What is the name of the matrix decomposition with specific properties?

In summary, the matrix ##B## can be decomposed into a rotation matrix (i.e. determinant equal to "positive" one and transpose is equal to "inverse"), two Hermitian matrices, and a matrix with polar coordinates.
  • #1
Ken Gallock
30
0
Hi everyone.
There is the ##2\times 2## matrix ##B##
$$B=
\left[
\begin{array}{cc}
B_{11} &B_{12} \\
B_{21}&B_{22}
\end{array}
\right],~B_{ij}\in \mathbb{C}
$$
with property
$$\vert B_{11}\vert^2 + \vert B_{12}\vert^2=1,$$
$$\vert B_{21}\vert^2 + \vert B_{22}\vert^2=1,$$
$$B_{11}B_{21}^{\ast}+B_{12}B_{22}^{\ast}=0.$$
According to one of the texts, it is said that this matrix can be decomposed like
$$B=e^{i\frac{\Lambda}{2}}
\left[
\begin{array}{cc}
e^{i\frac{\Phi}{2}} & 0 \\
0 & e^{-i\frac{\Phi}{2}}
\end{array}
\right]
\left[
\begin{array}{cc}
\cos (\Theta/2) & \sin (\Theta/2) \\
-\sin (\Theta/2) & \cos (\Theta/2)
\end{array}
\right]
\left[
\begin{array}{cc}
e^{i\frac{\Psi}{2}} & 0 \\
0 & e^{-i\frac{\Psi}{2}}
\end{array}
\right]
$$
$$\Lambda, \Phi, \Theta, \Psi \in \mathbb{R}$$.
I don't know what kind of decomposition this is.
Could someone tell me the name of this decomposition?

Thanks.
 
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  • #2
Hey Ken Gallock.

The middle matrix is a rotation matrix (i.e. determinant equal to "positive" one and transpose is equal to "inverse") and the other two look like they are Hermitian (but you would have to double check everything at least once yourself to make sure).

Look up matrix "decompositions" with rotations and Hermitian matrices and see if they fit the form for more information.
 
  • #4
chiro said:
Hey Ken Gallock.

The middle matrix is a rotation matrix (i.e. determinant equal to "positive" one and transpose is equal to "inverse") and the other two look like they are Hermitian (but you would have to double check everything at least once yourself to make sure).

Look up matrix "decompositions" with rotations and Hermitian matrices and see if they fit the form for more information.
fresh_42 said:
Thank you very much.
I wasn't paying attention. ##B## is unitary matrix...

Now, I have another question.
Is this something to do with polar decomposition? When I first saw this decomposition, the first thing came up my mind was polar decomposition because I heard polar decomposition is similar to decomposition of complex number, that is
$$z=\vert z \vert e^{i\theta}, (z\in \mathbb{C}).$$
 
  • #5
Whether you write a complex number ##z## as ##z=a+ib## or as ##z=|z|e^{i\theta} , (\theta \in [0,2\pi))## doesn't matter. But you are right, the decomposition looks as if it is easier to calculate with polar coordinates. Only the factor in the middle, I guess, will have to be transformed by using ##\sin \varphi = \frac{1}{2i}(e^{i\varphi}-e^{-i\varphi})## and ##\cos \varphi = \frac{1}{2}(e^{i\varphi}+e^{-i\varphi})## then.
 
  • #6
You might want to look at every operator from right to left (compositions with these operators do their "work" from right to left since of how things are evaluated) and see the geometric intuition from that.

For an interpretation of the outer linear operators I'd suggest a couple of things.

The first is to "un-pack" the matrices (i.e. convert a 2x2 matrix into a 4x4 one if possible and use all real numbers) and the second is to see what a multiplication of two complex numbers do another complex number.

Multiplication will always multiply the radii (the "r" term in the polar "decomposition") and add the angles so there is definitely a geometric intuition there (and the 4x4 un-packed definition should highlight the same dynamic).

This means that according to what is happening, you are doing a set of multiplications (scaled ones) and rotations on two complex variables.

That should give a bit more of the interpretation of what is going on.
 
  • #7
fresh_42 said:
Whether you write a complex number ##z## as ##z=a+ib## or as ##z=|z|e^{i\theta} , (\theta \in [0,2\pi))## doesn't matter. But you are right, the decomposition looks as if it is easier to calculate with polar coordinates. Only the factor in the middle, I guess, will have to be transformed by using ##\sin \varphi = \frac{1}{2i}(e^{i\varphi}-e^{-i\varphi})## and ##\cos \varphi = \frac{1}{2}(e^{i\varphi}+e^{-i\varphi})## then.
chiro said:
You might want to look at every operator from right to left (compositions with these operators do their "work" from right to left since of how things are evaluated) and see the geometric intuition from that.

For an interpretation of the outer linear operators I'd suggest a couple of things.

The first is to "un-pack" the matrices (i.e. convert a 2x2 matrix into a 4x4 one if possible and use all real numbers) and the second is to see what a multiplication of two complex numbers do another complex number.

Multiplication will always multiply the radii (the "r" term in the polar "decomposition") and add the angles so there is definitely a geometric intuition there (and the 4x4 un-packed definition should highlight the same dynamic).

This means that according to what is happening, you are doing a set of multiplications (scaled ones) and rotations on two complex variables.

That should give a bit more of the interpretation of what is going on.

Thank you very much. Sorry for this late reply.
Now I solved my problem.

By the way, this decomposition can also be applied to quasi-unitary matrices. (but with ##\cos \rightarrow \cosh, \pm \sin \rightarrow \sinh##)
 

FAQ: What is the name of the matrix decomposition with specific properties?

What is decomposition of a matrix?

Decomposition of a matrix is the process of breaking down a matrix into simpler, more manageable parts. It involves expressing a matrix as a product of two or more matrices, each with its own unique properties.

Why is decomposition of a matrix useful?

Decomposition of a matrix is useful because it can simplify complex matrix operations, making them easier to solve. It can also reveal important properties of a matrix, such as its rank and determinant.

What are the different types of matrix decomposition?

There are several types of matrix decomposition, including LU decomposition, QR decomposition, and singular value decomposition (SVD). Each type has its own purpose and can be used to solve different types of problems.

How is decomposition of a matrix performed?

Decomposition of a matrix is typically performed using algorithms and techniques such as Gaussian elimination, Gram-Schmidt process, and Householder transformation. These methods involve manipulating the original matrix to obtain the desired decomposition.

What are the applications of matrix decomposition?

Matrix decomposition has various applications in fields such as engineering, physics, and data analysis. It can be used to solve systems of linear equations, find eigenvalues and eigenvectors, and reduce the dimensionality of data. It is also commonly used in computer graphics and image processing.

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