Why do we need infinite dimensional vector spaces?

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Infinite dimensional vector spaces are essential for representing complex mathematical constructs, such as polynomials, which cannot be fully captured by finite dimensional spaces. For instance, the vector space of all polynomials over R is infinite dimensional because any finite basis can only represent polynomials up to a certain degree. Functional analysis heavily relies on these spaces, particularly in contexts like L2(X), which includes functions whose squares are Lebesgue integrable. Additionally, spaces of functions from an infinite domain to a ring illustrate how infinite dimensions allow for a broader range of representations. The discussion highlights the necessity of infinite dimensional vector spaces in advanced mathematical applications.
Ratzinger
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We have x=x1(1,0,0) + x2(0,1,0) + x3(0,0,1) to represent R^3. That's a finite dimensional vector space. So what do we need infinite dimensional vector space for? Why do we need (1,0,0,...), (0,1,0,0,...), etc. bases vectors to represent R^1 ?
 
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Ratzinger said:
We have x=x1(1,0,0) + x2(0,1,0) + x3(0,0,1) to represent R^3. That's a finite dimensional vector space. So what do we need infinite dimensional vector space for? Why do we need (1,0,0,...), (0,1,0,0,...), etc. bases vectors to represent R^1 ?
To represent R^1, you only need 1 vector. A vector space of dimension n is spanned by a basis of n vectors, just as in your example of R^3. This is because a basis needs to span the vector space (which means you need *at least* n vectors) and has to be linearly independant (which means you can only have *at most* n vectors) which makes the number of vectors in the basis exactly n.

Then, what do we need vector spaces with infinite dimension? Consider the vectorspace \mathbb{R}\left[ X \right] which is the vector space of all polynomials in x over R. This is trivially an infinite dimensional vector space since a finite number of vectors in a basis contains a vector with a maximum degree r, meaning that x^(r+1) and higher cannot be formed.
 
"Functional Analysis" makes intensive use of "function spaces"- infinite dimensional vector spaces of functions satisfying certain conditions. TD gave a simple example- the space of all polynomials. Perhaps the most important is L2(X), the vector space of all functions whose squares are Lebesque integrable on set X.
 
Another familiar example of an infinite dimensional vector space is functions from an infinite domain to a ring
Consider that the space of functions
f:A \rightarrow R
from some set A to a ring R
is a vector space with dimensions indexed on A
since we have a vector
f(a)=r
or
f_a=r
Scalar multiplication, and vector addition are performed using the ring.
 
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I think you should reconsider that example, NateTG. How is *a* function a vector space? Over what field? And what are its elements
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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