How can the existence of the tensor product be proven in Federer's construction?

In summary, the conversation discusses the construction of the Tensor Product in Herbert Federer's book on Geometric Measure Theory. It is mentioned that the existence of the linear map "g" in this construction is not clear. The process of proving the existence of "g" is explained, including the use of a basis for the vector space F and extending the map linearly. The conversation ends with a thank you for the help in understanding this concept.
  • #1
turiya
5
0
Hi all,

I was reading the book by Herbert Federer on Geometric Measure Theory and
it seems he proves the existence of the Tensor Product quite differently
from the rest. However it is not clear to me how to prove the existence of the linear map "g" in his construction.

He defines F as the vectorspace consisting of all real valued functions on
V1 x V2 x V3 x V4 ... x Vn which vanish outside some (varying) finite set.
(Here, V1, V2, V3, V4, ... Vn are all vector spaces). Now consider the map
\phi: V1 x V2 x V3 x V4 ... Vn -> F where \phi(v1,v2,v3,...,vn) is the function
with value 1 at (v1,v2,...,vn) and zero elsewhere.

If we let G to be the vector space generated by all elements of two types:
1) \phi(v1,..,vi-1,x,vi+1,..,vn) + \phi(v1,..,vi-1,y,vi+1,..,vn) - \phi(v1,..,vi-1,x+y,vi+1,..,vn)
2) \phi(v1,..,vi-1,cvi,vi+1,..,vn) - c\phi(v1,..,vi-1,vi,vi+1,..,vn) (c is a real number)
then G a sub-space of F.

Now F/G is considered to be the the Tensor product of V1, V2, .. Vn and \mu = r \dot \phi is the n-linear map associated with the Tensor product, where "r" is the quotient map from F to F\G. Ofcourse, the theorem says that for every n-linear map f : V1xV2x..xVn -> W there exists a unique linear map g : F/G -> W such that f = g \dot \mu for any vector space W.

I have tried and searched a lot to prove the existence of such a "g" but to no avail. Most other texts use a different definition of Tensor product and so any help in this is greatly appreciated.

Thanks in advance
Phanindra
 
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  • #2
The point is that [tex]\{\varphi(v_1,...,v_n)~\vert~v_i\in V_i\}[/tex] is a basis for F.
So, given a map [tex]f:V_1\times...\times V_n\rightarrow W[/tex], we can make a map [tex]g:F\rightarrow W[/tex]. It is enough to put [tex]g(\varphi(v_1,...,v_n))=f(v_1,...,v_n)[/tex] and extend this linearly. So, this gives a map [tex]g:F\rightarrow W[/tex].

Now, it suffices to show that [tex]g(G)=\{0\}[/tex]. Since in that case, g will factor as [tex]h:F/G\rightarrow W[/tex] by [tex]h([v])=g(v)[/tex]...
 
  • #3
Thanks a lot micromass. Your steps indeed prove that "h" is the unique linear map I am looking for.
 

FAQ: How can the existence of the tensor product be proven in Federer's construction?

1. What is a tensor product?

A tensor product is a mathematical operation that combines two vectors or matrices to create a new vector or matrix. It is often used in physics and engineering to represent physical quantities that have both magnitude and direction.

2. What is the significance of tensor product in science?

The tensor product is significant in science because it allows us to describe complex physical systems using simpler mathematical operations. It is also a fundamental concept in fields such as quantum mechanics and relativity.

3. How is tensor product different from regular multiplication?

Unlike regular multiplication, the tensor product does not simply multiply the corresponding elements of two vectors or matrices. Instead, it combines them in a way that preserves the underlying structure of the vectors or matrices.

4. Can you give an example of tensor product in real life?

One example of tensor product in real life is the moment of inertia tensor, which is used to describe the distribution of mass in a rigid body. It is calculated by taking the tensor product of the position vector and the mass distribution vector.

5. How is the tensor product related to other mathematical operations?

The tensor product is closely related to other mathematical operations such as the dot product and cross product. It can also be seen as a generalization of these operations to higher dimensional spaces.

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