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

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The discussion centers on Theorem 4.4.4 from "Semi-Riemannian Geometry" by Stephen Newman, which states that a linear map A is a linear isometry if it preserves norms and maintains the classification of vectors (spacelike, timelike, lightlike). The user questions the completeness of the proof, particularly in demonstrating that the inner product structure is preserved under the conditions given. They explore the implications of their proof approach, showing that the inner product relationship holds, but seek clarity on whether the theorem can be generalized to maps between different scalar product spaces. The consensus is that the theorem does extend beyond the original conditions, as the inner product relationships can be derived from norm preservation alone. The discussion emphasizes the importance of additional conditions to avoid misleading results in the context of linear maps.
Shirish
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I'm studying "Semi-Riemannian Geometry: The Mathematical Langauge 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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