If A and B are invertible square matrices, there exists

This is because det is not a dense map. Its range is not all of R. You need a bit of the complex plane to get all values.
  • #1
AntiElephant
25
0

Homework Statement



If A and B are invertible matrices over an algebraically closed field [itex] k [/itex], show there exists [itex] \lambda \in k [/itex] such that [itex] det(\lambda A + B) = 0 [/itex].

The Attempt at a Solution



Can anyone agree with the following short proof? I tried looking online for a confirmation, but I wasn't exactly sure how to search exactly for this problem.

Suppose [itex] det(\lambda A + B) \neq 0 ~~\forall \lambda \in k [/itex]

Since [itex] det(A^{-1}) \neq 0 [/itex], then

[itex] det(A^{-1}(\lambda A + B)) = det(\lambda I + A^{-1}B) = det(A^{-1}B - (-\lambda)I) \neq 0 ~~\forall \lambda \in k [/itex]So [itex] -\lambda [/itex] is not an eigenvalue for [itex] A^{-1}B [/itex] for all [itex] -\lambda \in k [/itex]. i.e. [itex] \lambda [/itex] is not an eigenvalue of [itex] A^{-1}B [/itex] for all [itex] \lambda \in k [/itex]. Since [itex] k [/itex] is algebraically closed this cannot be the case.

Is this okay? I guess it is not necessarily true when [itex]k[/itex] is not algebraically closed?
 
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  • #2
AntiElephant said:

Homework Statement



If A and B are invertible matrices over an algebraically closed field [itex] k [/itex], show there exists [itex] \lambda \in k [/itex] such that [itex] det(\lambda A + B) [/itex].
? What are you asked to show about [itex]det(\lambda A+ B)[/itex]? You have a subject with no verb!

The Attempt at a Solution



Can anyone agree with the following short proof? I tried looking online for a confirmation, but I wasn't exactly sure how to search exactly for this problem.

Suppose [itex] det(\lambda A + B) \neq 0 ~~\forall \lambda \in k [/itex]

Since [itex] det(A^{-1}) \neq 0 [/itex], then

[itex] det(A^{-1}(\lambda A + B)) = det(\lambda I + A^{-1}B) = det(A^{-1}B - (-\lambda)I) \neq 0 ~~\forall \lambda \in k [/itex]So [itex] -\lambda [/itex] is not an eigenvalue for [itex] A^{-1}B [/itex] for all [itex] -\lambda \in k [/itex]. i.e. [itex] \lambda [/itex] is not an eigenvalue of [itex] A^{-1}B [/itex] for all [itex] \lambda \in k [/itex]. Since [itex] k [/itex] is algebraically closed this cannot be the case.

Is this okay? I guess it is not necessarily true when [itex]k[/itex] is not algebraically closed?
 
  • #3
My bad...early morning. Edited.
 
  • #4
AntiElephant said:
I guess it is not necessarily true when [itex]k[/itex] is not algebraically closed?

You bet it can fail. Take A=[[0,1],[1,0]] and B=[[2,0],[0,1]]. What goes wrong if k is the field of rationals?
 
  • #5
I didn't see that last bit about what happens when k is not algebraically closed. It can also fail with the reals. Just construct your matrices so the eigenvalues of A-1B are complex.
 
  • #6
D H said:
I didn't see that last bit about what happens when k is not algebraically closed. It can also fail with the reals. Just construct your matrices so the eigenvalues of A-1B are complex.

Sure. But that's because the reals aren't algebraically closed. The det gives you a real polynomial. If it has no real roots, you are out of luck.
 

Related to If A and B are invertible square matrices, there exists

1. What is an invertible square matrix?

An invertible square matrix is a square matrix that has an inverse, meaning there exists another square matrix that when multiplied by the original matrix results in the identity matrix. In other words, the inverse of a square matrix reverses the effects of the original matrix.

2. What does it mean if two matrices, A and B, are invertible square matrices?

If two matrices, A and B, are invertible square matrices, it means that both A and B have an inverse and that when multiplied together, the result is the identity matrix. This is written as AB = BA = I.

3. Does every square matrix have an inverse?

No, only invertible square matrices have an inverse. A square matrix is invertible if and only if its determinant is non-zero.

4. How do I find the inverse of a square matrix?

To find the inverse of a square matrix, you can use various methods such as Gaussian elimination or the adjugate method. However, the most efficient way is to use the inverse matrix formula, which is: A^-1 = (1/det(A)) * adj(A), where det(A) is the determinant of A and adj(A) is the adjugate of A.

5. Why is it important for two matrices to have an inverse?

Having an inverse is important because it allows for the solving of systems of linear equations, which have many real-world applications. It also allows for matrix operations such as division, which is not possible without an inverse.

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