for x=(0,3) and y=(2,5) and it perfectly worked on both sides of the equation.
Should I choose the complex field for that space?

What's wrong with that?
 
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  • #8
You must have put some effort into find a case where it works. Almost all other cases don't work. Like x=(1,0) and y=(0,1). It has to work for all cases or your norm doesn't come from an inner product.
 
  • #9
Finally, It worked :)
I thought any ordered pair would work as a counterexample.

Thanks a lot!
 
  • #10
No, and ordered pair won't work because R2 DOES make a inner product space. This question was about [itex]l_\infty^2[/itex]. Your counterexample must be from that space. What are the vectors in that space?
 
  • #11
HallsofIvy said:
No, and ordered pair won't work because R2 DOES make a inner product space. This question was about [itex]l_\infty^2[/itex]. Your counterexample must be from that space. What are the vectors in that space?

I took it to be R^2 with the max norm. You think it's bounded functions on R^2, right? It's still pretty easy to find a counterexample with bounded functions.
 
  • #12
I have no clue whether one must use a bounded function or not in order to prove that.
 
  • #13
The question is, what is the definition of the space l^2_infinity? Can you tell us what it is?
 
  • #14
Sorry for so late reply:

The definition of [tex]l_{\infty}^{n} [/tex] :
On the linear space [tex]V_{n}(F) [/tex] with the infinity norm defined by
[tex]\left\|x\right\|_p=\left[\sum^{\infty}_{i=1}\left|\alpha^{i}\right|^p\right]^{1/p}[/tex]

where [tex] x=(\alpha^i) [/tex].
The corresponding linear space to this norm is denoted by [tex]l_{\infty}^{n} [/tex].
 
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  • #15
Dick said:
I took it to be R^2 with the max norm. You think it's bounded functions on R^2, right? It's still pretty easy to find a counterexample with bounded functions.

No, I was assuming infinite sequences {an} such that {a2n} was summable.
 
  • #16
so [tex]l_{\infty}^{2} [/tex] defines that norm which is basically the square of root total sum of square of each element of 2 vectors defined over R^2 field.

Eventually , I think that you're example is correct, but besides I have no idea about whether functions defined in [tex]l_{\infty}^{2} [/tex] is bounded or not (since I didn't take any real analysis course during undergrad.)
 
  • #17
HallsofIvy said:
No, I was assuming infinite sequences {an} such that {a2n} was summable.

I don't think that's what the problem is supposed to be about.
 
  • #18
cabin5 said:
so [tex]l_{\infty}^{2} [/tex] defines that norm which is basically the square of root total sum of square of each element of 2 vectors defined over R^2 field.

Eventually , I think that you're example is correct, but besides I have no idea about whether functions defined in [tex]l_{\infty}^{2} [/tex] is bounded or not (since I didn't take any real analysis course during undergrad.)

I think what you just described is l^2_2. The 'infinity' usually designates a max norm (supremum) rather than a power root norm.
 
  • #19
oh, ****!

you're right , It was supposed to be max norm! I miswrote the definition.
 

What is an inner product space?

An inner product space is a mathematical structure that consists of a vector space equipped with an inner product, which is a function that takes two vectors as inputs and returns a scalar value. It allows for the calculation of lengths and angles of vectors within the space, and also defines the notion of orthogonality between vectors.

What is the purpose of proving an inner product space?

Proving an inner product space is important because it provides a rigorous mathematical framework for understanding and manipulating vectors in a given space. It allows for the development of powerful techniques and theorems, such as the Cauchy-Schwarz inequality and the Gram-Schmidt process, that can be applied to a wide range of problems in mathematics and other fields.

What are the axioms of an inner product space?

The axioms of an inner product space are linearity in the first argument, symmetry, and positive definiteness. Linearity in the first argument means that the inner product is linear with respect to the first vector. Symmetry means that the order of the vectors does not matter, and positive definiteness means that the inner product of a vector with itself is always positive (unless the vector is the zero vector).

What are some examples of inner product spaces?

Some examples of inner product spaces include Euclidean spaces, where the inner product is defined as the dot product; function spaces, where the inner product is defined as the integral of the product of two functions; and complex vector spaces, where the inner product is defined as the Hermitian inner product.

How is the inner product related to the concept of orthogonality?

The inner product is closely related to the concept of orthogonality. Two vectors in an inner product space are considered orthogonal if their inner product is equal to zero. This means that they are perpendicular to each other and form a 90-degree angle. The concept of orthogonality is important in many areas, such as geometry, physics, and signal processing.

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