Properties of Inverse Matrices

In summary, A, B, and F are incorrect for all invertible nxn matrices A and B. However, C, D, and E are all correct. A counterexample for F would be any two matrices that do not commute. C is true due to a simple property of inverse matrices, while D holds only for real matrices with positive eigenvalues. E follows from the distribution property.
  • #1
tigger1989
3
0
Determine which of the formulas hold for all invertible nxn matrices A and B

A. AB=BA
B. (A+A^–1)^8=A^8+A^–8
C. A^5 is invertible
D. A+A^–1 is invertible
E. (In+A)(In+A^–1)=2In+A+A^–1 (where In is the identity matrix)
F. (A+B)^2=A^2+B^2+2AB

I was able to find counterexamples to prove A and B and F incorrect. However, the webwork program (designed for practicing basic Linear Algebra) I am using states that C, D, and E are not all correct ... what am I missing?
 
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  • #2
The ones that are false all have trivial counterexamples. What have you tried so far?
 
  • #3
zhentil said:
The ones that are false all have trivial counterexamples. What have you tried so far?
Hi, I'm a little rusty on Linear Algebra, so forgive my ignorance on some of the approaches. Can you elaborate by what you mean by "trivial"?

I tried random matrices (often with easy numbers, such as the identity matrix) for A, B, C, D, E, and F, but only found counterexamples for A, B, and F (but knowing for a fact that C, D, and E are not all correct).

By one of the simple properties of inverse matrices, I am almost certain that C is correct.
 
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  • #4
C is true, since otherwise A^(-1) is not invertible in the first place, with [itex]A^{-5}=A^{-1}...A^{-1}[/itex] hence if the determinant is zero then one of them has also a zero determinant.

D holds only for reals and from the positive eigenvalues of A^(2) .[itex]trace(A^2)>0, \det(A^2)>0[/itex]
[tex]
(A+A^{-1})= (I+A^{2})A^{-1} = (A(I+A^{2})^{-1})^{-1}
[/tex]

E follows from the distribution property,

I think I have to sleep so what is the the counterexample for F?
 
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  • #5
Any two matrices that do not commute will give an counterexample to F.
 
  • #6
Haha, Of course! That was lame, sorry!
 

What is an inverse matrix?

An inverse matrix is a square matrix that, when multiplied by the original matrix, results in the identity matrix. In other words, the inverse matrix "undoes" the original matrix.

How do you find the inverse of a matrix?

To find the inverse of a matrix, you can use the Gauss-Jordan elimination method or the adjugate matrix method. Both methods involve performing row operations on the matrix until it is in reduced row echelon form.

What is the significance of inverse matrices?

Inverse matrices are important because they allow us to solve systems of linear equations and perform other operations that would otherwise be difficult or impossible. They also have applications in areas such as computer graphics and cryptography.

Can all matrices have an inverse?

No, not all matrices have an inverse. A matrix must be square and have a non-zero determinant in order to have an inverse. If the determinant is zero, the matrix is singular and does not have an inverse.

Are inverse matrices commutative?

No, inverse matrices are not commutative. This means that the product of two inverse matrices is not necessarily equal to the inverse of the product of the two matrices. In other words, AB does not always equal BA when A and B are inverse matrices.

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