How Does a Minimal Polynomial Differ from a Characteristic Polynomial?

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SUMMARY

The discussion clarifies the distinction between minimal polynomials and characteristic polynomials in the context of linear algebra. The characteristic polynomial of matrix A, given by \( \lambda^2 - 4\lambda + 4 = (\lambda - 2)^2 \), is derived from its double eigenvalue of 2. In contrast, matrix B, which also has a double eigenvalue of 2, has a minimal polynomial that matches its characteristic polynomial due to the absence of independent eigenvectors. The key takeaway is that the minimal polynomial can be of lower degree than the characteristic polynomial when multiple eigenvalues exist with independent eigenvectors.

PREREQUISITES
  • Understanding of eigenvalues and eigenvectors in linear algebra
  • Familiarity with characteristic polynomials
  • Knowledge of minimal polynomials and their properties
  • Basic concepts of irreducible and primitive polynomials in abstract algebra
NEXT STEPS
  • Study the properties of minimal polynomials in detail
  • Explore the relationship between eigenvalues and eigenvectors
  • Learn about irreducible polynomials in the context of field theory
  • Investigate the implications of multiple eigenvalues on matrix diagonalization
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Students and professionals in mathematics, particularly those studying linear algebra, abstract algebra, or anyone seeking to deepen their understanding of polynomial characteristics in matrix theory.

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I have a small idea on what irreducible and primitive polynomials are in Abstract algebra. But what is minimal polynomial?

-Devanand T
 
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Why not look it up and then ask about the bits where you get confused?
 
The matrix
A= \begin{bmatrix}2 & 0 \\ 0 & 2\end{bmatrix}
has 2 as a double eigenvalue. Its characteristic polynomial is \lambda^2- 4\lambda + 4= (\lambda- 2)^2. Of course, the matrix itself satisfies A^2- 4A+ 4I= 0. But, here, it also satisfies A- 2I= 0. That is its "minimal" polynomial.

On the other hand,
B= \begin{bmatrix}2 & 1 \\ 0 & 2\end{bmatrix}
also has 2 as a double eigenvalue and, of course, satisfies its charateristic equation, (2- A)^2= 0, but does not satisfy A- 2= 0 so its minimal polynomial is the same as its characteristic polynomial.

One can show that all eigenvalues must be "represented", as factors, in the minimal polynomial so, if all eigenvalues are distinct, all the linear factors must be there- the minimal polynomial is the same as the characteristic polynomial. But if a matrix has a multiple eigenvalue with more than one independent corresponding eignvector, then we can remove some of those factors and get a minimal polynomial of degree lower than the characteristic polynomial.

I also recommend you look at the website Simon Bridge links to.
 

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