A question about tensor product

In summary: For example, a linear map from R^3 to R^2 can be represented by a 2 by 3 matrix, but it is not a tensor.Well, that's not entirely true. A matrix can represent a tensor, but it is not the only way to represent a tensor. Tensors are more general than matrices and can be represented in many different ways, such as a multi-dimensional array, a set of linear transformations, or a differential operator. So while a matrix can represent a tensor, a tensor is not necessarily a matrix.
  • #1
Harel
6
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Hey it might be a stupid question but I saw that the tensor product of 2 vectors with dim m and n gives another vector with dimension mn and in another context I saw that the tensor product of vector gives a metrix. For example from sean carroll's book: "If T is a (k,l) tensor and S is a (m, n) tensor, we define a (k + m, l + n) tensor T ⊗ S"
so the tensor product of two type 1 tensors,k=1,vectors, is a metrix
and in the context of quantum mechanic I saw
(1,0)⊗(1,0)↦(1,0,0,0) when those our basis vectors.
I'm sure I'm just getting something wrong but I am hopefull that you can explain me what.
 
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  • #2
Harel said:
so the tensor product of two type 1 tensors,k=1,vectors, is a metrix
Why it would be? If one of them is dual vector, then it might be. I will give you some examples:
[tex] \begin{pmatrix} 1 \\ 2 \end{pmatrix} \otimes \begin{pmatrix} 1 & 2 \end{pmatrix} = \begin{pmatrix} 1 \times \begin{pmatrix} 1 & 2 \end{pmatrix}\\ 2 \times \begin{pmatrix} 1 & 2 \end{pmatrix} \end{pmatrix} = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix}, [/tex]
[tex] \begin{pmatrix} 1 & 2 \end{pmatrix} \otimes \begin{pmatrix} 1 & 2 \end{pmatrix} = \begin{pmatrix} 1 \times \begin{pmatrix} 1 & 2 \end{pmatrix} & 2 \times \begin{pmatrix} 1 & 2 \end{pmatrix} \end{pmatrix} = \begin{pmatrix} 1 & 2 & 2 & 4 \end{pmatrix} [/tex]
 
  • #3
Daeho Ro said:
Why it would be? If one of them is dual vector, then it might be. I will give you some examples:
[tex] \begin{pmatrix} 1 \\ 2 \end{pmatrix} \otimes \begin{pmatrix} 1 & 2 \end{pmatrix} = \begin{pmatrix} 1 \times \begin{pmatrix} 1 & 2 \end{pmatrix}\\ 2 \times \begin{pmatrix} 1 & 2 \end{pmatrix} \end{pmatrix} = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix}, [/tex]
[tex] \begin{pmatrix} 1 & 2 \end{pmatrix} \otimes \begin{pmatrix} 1 & 2 \end{pmatrix} = \begin{pmatrix} 1 \times \begin{pmatrix} 1 & 2 \end{pmatrix} & 2 \times \begin{pmatrix} 1 & 2 \end{pmatrix} \end{pmatrix} = \begin{pmatrix} 1 & 2 & 2 & 4 \end{pmatrix} [/tex]
Because by the defenition of sean, let's take a type (1,0) tensor which is a vector and another (1,0) tensor which is also a vector and the product will be a (2,0) tensor, which is a metrix.
 
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  • #4
Harel said:
Hey it might be a stupid question but I saw that the tensor product of 2 vectors with dim m and n gives another vector with dimension mn and in another context I saw that the tensor product of vector gives a metrix. For example from sean carroll's book: "If T is a (k,l) tensor and S is a (m, n) tensor, we define a (k + m, l + n) tensor T ⊗ S"
so the tensor product of two type 1 tensors,k=1,vectors, is a metrix
and in the context of quantum mechanic I saw
(1,0)⊗(1,0)↦(1,0,0,0) when those our basis vectors.
I'm sure I'm just getting something wrong but I am hopefull that you can explain me what.

A vector can be seen as a ## 1 \times n ## matrix.
 
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  • #5
Harel said:
Because by the defenition of sean, let's take a type (1,0) tensor which is a vector and another (1,0) tensor which is also a vector and the product will be a (2,0) tensor.
That is true. But, the results of mine are matrices with the size [itex] 2 \times 2 [/itex] and [itex] 1 \times 4 [/itex].
 
  • #6
Harel said:
Hey it might be a stupid question but I saw that the tensor product of 2 vectors with dim m and n gives another vector with dimension mn and in another context I saw that the tensor product of vector gives a metrix. For example from sean carroll's book: "If T is a (k,l) tensor and S is a (m, n) tensor, we define a (k + m, l + n) tensor T ⊗ S"
so the tensor product of two type 1 tensors,k=1,vectors, is a metrix
<Snip>.

Actually, if your map is k-linear ( in any " coordinate") for k>2 (where you may have quadratic forms), it is not representable as a matrix anymore. That is the actual point of tensors: to represent k - , or j- ( k,j pos. integers) linear maps in many variables, which is not feasible with matrices alone whenever you have an index >2.
Only linear and bilinear maps may be represented using matrices.
 
  • #7
I feel compelled to point out that a tensor can be represented by a matrix in a given coordinate system, but, strictly speaking, a tensor is NOT a matrix.
 
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  • #8
HallsofIvy said:
I feel compelled to point out that a tensor can be represented by a matrix in a given coordinate system, but, strictly speaking, a tensor is NOT a matrix.
How do you represent a higher-order tensor as a matrix? e.g., a 3-linear map .
 
  • #9
In the same sense that a "vector" is a 1 by 3 matrix, so a higher order tensor can be represented by a "3 by 3 by 3" or higher matrix. I admit that is stretching the concept of "matrix" a bit far. My point was simply that a matrix is NOT a tensor.
 

Related to A question about tensor product

1. What is a tensor product?

A tensor product is a mathematical operation that combines two vector spaces to create a new vector space. It is used in linear algebra and is denoted by the symbol ⊗ (a circle with a cross inside).

2. How is tensor product different from vector multiplication?

Tensor product is a more general operation than vector multiplication. While vector multiplication is only defined for two vectors, the tensor product can be applied to any two vector spaces, including matrices and higher dimensional objects.

3. What are some applications of tensor product in science?

Tensor product has numerous applications in various fields of science, including physics, computer science, and engineering. It is used in quantum mechanics, signal processing, and image recognition, among others.

4. What are the properties of tensor product?

Some of the properties of tensor product include distributivity, associativity, and commutativity. It also follows the rules of bilinearity and linearity, which are important in linear algebra.

5. Are there any limitations to using tensor product?

One limitation of tensor product is that it can become computationally expensive when dealing with large or high-dimensional objects. Additionally, it may not always be applicable in certain mathematical operations, such as division.

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