Can Any Complex Matrix Be Decomposed into Hermitian and Skew-Hermitian Parts?

In summary, the first matrix, $(C+C^{\dagger})/2$, has the property that its determinant is always negative, while the second matrix, $(C-C^{\dagger})/2$, has the property that its determinant is always positive.
  • #1
ognik
643
2
If C NOT Hermitian, show we can decompose C into $\frac{1}{2}\left( C + {C}^{\dagger} \right) +\frac{1}{2i}i\left( C- {C}^{\dagger} \right) $

I've managed to prove C = C a couple of times, EG taking Hermitian or conjugate of both sides, probably there is a bit of info I am not thinking of or missing altogether, a hint please?
 
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  • #2
ognik said:
If C NOT Hermitian, show we can decompose C into $\frac{1}{2}\left( C + {C}^{\dagger} \right) +\frac{1}{2i}i\left( C- {C}^{\dagger} \right) $

I've managed to prove C = C a couple of times, EG taking Hermitian or conjugate of both sides, probably there is a bit of info I am not thinking of or missing altogether, a hint please?

I'm guessing there's a bit more to this problem. That the RHS equals the LHS you've probably already shown, as indicated by your statement that you've proved "C = C a couple of times". I'm guessing the problem wants you to investigate the first matrix, $(C+C^{\dagger})/2$, and the second matrix, $(C-C^{\dagger})/2$, a bit more closely. What are their properties?
 
  • #3
I had, but on second looks, it seems ridiculously easy...

Let $H(C)=\frac{1}{2}(C+C^{\dagger}) $, then $H^{\dagger}=\frac{1}{2}(C^{\dagger}+C) = H$

Similarly $S(C)^{\dagger}= -S$

I remember similar expressions for symmetric Matrices (same proof?) isn't there a similar formula for complex numbers?

...and why do they show the formula for C with $\frac{1}{2i}i$?
 
  • #4
Exactly - you've got it. So, the moral of the story is that you can write any (square) matrix as the sum of an Hermitian matrix, and a skew-Hermitian matrix. These two kinds of matrices have special properties that are nice to work with in certain circumstances.

I have no idea why they didn't just write
$$C=\frac12\left(C+C^{\dagger}\right)+\frac12\left(C-C^{\dagger}\right).$$
Probably to confuse the newbie. ;)
 
  • #5
This book excels at that :confused: suppose it gives the neurons more exercise. (Can't imagine why the Uni didn't use the latest edition...)

The other formula I was thinking of is $ z = \frac{1}{2}(z+z^*) + \frac{1}{2i}(z-z^*) $, real + imag component, even easier to prove
 
  • #6
This is my theory (as to the form of the decomposition):

$(C - C^{\dagger})^{\dagger} = -(C - C^{\dagger})$ is skew-Hermitian, whereas:

$(i(C - C^{\dagger}))^{\dagger} = (-i)(-(C - C^{\dagger})) = i(C - C^{\dagger})$ is Hermitian.
 

Related to Can Any Complex Matrix Be Decomposed into Hermitian and Skew-Hermitian Parts?

1. What is hermitian decomposition?

Hermitian decomposition is a mathematical method used to decompose a Hermitian matrix into a product of a unitary matrix and a diagonal matrix. It is also known as spectral decomposition or eigendecomposition.

2. How is hermitian decomposition used in quantum mechanics?

In quantum mechanics, hermitian decomposition is used to represent a quantum state as a linear combination of eigenstates. This allows for easier calculation of probabilities and measurements of quantum systems.

3. What is the significance of unitary and diagonal matrices in hermitian decomposition?

Unitary matrices represent rotations and reflections, while diagonal matrices represent scaling. Together, they can be used to represent any linear transformation, making hermitian decomposition a powerful tool in linear algebra and quantum mechanics.

4. How is hermitian decomposition related to the spectral theorem?

Hermitian decomposition is a special case of the spectral theorem, which states that any Hermitian matrix can be diagonalized by a unitary matrix. This allows for easier analysis and manipulation of Hermitian matrices in various mathematical contexts.

5. Can hermitian decomposition be applied to non-Hermitian matrices?

No, hermitian decomposition is only applicable to Hermitian matrices, which are square matrices that are equal to their own complex conjugate transpose. For non-Hermitian matrices, other methods such as singular value decomposition must be used for decomposition.

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