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  • Eigenvalues and eigenvectors

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  • Particular types of matrices
  • Definition of eigenvalue of a matrix
  • Definition of eigenvector of a matrix
  • Eigenspaces
  • Every matrix has an eigenvalue over the complex numbers.
  • Eigenvalues and operations on matrices
  • Eigenvectors with distinct eigenvalues are linearly independent.
  • Multiplicity
  • Characteristic and minimal polynomials
  • The dimension of a eigenspace is less than or equal to the (algebraic) multiplicity of the eigenvalue.
  • Definition of eigenvalue/characteristic value of a linear transformation
  • Definition of eigenvector/characteristic vector of a linear transformation
  • Definition of characteristic polynomial of a linear transformation
  • Definition of minimal polynomial of a linear transformation
  • The Cayley-Hamilton theorem for a linear transformation
  • The minimal polynomial of a linear transformation exists and is unique.
  • Definition of applying a polynomial to a linear transformation
  • A linear transformation on a finite dimentional nontrivial vector space has at least one eigenvalue.
  • Definition of eigenspace of a linear transformation
  • The eigenspace of a linear transformation is a nontrivial subspace.
  • Definition of invariant subspace of a linear transformation.
  • If a space is the direct sum of invariant subspaces
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Description:Added minimal polynomial definition
# Definition of minimal polynomial of a linear transformation

Put content here**Definition:** The *minimal polynomial* of a linear transformation \(T: V 	o V\) is the unique monic polynomial \(m_T(x)\) of least degree such that \(m_T(T) = 0\) (the zero transformation).
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**Properties:**
- The minimal polynomial divides every polynomial \(p(x)\) for which \(p(T) = 0\)
- The minimal polynomial divides the characteristic polynomial
- The minimal polynomial and characteristic polynomial have the same irreducible factors (possibly with different multiplicities)
- The minimal polynomial is unique
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**Example:** For \(T\) represented by \(A = egin{pmatrix} 2 & 0 \ 0 & 2 \end{pmatrix}\), the characteristic polynomial is \((\lambda-2)^2\) but the minimal polynomial is \((\lambda-2)\) since \(A - 2I = 0\).

# Parents

* Eigenvalues and eigenvectors
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