Kronecker product decomposition

In summary, the conversation discusses the Kronecker product between two matrices, A and B, and the uniqueness of B when A and C are given. The speaker mentions an algorithm that can approximately decompose C into an A' and B' pair, but notes that it lacks control over A and B. They ask for any ideas or suggestions on how to find predictions or guesses for B. They also mention that the elements of C are pairs of elements from A and B.
  • #1
umut_caglar
7
0
hi everybody

Today I have a question about Kronecker products, If you have a direct answer it is perfect but if not, any kind of paper reference might work as well.

now say I have to matrices A and B in general there is nothing special about them. They are not hermitian or triangular or what ever special.

then I calculate the Kronecker product between them [itex]A \otimes B = C[/itex]

then assume that A and C is given to us and we want to figure out what B is. I know that B is not unique but is there anything that we can say about B? Do you know any algorithm that can give some predictions or guesses for B.

I know one stuff that can approximately decompose C into a A', B' pair. But in this algorithm you do not have any control on A or B.

http://www.mit.edu/~wingated/scripts/krondecomp.m

any idea is welcome, thank you.
 
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  • #2
Given ##A## and ##C## makes ##B## unique. The elements of ##C## are pairs ##c_{ijkl}=a_{ij}b_{kl}##. Tensor products are not unique in the sense that ##\lambda A\oplus B = A \oplus \lambda B##. But as you nailed ##A##, there are no scalars ##\lambda \neq 1## which can be swapped. Also, as you have only a dyade, sums don't have any effect either, as there are simply none.
 

Related to Kronecker product decomposition

1. What is the Kronecker product decomposition?

The Kronecker product decomposition is a mathematical operation that involves multiplying two matrices to create a larger matrix. It is often used in linear algebra and statistics, and it is named after the German mathematician Leopold Kronecker.

2. How is the Kronecker product decomposition calculated?

The Kronecker product decomposition is calculated by multiplying each element of one matrix by every element in the other matrix. This results in a larger matrix with dimensions equal to the product of the dimensions of the two original matrices.

3. What are the applications of the Kronecker product decomposition?

The Kronecker product decomposition has many applications in mathematics and science. It is commonly used in statistics to model complex data sets, in image processing to create larger images from smaller ones, and in quantum mechanics to represent the dynamics of quantum systems.

4. What are the advantages of using Kronecker product decomposition?

The Kronecker product decomposition can simplify calculations involving large matrices, as it allows for the representation of complex systems in a more concise form. It also has applications in data compression and can help identify patterns in data sets.

5. Are there any limitations to using the Kronecker product decomposition?

While the Kronecker product decomposition is a useful tool in many applications, it can also lead to very large matrices, which can be computationally expensive to work with. Additionally, it may not always be the most efficient method for solving a problem, so it is important to consider alternative approaches as well.

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