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catsarebad
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Homework Statement
show that minimal poly for a sq matrix and its transpose is the same
Homework Equations
The Attempt at a Solution
no clue.
Last edited:
catsarebad said:Homework Statement
show that minimal poly for a sq matrix and its transpose is the same
Homework Equations
The Attempt at a Solution
no clue.
pasmith said:Let [itex]\lambda[/itex] be an eigenvalue of [itex]A[/itex] of geometric multiplicity [itex]n[/itex]. Then
[tex](A - \lambda I)^n = 0[/tex]
but
[tex](A - \lambda I)^{m} \neq 0[/tex]
for every positive integer [itex]m < n[/itex].
Given that, can you show that [itex](A^T - \lambda I)^n = 0[/itex] and that there does not exist a positive integer [itex]m < n[/itex] such that [itex](A^T - \lambda I)^m = 0[/itex]?
catsarebad said:i'm not sure where we are going with this.
i assume this is a property
[tex](A - \lambda I)^n = 0[/tex]
but
[tex](A - \lambda I)^{m} \neq 0[/tex]
for every positive integer [itex]m < n[/itex].
but i don't get how showing the next part will help with minimal poly problem.
A minimal polynomial for a square matrix is the smallest degree monic polynomial that the matrix satisfies. This means that if the matrix A satisfies the polynomial p(x), then there is no other polynomial of smaller degree that A satisfies.
The minimal polynomial for a square matrix A and its transpose AT are related in that they have the same degree and the same roots. This means that if A satisfies the minimal polynomial p(x), then AT will also satisfy p(x).
Showing that the minimal polynomial for a square matrix and its transpose are the same is important because it provides a deeper understanding of the properties of the matrix. It also allows for easier computation and analysis of the matrix, as the minimal polynomial can provide information about the matrix's eigenvalues and other important characteristics.
The most common way to prove that the minimal polynomial for a square matrix and its transpose are the same is by using the Cayley-Hamilton theorem. This theorem states that a matrix satisfies its own characteristic polynomial, and since the characteristic polynomial is the same for a matrix and its transpose, this proves that the minimal polynomial is also the same.
Yes, there are some rare cases where the minimal polynomial for a square matrix and its transpose may not be the same. This can occur when the matrix has complex eigenvalues or when the minimal polynomial has repeated roots. However, in most cases, the minimal polynomial will be the same for a matrix and its transpose.