Difference between the outer product

In summary, the outer product |v><u| and the tensor product |v>⊗|u> are different in that the former is a linear operator on the Hilbert space, while the latter is an element of the tensor product of the vector space with itself. The tensor product of a vector space and its dual is the space of linear operators, while the Kronecker product is a representation of the tensor product used for dealing with tensor products of linear operators.
  • #1
Dragonfall
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Given [tex]\left| v\right>[/tex] and [tex]\left| u\right>[/tex] what is the difference between the outer product [tex]\left| v\right>\left< u\right|[/tex] and the tensor product [tex]\left| v\right>\otimes\left|u\right>[/tex]? Is the latter a matrix representation of the former in some basis? Which basis would that be?
 
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  • #2
And what's the difference between a tensor product and a kronecker product?
 
  • #3
I meant the tensor product [tex]\left< v\right|\otimes\left| u\right>[/tex].
 
  • #4
|u><v| is a way of writing the tensor product of a vector and a dual vector (ie, an element of the Hilbert space and an element of its dual, which is usually casually identified with the Hilbert space using the inner product). This is a linear operator on the Hilbert space, sending |w> to <v|w>|u>. In general, the tensor product of a vector space and its dual is the space of (finite rank) linear operators on the vector space.

On the other hand, |u>|v> is an element of the tensor product of the vector space with itself, usually used in physics for describing a composite of two identical systems. Again, since there is an isomorphism between the vector space and its dual, there is one between the space of composite states and the space of linear operators. This is interesting, but I've never seen this put to good use.

Finally, the Kronecker product is just a particular representation of the tensor product, convenient for dealing with tensor products of linear operators.
 

1. What is the outer product?

The outer product is a mathematical operation between two vectors that results in a matrix. It is also known as the tensor product or direct product.

2. How is the outer product different from the inner product?

The outer product results in a matrix, while the inner product results in a scalar value. The outer product also follows the rules of matrix multiplication, while the inner product follows the rules of vector dot product.

3. What are some applications of the outer product?

The outer product is commonly used in linear algebra, signal processing, and machine learning. It can also be used to find the composition of two linear transformations and to calculate the covariance matrix in statistics.

4. Can the outer product be performed on vectors of different dimensions?

Yes, the outer product can be performed on vectors of different dimensions. The resulting matrix will have the number of rows equal to the dimension of the first vector and the number of columns equal to the dimension of the second vector.

5. How is the outer product calculated?

The outer product is calculated by multiplying each element of one vector by each element of the other vector and arranging the results in a matrix. For example, if vector A has m elements and vector B has n elements, the resulting matrix will have dimensions m x n.

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