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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 direct sum of invariant subspaces theorem
# If a space is the direct sum of invariant subspaces

Put content here**Theorem:** If \(V = W_1 \oplus W_2 \oplus \cdots \oplus W_k\) is a direct sum of \(T\)-invariant subspaces, then the matrix of \(T\) (with respect to a basis adapted to this decomposition) is block diagonal:
\[[T] = egin{pmatrix} A_1 & 0 & \cdots & 0 \ 0 & A_2 & \cdots & 0 \ dots & dots & \ddots & dots \ 0 & 0 & \cdots & A_k \end{pmatrix}\]
⏎
where each block \(A_i\) represents the restriction \(T|_{W_i}: W_i 	o W_i\).
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**Intuition:** When a space decomposes into invariant pieces, the transformation acts independently on each piece. The behavior of \(T\) on \(W_i\) does not affect vectors in \(W_j\) (for \(i 
eq j\)).
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**Example:** If \(V = E_{\lambda_1} \oplus E_{\lambda_2}\) where \(E_{\lambda_i}\) are eigenspaces, then \(T\) is represented by a diagonal matrix with \(\lambda_1\) and \(\lambda_2\) on the diagonal. This is the essence of diagonalization.

# Parents

* Eigenvalues and eigenvectors
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