Can I prove that I-S is a nonsingular matrix?

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In summary, Dick told me that the equation for x is always zero, but I am still unable to figure out what is wrong with that statement.
  • #1
Codezion
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Homework Statement



Given S is a Skew-Hermitian (S*=-S), Prove that I - S is a nonsingular matrix

Homework Equations



If a matrix A is nonsingular, for Ax=0, x={0}

The Attempt at a Solution



(I-S)x=0, and I have been trying to show that the solution for x is always zero. Is this the correctly direction, because I have been trying to do this since last night and I seem to be unable to do this. I would really appreciate it if you have a better suggestion. Thanks!
 
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  • #2
If (I-S) is singular, then there is a nonzero vector x such that <(I-S)x,(I-S)x>=0. (<> is the inner product). Play around with the consequences of that for a while.
 
  • #3
Thanks Dick:

After playing with it, I arrived at the concolusion I = S^2, but cannot see how to contradict that.

<(I-S)x, (I-S)x>
=[(I-S)x]*[(I-S)x]
=[Ix*-S*x*][Ix-Sx]
=[Ix*+Sx*][Ix-Sx]
=Ix*x+ISx*x - Ix*sx - Sx*Sx
=Ix*x - Sx*Sx (middle two cancel out)
=b(I) - b(S^2) (let x*x = b (some constant)
=0
=> I = S^2
Now, I have to show this is a false statement...right? Can we show this is false for all cases?
 
  • #4
Taking the square root of each side...Sqrt(I) = S, however, the square root of an identity matrix is by no means a skew Hermitian, hence the original assumption is erroneous!

Thanks Dick!
 
  • #5
Codezion said:
Taking the square root of each side...Sqrt(I) = S, however, the square root of an identity matrix is by no means a skew Hermitian, hence the original assumption is erroneous!

Thanks Dick!

No, no, no. You can't do any such thing. I think you have a stray sign. What I wound up with is <x,x>+<Sx,Sx>=0. What's wrong with that?
 
  • #6
Hmm...I played with it overnight and cannot figure out what is wrong with that statement. The only thing I can see is that x*x cannot be zero since x is a non zero vector..?
 
  • #7
<x,x> is positive since x is nonzero and the dot product is positive definite. <Sx,Sx> is nonnegative. The sum can't be zero.
 
  • #8
Wow! Thank you. I did not see that <x,x> is always positive, I was just focusing on it being nonzero!

Thank you so much!
 

1. What is the meaning of "Identity minus Skew-Hermitian" in mathematics?

In mathematics, "Identity minus Skew-Hermitian" refers to the operation of subtracting a skew-Hermitian matrix from the identity matrix. A skew-Hermitian matrix is a square matrix whose conjugate transpose is equal to its negative, while the identity matrix is a square matrix with ones on the main diagonal and zeros everywhere else.

2. How is "Identity minus Skew-Hermitian" used in linear algebra?

In linear algebra, "Identity minus Skew-Hermitian" is often used to represent the difference between a matrix and its conjugate transpose. This operation has applications in solving systems of linear equations, determining eigenvalues and eigenvectors, and studying symmetry and skew-symmetry in matrices.

3. What is the result of subtracting a skew-Hermitian matrix from the identity matrix?

The result of subtracting a skew-Hermitian matrix from the identity matrix is a matrix that is both Hermitian and unitary. This means that the matrix is equal to its own conjugate transpose and its inverse, respectively. In other words, the resulting matrix is symmetrical and preserves length and angles.

4. How does "Identity minus Skew-Hermitian" relate to complex numbers?

"Identity minus Skew-Hermitian" is closely related to complex numbers because skew-Hermitian matrices are a subset of complex matrices. In fact, a skew-Hermitian matrix can be represented in terms of complex numbers as a matrix whose imaginary part is the negative of its conjugate transpose.

5. What are some real-world applications of "Identity minus Skew-Hermitian"?

"Identity minus Skew-Hermitian" has various applications in fields such as physics, engineering, and computer science. For example, it is used in quantum mechanics to study the properties of quantum systems, in signal processing to analyze complex signals, and in image processing to manipulate and enhance images. It also has applications in cryptography and error correction codes.

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