Method to solve a coupled system of matrix equation

In summary, the conversation discusses a system of matrix equations of the form AX + CY = cX and BY + DX = cY, where A, B, C, and D are square matrices and X, Y, and c are unknown variables to be solved for. The topic is focused on finding an efficient method or algorithm to solve this system using Fortran. A reference is suggested, but the conversation also delves into the physical interpretation of the equations and how they relate to spinors. A solution is proposed by rearranging the equations and lining up the matrices in a specific way.
  • #1
duc
9
0
Hello everyone,

I'm struggling with a coupled of matrix equations of the general form:

AX + CY = cX
BY + DX = cY

where A, B, C and D are hermitics square matrices. X, Y and c are the eigenvector and eigenvalue to be found. I'm looking for a method or an algorithm to solve this system by using Fortran. Could you suggest a reference or paper which treats this kind of equation ?

Thanks a lot!
duc
 
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  • #2
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  • #3
DEvens said:
Hello duc. Welcome to the forum.

Google is your friend.
https://www.google.ca/search?site=&source=hp&q=gaussian+elimination

https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix
https://en.wikipedia.org/wiki/Gaussian_elimination

If you need something more specific you could check out one of the _Numerical Recipes_ books. I think this is the most recent edition.

http://www.amazon.com/dp/0521880688/?tag=pfamazon01-20

Hi DEvens,

Thanks for your reply.
Maybe i should be more specific. The system of matrix equations I've mentioned is not linear system of equations that can be solved by using matrix technique. A, B, C and D here are not number but square matrices of dimension N x N (where N is integer and typically of order 1000). And X, Y and c are respectively the column vectors of N elements and the unknown value which have to be found.

Physically, X and Y are two components of a spinor associated to a spin-1/2 particle. c is the energy associated with this spinor. When studying a physical system whose Hamiltonian contains an interaction which involves spin operators in an intricate manner, I've been led to the above kind of equations.

In other word, instead of the usual eigenvalue problem: A X = e X (A: matrix, X - a vector of given dimension, e - eigenvalue), the system of matrix equations above describes the eigenvalue problem for a spinor X of 2-components and A will become a matrix of 2x2 elements (and each one is an operator) in spin space. When using a discrete truncated basis in position space for example, each "element" of A will be represented by a matrix.

Hope that would make my question clearer. I'll consult the reference you've cited any way.

duc
 
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  • #4
Oh, it's a different part of it you are having difficulty with. I sort of assumed this part was trivial.

AX + CY = cX
BY + DX = cY

Presuming X and Y are column vectors. Rearrange that as Z = {X,Y}. I'm too lazy to get into the TeX-like things here to make that line up properly. But that is a column vector with the first "n" components being the components of X, and the second "n" being the components of Y.

Then line up your matrices like so:

A C
D B

That is make a 2n x 2n matrix out of the four matrices A, C, D, and B. Call this new matrix M.

So you then have M Z = c Z. And you round up the usual suspects to solve the matrix equation.
 
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Likes duc
  • #5
DEvens said:
Oh, it's a different part of it you are having difficulty with. I sort of assumed this part was trivial.

AX + CY = cX
BY + DX = cY

Presuming X and Y are column vectors. Rearrange that as Z = {X,Y}. I'm too lazy to get into the TeX-like things here to make that line up properly. But that is a column vector with the first "n" components being the components of X, and the second "n" being the components of Y.

Then line up your matrices like so:

A C
D B

That is make a 2n x 2n matrix out of the four matrices A, C, D, and B. Call this new matrix M.

So you then have M Z = c Z. And you round up the usual suspects to solve the matrix equation.

Thank you very much DEvens. I've thought of it but had some doubts and didn't go further :D. It is indeed "trivial" as you said :">. Thanks again. :)
 

1. How do you solve a coupled system of matrix equations?

To solve a coupled system of matrix equations, you can use methods such as the Gauss-Jordan elimination, Gaussian elimination, or the LU decomposition method. These methods involve manipulating the equations and matrices to reduce the system to a simpler form, which can then be solved using basic algebraic techniques.

2. What is the purpose of solving a coupled system of matrix equations?

The purpose of solving a coupled system of matrix equations is to find the values of the variables that satisfy all equations in the system simultaneously. This can help in solving complex systems of equations that involve multiple variables and can have various applications in fields such as engineering, physics, and economics.

3. What are some common challenges in solving a coupled system of matrix equations?

Some common challenges in solving a coupled system of matrix equations include dealing with large matrices, encountering situations where the system has no solution or infinitely many solutions, and making errors during the manipulation of equations and matrices. It is also important to check for consistency and compatibility of the equations in the system before attempting to solve them.

4. Can a coupled system of matrix equations be solved using software?

Yes, there are various software programs and packages that can solve coupled systems of matrix equations. These include MATLAB, Mathematica, and Python libraries such as NumPy and SciPy. These programs use efficient algorithms and can handle large systems of equations with ease, making the solving process faster and more accurate.

5. What are some real-life applications of solving coupled systems of matrix equations?

Coupled systems of matrix equations have various applications in fields such as engineering, physics, economics, and computer science. Some examples include solving electrical circuits, analyzing chemical reactions, predicting economic trends, and solving systems of linear equations in machine learning algorithms.

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