What Are the Key Questions About Inner Product Spaces?

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Discussion Overview

The discussion revolves around key questions regarding inner product spaces, focusing on definitions, properties, and examples of inner products in arbitrary n-dimensional vector spaces over the real numbers. The scope includes theoretical aspects and mathematical reasoning.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant questions whether the value of the inner product (v,w) is independent of the choice of basis used to compute it.
  • Another participant proposes that it is possible to define an inner product on an arbitrary n-dimensional vector space such that the inner product of two linearly independent vectors is non-zero, providing two cases: one where the vectors are not orthogonal and another where they are orthogonal.
  • Examples are given involving skewing the space and shuffling the dual space to achieve non-zero inner products for orthogonal vectors.
  • A later reply challenges the validity of the proposed inner products, noting that one example does not satisfy the symmetry condition required for inner products.
  • Another participant acknowledges the requirement for symmetry in the definition of inner products and suggests a modified inner product that meets the criteria under certain conditions.
  • One participant expresses understanding of the conditions under which the inner product of a vector with itself vanishes only for the zero vector.

Areas of Agreement / Disagreement

Participants express differing views on the validity of certain inner product definitions and whether they meet the necessary conditions. There is no consensus on the examples provided, and the discussion remains unresolved regarding the implications of the proposed inner products.

Contextual Notes

Some definitions and conditions for inner products are debated, particularly regarding symmetry and the implications of specific matrix representations. The discussion highlights the complexity of defining inner products in arbitrary vector spaces.

samkolb
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I have some questions about inner product sapces.

1. If V is a vector space over R and ( , ):VxV-->R is an inner product on V, then for v,w in V, is the value of (v,w) independent of my choice of basis for V used to compute (v,w)?

2. If V is an arbitrary n dimensional vector space over R, are there some standard ways to define an inner product on V?

3. If V is an arbitrary n dimensional vector space over R, and v and w are linearly independent vectors in V, is it always possible to define an inner product on V such that (v,w) is not zero?
 
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samkolb said:
3. If V is an arbitrary n dimensional vector space over R, and v and w are linearly independent vectors in V, is it always possible to define an inner product on V such that (v,w) is not zero?
I believe so.

Case 1: v and w are not orthogonal ... done.

Case 2: v and w are orthogonal according to "the naive" inner product. You can either skew the space or shuffle the dual space.

Example 1, skewing the space
[tex] v\rightarrow\left(\begin{array}{c}1\\0\end{array}\right)<br /> \qquad<br /> w\rightarrow\left(\begin{array}{c}0\\1\end{array}\right)[/tex]
inner product:
[tex] (,)\rightarrow\left(\begin{array}{cc}1&1\\0&1\end{array}\right)[/tex]
i.e.
[tex] (v,w)<br /> \rightarrow<br /> \left(\begin{array}{cc}1&0\end{array}\right)\left(\begin{array}{cc}1&1\\0&1\end{array}\right)\left(\begin{array}{c}0\\1\end{array}\right)<br /> =<br /> \left(\begin{array}{cc}1&0\end{array}\right)\left(\begin{array}{c}1\\1\end{array}\right)<br /> \textrm{ or }<br /> \left(\begin{array}{cc}1&1\end{array}\right)\left(\begin{array}{c}0\\1\end{array}\right)<br /> =<br /> 1[/tex]
This sort of inner product actually occurs in, for example, crystalography.

Example 2, shuffling the dual space:
[tex] v\rightarrow\left(\begin{array}{c}1\\0\end{array}\right)<br /> \qquad<br /> w\rightarrow\left(\begin{array}{c}0\\1\end{array}\right)[/tex]
inner product:
[tex] (,)\rightarrow\left(\begin{array}{cc}0&1\\-1&0\end{array}\right)[/tex]
i.e.
[tex] (v,w)<br /> \rightarrow<br /> \left(\begin{array}{cc}1&0\end{array}\right)\left(\begin{array}{cc}0&1\\-1&0\end{array}\right)\left(\begin{array}{c}0\\1\end{array}\right)<br /> =<br /> \left(\begin{array}{cc}1&0\end{array}\right)\left(\begin{array}{c}1\\0\end{array}\right)<br /> \textrm{ or }<br /> \left(\begin{array}{cc}0&1\end{array}\right)\left(\begin{array}{c}0\\1\end{array}\right)<br /> =<br /> 1[/tex]
This sort of inner product actually occurs in, for example, the Majoranna Lagrangian. Actually, I'm not sure if this satisfies your definition of an inner product, because (v,v) and (w,w) vanish in this case.
 
Last edited:
Turin.

I know it's been a while since you replied to my post, but I have a question about it. I think the first two by two matrix you used is a matrix representation of a particular bilinear form, but I don't think it satisfies the conditions for an inner product since it is not symmetric.
 
OK, yes, the official definition of inner product requires

[tex] \langle{}v,w\rangle=\langle{}w,v\rangle^*[/tex]

So, based on this requirement, neither one of the two suggestions that I gave is valid. However, if you restrict the field to R, then I believe that you can get away with

[tex] \langle{}v,w\rangle<br /> \equiv<br /> \left(\begin{array}{cc}v_1&v_2\end{array}\right)<br /> \left(\begin{array}{cc}1&a\\a&1\end{array}\right)<br /> \left(\begin{array}{c}w_1\\w_2\end{array}\right)[/tex]

if

[tex] -1<a<1[/tex]

This is obviously symmetric, and the condition on a guaruntees that no vector with nonzero real-valued components can have a vanishing inner product. Then

[tex] \left(\begin{array}{cc}1&0\end{array}\right)<br /> \left(\begin{array}{cc}1&a\\a&1\end{array}\right)<br /> \left(\begin{array}{c}0\\1\end{array}\right)<br /> =<br /> a[/tex]
 
That works! It took me some time to see why the condition -1<a<1 guarantees that the inner product of a vector with itself vanishes if and only if the vector is the zero vector, but I get it now. Thanks a lot.

Sam
 

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