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

In summary: The next step is$$\langle v,w\rangle = \frac12 \left[\langle A(v+w),A(v+w)\rangle-\langle v+w,v+w\rangle \right]$$If you expand ##\langle A(v+w),A(v+w)\rangle## you get four terms, each of which is of the form ##\langle a,a\rangle##, which is what you want.In summary, Theorem 4.4.4 in "Semi-Riemannian Geometry: The Mathematical Language of General Relativity" by Stephen Newman states that if a linear map A:V→V is a linear isometry, then the norm of Av is equal to the norm of v for all
  • #1
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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  • #2
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.
 
  • #3
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
 
  • #4
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##.
 
  • #5
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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1. What is a norm-preserving linear map?

A norm-preserving linear map is a mathematical function that preserves the length or magnitude of a vector. This means that the norm or length of the input vector is equal to the norm or length of the output vector after the map is applied.

2. What are the conditions for a linear map to be an isometry?

In addition to being norm-preserving, a linear map must also satisfy the condition of being distance-preserving to be considered an isometry. This means that the distance between any two points in the input vector space must be equal to the distance between the corresponding points in the output vector space.

3. How is a linear map related to a linear isometry?

A linear map is considered a linear isometry if it satisfies the conditions of being both norm-preserving and distance-preserving. This means that the map not only preserves the magnitude of vectors, but also the distances between points in the vector space.

4. What are some examples of linear isometries?

Some examples of linear isometries include rotations, reflections, and translations in a Euclidean space. In general, any transformation that preserves distances and angles can be considered a linear isometry.

5. What are the applications of linear isometries in science?

Linear isometries have various applications in science, particularly in fields such as physics, engineering, and computer graphics. They are used to model and analyze physical systems, perform geometric transformations, and create realistic visual effects in computer-generated images.

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