Coninvolution: A Powerful Property of Nonsingular Matrices

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In summary, we have discussed a lemma involving coinvolutory matrices and ways to prove it. We have also discussed examples of coinvolutory matrices and how they can be represented using real matrices. Further literature on coinvolutory matrices is not readily available.
  • #1
BrainHurts
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It's very difficult for me to find any simple literature to explain this idea.

J[itex]\in[/itex]Mn(ℂ) is a coninvolutory (or a "coninvolution") if A-1=[itex]\overline{A}[/itex]
I'm looking to prove this lemma:

Let A be an element of Mn(ℂ) and A is nonsingular, then [itex]\bar{A}[/itex]-1A is coninvolutory.

I see that the identity matrix is a coninvolution. Does anyone have another example?
 
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  • #2
It is unclear what you are asking for. Are you asking
1) How to prove your lemma,
2) for examples of coinvolutory matrices, or
3) for general litterature on coinvolutory matrices?

The lemma is fairly straightforward to prove by calculating [itex]\left(\left(\overline{A}\right)^{-1}A\right)^{-1}[/itex] and showing that it is equal to [itex]\overline{\left(\overline{A}\right)^{-1}A}[/itex] using identities like
[tex](AB)^{-1}=B^{-1}A^{-1} \qquad \overline{AB} = \overline{A}\,\overline{B}[/tex]
If you are having trouble proving it, then tell us where you get stuck.

If you need examples of coinvolutory matrices, then just pick some nonsingular matrix A and calculate [itex]\left(\overline{A}\right)^{-1}A[/itex] as the lemma suggests. Alternatively if you write A = B + i C for real matrices B and C, then you can show that A is coinvolutory precisely if A^2 + B^2 = I and AB=BA. In particular if A is a real matrix, then it is coinvolutory if and only if it is involutory (i.e. iff A^2=I).

If you want to read more about coinvolutory matrices, then I can't help you as I have never heard of the term and a quick google search does not reveal much.
 
  • #3
I was asking for help on all three and your post really helped a lot! I'll try to be more clear in the the near future. Thank so much!
 
  • #4
rasmhop said:
If you need examples of coinvolutory matrices, then just pick some nonsingular matrix A and calculate [itex]\left(\overline{A}\right)^{-1}A[/itex] as the lemma suggests. Alternatively if you write A = B + i C for real matrices B and C, then you can show that A is coinvolutory precisely if A^2 + B^2 = I and AB=BA. In particular if A is a real matrix, then it is coinvolutory if and only if it is involutory (i.e. iff A^2=I).

Do you mean B^2 +C^2 = I and BC=CB?
 
  • #5
BrainHurts said:
Do you mean B^2 +C^2 = I and BC=CB?

Yes.
 

1. What is the definition of coninvolution of a matrix?

The coninvolution of a matrix is a mathematical operation that involves reversing the order of the elements in each row and column of a matrix, and then taking the conjugate of each element. This results in a new matrix with the same dimensions as the original matrix.

2. How is coninvolution different from inverse of a matrix?

Coninvolution and inverse of a matrix are two different mathematical operations. While coninvolution involves reversing the order and taking the conjugate of elements, inverse of a matrix involves finding a matrix that, when multiplied with the original matrix, gives the identity matrix. In other words, coninvolution results in a new matrix, while inverse results in a scalar value or a matrix.

3. What are the properties of coninvolution of a matrix?

The properties of coninvolution of a matrix include:

  • It is an involution, meaning that performing coninvolution twice on a matrix will result in the original matrix.
  • It is an anti-morphism, meaning that performing coninvolution on the product of two matrices is equivalent to performing coninvolution on each matrix separately and then multiplying them in reverse order.
  • It is not distributive, meaning that coninvolution of the sum of two matrices is not equal to the sum of their individual coninvolution.

4. What are the applications of coninvolution of a matrix?

Coninvolution of a matrix has applications in signal processing, image processing, and cryptography. It is used to reverse and decrypt encrypted data and to perform operations on signals and images such as filtering and compression.

5. Can every matrix be coninvoluted?

Yes, every matrix can be coninvoluted, as long as it is a square matrix. This means that the number of rows and columns must be equal. Non-square matrices cannot be coninvoluted.

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