Matrix Decomposition: Solving for B and D in A = B*inv(B+D)?

In summary, this equation does not seem to have a unique solution, and it may not be possible to solve it efficiently.
  • #1
TimSal
5
0
Hi everyone,

I have a problem with the following matrix equation:

A = B*inv(B+D)

where A is a square matrix, B a positive semi-definite matrix, D a positive diagonal matrix and inv() denotes the inverse matrix. All are real-valued.

Does anyone know of any simple way to check whether this equation has a solution for given A? And how to obtain this solution? (i.e. find B and D) And whether the solution is unique?

Thanks in advance!
 
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  • #2
Welcome to PF!

Hi TimSal! Welcome to PF! :smile:

Hint: multiply both sides by … ? :wink:
 
  • #3
Yes, that gives

A(B+D)=B

or

AD=(I-A)B

I don't see how that really helps to answer the question though. It's a system of linear equations but I still don't see an easy way of checking whether there exists a solution, nor do I know how to solve this efficiently under the restriction that B is positive semi-definite and D is positive diagonal.
 
  • #4
Hi TimSal! :smile:
TimSal said:
AD=(I-A)B

So, if (I - A) is invertible, then B = (1 - A)-1AD :wink:
 
  • #5
Thanks. Any thoughts on the case where A and (I-A) are not invertible?
 
  • #6
Also, with
tiny-tim said:
B = (1 - A)-1AD
there does not seem to be any guarantee that B will indeed be positive semi-definite for any given positive diagonal D.
 
  • #7
TimSal said:
Thanks. Any thoughts on the case where A and (I-A) are not invertible?

Nope! :smile:
TimSal said:
Also, with

there does not seem to be any guarantee that B will indeed be positive semi-definite for any given positive diagonal D.

But D isn't given.
 
  • #8
D isn't given, but because there is no guarantee that B will be positive semi-definite for any chosen D, this expression does not help me solve the equation. I still don't know how to pick D and B.
 

1. What is matrix decomposition?

Matrix decomposition, also known as matrix factorization, is the process of breaking down a matrix into simpler, more easily understandable matrices. This allows for more efficient computation and can reveal hidden patterns in the data.

2. Why is matrix decomposition important?

Matrix decomposition is important because it allows for more efficient computation and can help simplify complex matrices. It is also a valuable tool in data analysis and can reveal hidden patterns and relationships in the data.

3. What are the different types of matrix decomposition?

There are several types of matrix decomposition, including LU decomposition, QR decomposition, Singular Value Decomposition (SVD), and Eigenvalue Decomposition. Each type has its own specific purpose and can be used for different applications.

4. How is matrix decomposition used in data analysis?

Matrix decomposition is commonly used in data analysis to simplify complex datasets and reveal hidden patterns and relationships. It can also be used for data compression, collaborative filtering, and other machine learning algorithms.

5. Are there any limitations to matrix decomposition?

While matrix decomposition can be a useful tool, it does have its limitations. It may not always be possible to decompose a matrix, and even when it is possible, the resulting matrices may not accurately represent the original data. Additionally, some types of decomposition may result in loss of information.

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