Show Non-Singular Matrix A Has Non-Zero Minimal Polynomial Coefficient

In summary, the conversation discusses a problem involving a complex n x n matrix and its minimal polynomial. The goal is to prove that if the matrix is non-singular, then the constant term in the minimal polynomial cannot be zero. The conversation suggests multiplying both sides of the equation by the inverse of the matrix, but it is noted that this does not necessarily result in a lower degree polynomial. Ultimately, it is concluded that if the constant term is assumed to be zero, it would lead to a contradiction and therefore it must be non-zero.
  • #1
azdang
84
0
Here is my problem:

Let A be a complex n x n matrix with minimal polynomial q(x)=the sum from j=0 to m of [tex]\alpha_j x^j[/tex] where [tex]m\leq n[/tex] and [tex]\alpha_m[/tex] = 1.

Show: If A is non-singular then [tex]\alpha_0[/tex] does not equal 0.

So, I get that 0=q(A)=[tex]\alpha_0 I_n + \alpha_1 A + \alpha_2 A^2 +...+A^m[/tex], but I'm not sure what to do here. I assume we will have to use the fact that A is non-singular, but I'm not sure how. Does it maybe involve multiplying both sides by x on the right side? Any hints would be much appreciated! :)
 
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  • #2
Assume a0 is zero. See if you can find a way to reduce the degree of the minimal polynomial such that A is still a zero
 
  • #3
So, first, I've tried rewriting q(A) like this:
0=[tex]\alpha_0 I_n+(\alpha_1+\alpha_2 A+...+A^{m-1})A[/tex]

Then, if [tex]\alpha_0[/tex] is 0, then 0=[tex](\alpha_1+\alpha_2 A+...+A^{m-1})A[/tex].

So, if we multiply both sides by a nonzero vector x, we have:
0=[tex](\alpha_1+\alpha_2 A+...+A^{m-1})Ax[/tex].
But since x is nonzero and A is nonsingular, this cannot equal 0. Therefore, [tex]\alpha_0[/tex] must equal 0. Is my reasoning correct? I'm still not sure. Thanks!
 
  • #4
You don't know that [tex] \alpha_1 + ... + A^{m-1}[/tex] is non-singular. Instead of applying both operators to a vector, try multiplying on the right by A-1 and see what you have
 
  • #5
Well, then [tex]\alpha_1 + \alpha_2 A+...+A^{m-1}[/tex]=0. But I don't see how that helps because if we are assuming that [tex]\alpha_0[/tex] is 0, and then [tex]\alpha_1 + \alpha_2 A+...+A^{m-1}[/tex]=0, q(A) still equals 0, so it would give us what we want. I must be missing something :confused:
 
  • #6
Hey guys, I'm still having problems with this. As Office_Shredder suggested, I assume [tex]\alpha_0[/tex] is 0, and then multiply by the inverse of A on the right side of each side which leaves me with:

0=[tex]\alpha_1 + \alpha_2 A+...+A^{m-1}[/tex], but what to do with this I can't seem to see.
 
  • #7
Oh! Does this last equation set equal to 0 imply that the there would have to be a polynomial of lower degree (m-1)? Which would be impossible because we were talking about the minimal polynomial to begin with.
 
  • #8
Yea, that last statement sounds about right.
 

1. What is a non-singular matrix?

A non-singular matrix is a square matrix that has a non-zero determinant, meaning it is invertible.

2. What is a minimal polynomial coefficient?

A minimal polynomial coefficient is the smallest possible polynomial that a matrix can be multiplied by to equal the zero matrix.

3. Why does a non-singular matrix have a non-zero minimal polynomial coefficient?

A non-singular matrix has a non-zero minimal polynomial coefficient because it is invertible, meaning it cannot be multiplied by any other matrix to equal the zero matrix.

4. How can you prove that a non-singular matrix has a non-zero minimal polynomial coefficient?

A non-singular matrix can be proven to have a non-zero minimal polynomial coefficient by showing that its determinant is non-zero, which indicates that it is invertible and cannot be multiplied by any other matrix to equal the zero matrix.

5. What is the significance of a non-zero minimal polynomial coefficient in a non-singular matrix?

The non-zero minimal polynomial coefficient in a non-singular matrix shows that the matrix is invertible, meaning it has a unique inverse and can be used to solve systems of equations and perform other important mathematical operations.

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