L2 Norm of +Infinity: Admitted & Defined

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In summary, - The inner product space has an infinite number of elements, so the L2-norm of many vectors would be eventually equal to +Infinity.- Provided you restrict yourself to the subspace of sequences that have a finite norm, that is, for which the infinite series \sum x_i^2 converges to a finite limit. Ignore the series that do not converge -- those sequences are not elements of the inner product space.- Defining an orthonormal basis for an inner product space is difficult, as the space of sequences of length N does not have an orthonormal basis.- If you consider the space of the real functions obtainable by
  • #1
mnb96
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Hello,
I have a (infinite dimensional) vector space and defined an inner product on it.
The vectors element are infinite sequence of real numbers [tex](x_1, x_2,\ldots)[/tex].
The inner product has the common form: [tex]x_iy_i[/tex]

The problem now is that the vectors have an infinite number of elements, so the L2-norm of many vectors would be eventually equal to +Infinity.

- Is that admitted?
- How can one define an orthonormal base for such a space?
 
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  • #2
Are the vectors sequences that are eventually zero?
 
  • #3
mnb96 said:
Hello,
I have a (infinite dimensional) vector space and defined an inner product on it.
The vectors element are infinite sequence of real numbers [tex](x_1, x_2,\ldots)[/tex].
The inner product has the common form: [tex]x_iy_i[/tex]
You mean [tex]\sum^{\infty}_{i=1}x_iy_i[/tex]
mnb96 said:
The problem now is that the vectors have an infinite number of elements, so the L2-norm of many vectors would be eventually equal to +Infinity.

- Is that admitted?
Yes, provided you restrict yourself to the subspace of sequences that have a finite norm, that is, for which the infinite series [itex]\sum x_i^2[/itex] converges to a finite limit. Ignore the series that do not converge -- those sequences are not elements of the inner product space.

mnb96 said:
- How can one define an orthonormal base for such a space?
How would you answer this for the space of sequences of length N? It's almost the same answer here.

(By the way, this post should really be in the Linear & Abstract Algebra forum!)
 
  • #4
I am not sure about what I am about to say, but as far as I understood, the inner-product that goes to +Infinity is always admitted: there is nothing in the definition of inner-product that prevents it to be so.

Since an inner-product-space is apparently just a vector space with an inner product, we have to admit that also those series [tex]\sum^{\infty}_{i=1}x_iy_i[/tex] which do not converge are allowed. So, the vectors with Infinite norm are still in the inner product space.

If you add the requirement that those series have always to converge, then you are defining an Hilbert-Space (complete metric).

Now, if we have an Hilbert-space, it is probably easier to define an orthonormal basis.
I am not sure it is possible to define always an orthonormal base for inner products, as I can't see how you could (for example) normalize the squared integral of a sinusoid extendind through the whole real line.
 
  • #5
I think there is confusion here as to what, exactly L2 means.

The space l2 (small l) is defined as "the set of all infinite sequences {an} such that [itex]\sum a_n^2[/itex] is finite". The l2 norm is then defined as [itex]\sum a_n^2[/itex] which is now guaranteed to be finite. And that norm can be derived from an "inner product" [itex]\{a_n\}\cdot\{b_n\}= \sum a_nb_n[/itex] which can be shown to always exist.

The space L2 Is defined as the set of functions, f(x), is defined to be the set of functions, defined on some set A, such that [itex]\int_A (f(x))^2 dx[/itex] is finite. Now the "L2" is defined to be that integral which is not guaranteed to be finite. And, again, it can be derived from the inner product, [itex]f\cdot g= \int_A f(x)g(x) dx[/itex].

That is, the "L2 norm" and "l2" are not defined independently of the set of "vectors".
 
  • #6
thanks for the clarification!
Now, in this context, could you please explain what happens if we consider the space of the real functions obtainable by sum of complex sinusoids?

The complex sinusoids (from -Inf to +Inf) have infinite norm. This means we are not in [tex]L_{2}[/tex] anymore. However the sinusoids are still orthogonal(?), so we must conclude they are a basis for some space, but what space?

In other words, when we simply take a Fourier Transform of a function from -Inf to +Inf, what are we actually doing?
 
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  • #7
To ease explanation, consider the complex exponentials e-ikx instead of sines and cosines, and consider the complex exponential version of the Fourier transform.

If you give it a bit of thought, you will realize that the Fourier transform of e-ikx is not actually a function, but rather a delta "function" (really, the delta distribution). Why? e-ikx is a perfect wave of a single frequency, so it's Fourier transform has all of the weight concentrated at a single point k, and no weight at any other frequencies.

Therefore the natural space to think about Fourier transforms of things like e-ikx is a space of distributions. The space commonly used is the dual space to the Schwarz space, and then the Fourier transform is F:S'->S' rather than F:L2->L2.

The complex exponentials might form a Schauder basis for S', though I highly doubt it. They certainly don't form a Hamel basis. This is actually an interesting question.
 
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1. What is the L2 Norm of +Infinity?

The L2 Norm of +Infinity refers to the mathematical concept of taking the absolute value of a number raised to the power of two and then taking the square root of that value. In the case of +Infinity, the value would be infinity.

2. How is the L2 Norm of +Infinity defined?

The L2 Norm of +Infinity is defined as the limit of the L2 Norm of a vector as its magnitude approaches infinity. In other words, as the vector becomes larger and larger, the L2 Norm will approach infinity as well.

3. Why is the L2 Norm of +Infinity important?

The L2 Norm of +Infinity is important in various fields of science, particularly in data analysis and machine learning. It is commonly used as a measure of error or distance between vectors, and can help in identifying outliers or anomalies in data.

4. How is the L2 Norm of +Infinity calculated?

The L2 Norm of +Infinity is calculated by taking the absolute value of each component in a vector, raising it to the power of two, summing all the values, and then taking the square root of the final result. In the case of +Infinity, the final result would be infinity.

5. What are some real-world applications of the L2 Norm of +Infinity?

The L2 Norm of +Infinity is commonly used in image and signal processing, as well as in machine learning algorithms such as support vector machines and k-nearest neighbors. It can also be used in optimization problems, such as finding the minimum distance between two points in a high-dimensional space.

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