Why do we need infinite dimensional vector spaces?

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Discussion Overview

The discussion revolves around the necessity and applications of infinite dimensional vector spaces in mathematics and physics. Participants explore various examples and contexts where these spaces are relevant, including polynomial spaces and function spaces.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants question the need for infinite dimensional vector spaces, particularly in relation to finite dimensional spaces like R^3.
  • It is suggested that infinite dimensional spaces are necessary to represent polynomials, as a finite basis cannot span all polynomial degrees.
  • Functional analysis is mentioned as a field that utilizes infinite dimensional function spaces, with L2(X) being highlighted as a significant example.
  • Another participant introduces the concept of functions from an infinite domain to a ring as an example of an infinite dimensional vector space.
  • A challenge is raised regarding the definition of a function as a vector space, questioning the field over which it is defined and the nature of its elements.

Areas of Agreement / Disagreement

Participants express differing views on the definition and necessity of infinite dimensional vector spaces, with some providing examples while others raise questions and challenges. The discussion remains unresolved regarding the foundational aspects of these spaces.

Contextual Notes

There are unresolved questions about the definitions of vector spaces in the context of functions and the fields over which they are defined. Additionally, the implications of using infinite dimensional spaces in various mathematical frameworks are not fully explored.

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 independent (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.
 
Last edited:
I think you should reconsider that example, NateTG. How is *a* function a vector space? Over what field? And what are its elements
 

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