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Parents1

  • Eigenvalues and eigenvectors

Siblings22
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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

Children5
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  • The eigenvalues of a scalar multiple of a matrix are the scalar multiples of the eigenvalues.
  • The eigenvalues of a power of a matrix are the power the eigenvalues.
  • The eigenvalues of a polynomial of a matrix are the polynomial of the eigenvalues.
  • The eigenvalues of the inverse of a nonsingular matrix are the reciprocals of the eigenvalues.
  • A matrix and its transpose have the same eigenvalues/characteristic polynomial.
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Description:Added eigenvalues and operations
# Eigenvalues and operations on matrices

Put content here.**Properties of eigenvalues under matrix operations:**
- If $\lambda$ is an eigenvalue of $A$, then $\lambda^k$ is an eigenvalue of $A^k$
- If $\lambda$ is an eigenvalue of $A$, then $1/\lambda$ is an eigenvalue of $A^{-1}$ (if $A$ invertible)
- If $\lambda$ is an eigenvalue of $A$, then $\lambda + c$ is an eigenvalue of $A + cI$
- If $\lambda$ is an eigenvalue of $A$, then $c\lambda$ is an eigenvalue of $cA$
- Similar matrices have the same eigenvalues

# Parents

* Eigenvalues and eigenvectors
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