Tensor Product of V1 and V2 in Vector Space V: 0 Intersection Required?

In summary, for the tensor product to be defined between two subspaces V1 and V2 of a vector space V, their intersection does not have to be 0. An important example of tensor product is between a vector space and itself. Another possible definition of tensor product is that it maps two vector spaces onto ordered pairs. Existence of the tensor product is not a problem as it is more of a construction than an operation and it always endows the product with the required properties. The tensor product is bilinear and a basis can be obtained from the bases of the spaces used to construct it. Furthermore, the tensor product contains all elements of the form (u,v) and all linear combinations of such terms, with a dimension of mn
  • #1
Werg22
1,431
1
If V1 and V2 are both subspaces of a vector space V, then in order for their tensor product to be defined, does the intersection of V1 and V2 have to be 0?
 
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  • #2
No, infact an important example of tensor product is that between a vector space and itself.
V^2=VxV

One possible definition of tensor product is that it maps two vector spaces onto ordered pairs.
UxV contains ordered pairs (u,v) with u from U and v from V.
Edited to add. UxV also contains linear combinations of ordered pairs.
 
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  • #3
I see. But I have to say, I'm puzzled about this: according to one definition I have, the operation on pairs (u,v), u belonging to U and v belonging to V, that is used to construct the tensor product has to be bilinear. But that certainly isn't a sufficient condition, is it? One must also have that the operation in question applied to the whole of the cartesian product of a basis of U and a basis of V forms a basis for the tensor product, right? Does the tensor product always exist (i.e. is there always such a bilinear operation and vector space for any ordered pair of vector spaces)?
 
  • #4
The tensor product always exists.
Existence is not a problem because tensor product is more a construction than an operation. That is the operation always endows the product with the required properties.
lets assume U and V are vector spaces over the same field F.
we want
(a1u1+a2v2,b1v1+b2v2)=a1b1(u1,v1)+a1b2(u1,v1)+a2b1(u2,v1)+a2b2(u2,v2)
so in declaring the tensor product has this property we determin the tensor product uniquely.
Since the tensor product is bilinear we can obtain a basis from the bases of the spaces used to construct it.
if
{u(i)} is a basis for U
and
{v(j)} is a basis for V
{u(i),v(j)} is a basis for UxV

it is important to notice that
X is an element of UxV does not mean X is of the form (u,v)
UxV contains all elements of the form (u,v), but also all linear combinations of such terms.
By a counting argument if
dim(U)=m
dim(V)=n
dim(UxV)=mn
dim(elements of the form (u,v))=m+n
I see I was unclear above I said
UxV contains ordered pairs (u,v) with u from U and v from V
which is true but it contains more ie sums of such.
 

1. What is the definition of tensor product in vector spaces?

The tensor product of two vector spaces V1 and V2 is a new vector space V which is constructed from the two original vector spaces through a specific mathematical operation. This operation takes in two vectors, one from each space, and returns a new vector that combines the properties of both input vectors.

2. Why is a 0 intersection required in the tensor product of V1 and V2?

The 0 intersection requirement ensures that the resulting tensor product space is a direct sum of V1 and V2, meaning that any vector in the product space can be uniquely expressed as a combination of vectors from V1 and V2. This is important for preserving the linear independence and basis of the original vector spaces.

3. How is the tensor product different from the direct sum of V1 and V2?

The direct sum of V1 and V2 is a vector space that contains all possible combinations of vectors from V1 and V2. In contrast, the tensor product of V1 and V2 is a specific operation that takes in two vectors and returns a new vector that combines the properties of the input vectors. The tensor product is a more specific and structured operation compared to the direct sum.

4. What is the role of the tensor product in linear algebra?

The tensor product is a fundamental concept in linear algebra and is used to construct new vector spaces from existing ones. It is also important in defining multilinear maps, which are functions that take in multiple vectors and return a scalar value. The tensor product is also used in various applications, such as in quantum mechanics and differential geometry.

5. How is the tensor product of V1 and V2 related to the Kronecker product?

The Kronecker product is a specific type of tensor product that is used when the two input vector spaces are matrices. In this case, the Kronecker product of two matrices A and B would result in a new matrix that combines the properties of both A and B. Therefore, the Kronecker product is just a special case of the more general tensor product operation.

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