A = norm-preserving linear map (+other conditions) => A = lin isometry

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SUMMARY

The discussion centers on Theorem 4.4.4 from "Semi-Riemannian Geometry: The Mathematical Language of General Relativity" by Stephen Newman, which establishes that a linear map \( A: V \to V \) is a linear isometry if it preserves norms and maintains the classification of vectors (spacelike, timelike, lightlike). The proof involves showing that norm preservation leads to equality in inner products, specifically \( \langle A(v), A(w) \rangle = \langle v, w \rangle \). Participants confirm that the theorem holds under broader conditions, indicating that the proof is not restricted to the same vector space for both the domain and codomain.

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Shirish
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I'm studying "Semi-Riemannian Geometry: The Mathematical language of General Relativity" by Stephen Newman. Theorem 4.4.4 in that book:

Let ##(V,g)## be a scalar product space, and let ##A:V\to V## be a linear map. Then:
  1. If ##A## is a linear isometry, then ##\|A(v)\|=\|v\|\ \forall\ v\in V##
  2. If ##\|A(v)\|=\|v\|\ \forall\ v\in V##, and if ##A## maps spacelike (resp. timelike and lightlike) vectors to spacelike (resp. timelike and lightlike) , then ##A## is a linear isometry.

The proof of part 2 is given like this:

Since ##\|A(v)\|=\|v\|## is equivalent to ##|\langle A(v),A(v)\rangle|=|\langle v,v\rangle|##, the assumption regarding the way ##A## maps vectors yields ##\langle A(v),A(v)\rangle=\langle v,v\rangle##.

Seems a bit incomplete. I'd like to know if my approach is correct:

$$\langle A(v+tw),A(v+tw)\rangle=\langle A(v)+tA(w),A(v)+tA(w)\rangle$$
$$=\langle A(v),A(v)\rangle+t^2\langle A(w),A(w)\rangle+2t\langle A(v),A(w)\rangle=\langle v,v\rangle+t^2\langle w,w\rangle+2t\langle A(v),A(w)\rangle$$

But $$\langle A(v+tw),A(v+tw)\rangle=\langle v+tw,v+tw\rangle=\langle v,v\rangle+t^2\langle w,w\rangle+2t\langle v,w\rangle$$

(I'm not even sure if the ##t## coefficient matters) The above shows that ##\langle v,w\rangle=\langle A(v),A(w)\rangle##.

Is this fine? Secondly, wouldn't this same theorem more generally hold for a linear map ##A:V\to W##, where ##V,W## are both scalar product spaces (regardless of dimensions of ##V## and ##W##)?
 
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The book proof is just a sketch. The point is that without the second condition you could have$$\langle Av, Aw\rangle = -\langle v,w\rangle$$Your proof needs to take this into account.
 
PeroK said:
The book proof is just a sketch. The point is that without the second condition you could have$$\langle Av, Aw\rangle = -\langle v,w\rangle$$Your proof needs to take this into account.
Yep that I understood already (I mean about why the additional conditions are needed apart from norm preservation), but I was still wondering if the whole theorem holds in more general conditions
 
Shirish said:
Yep that I understood already (I mean about why the additional conditions are needed apart from norm preservation), but I was still wondering if the whole theorem holds in more general conditions
Yes, it must. The proof is not dependent on the image space being ##V##.
 
From
##\langle v+w,v+w\rangle=\langle v,v\rangle+\langle w,w\rangle+2\langle v,w\rangle##

you have

##\langle v,w\rangle = \frac12 \left[\langle v+w,v+w\rangle-\langle v,v\rangle-\langle w,w\rangle \right]##

Thus the inner product of any pair of vectors is determined by inner products of the form ##\langle a,a\rangle##. That is why they didn't write any more.
 
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