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  • Matrix diagonalization

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  • Definition of matrix diagonalization
  • Definition of diagonalizable matrix
  • An n-by-n matrix is diagonalizable if and only if it has n linearly independent eigenvectors.
  • An n-by-n matrix is diagonalizable if and only if the sum of the dimensions of the eigenspaces equals n.
  • An n-by-n matrix is diagonalizable if and only if the characteristic polynomial factors completely
  • A diagonalizable matrix is diagonalized by a matrix having the eigenvectors as columns.
  • An n-by-n matrix is diagonalizable if and only if the union of the basis vectors for the eigenspaces is a basis for R^n (or C^n).
  • An n-by-n matrix with n distinct eigenvalues is diagonalizable.
  • Formula for diagonalizing a real 2-by-2 matrix with a complex eigenvalue.
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  1. An n-by-n matrix is diagonalizable if and only if the union of the basis vectors for the eigenspaces is a basis for R^n (or C^n).

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326–350 of 715 nodes
  • Definition of weights in a linear combination of vectors#23802
  • Description of a basis for the null space of a matrix from the reduced row-echelon form.#23870
  • Description of a spanning set for the null space of a matrix from the reduced row-echelon form.#23869
  • Description of the Gram-Schmidt process#23935
  • Determinants#23714
  • Determinants and operations on matrices#23723
  • Determinants axiomatically#23732
  • Determine if a particular set of vectors in R^3 in linearly independent#23818
  • Determine if a particular set of vectors spans R^3#23807
  • Determine if a particular vector is in the span of a set of vectors#23808
  • Determine if a particular vector is in the span of a set of vectors in R^2#23809
  • Determine if a particular vector is in the span of a set of vectors in R^3#23810
  • Dimension#23836
  • Distinct eigenvalues of a Hermitian matrix have orthogonal eigenvectors.#23712
  • Each vector can be written uniquely as a linear combination of vectors from a given basis.#23987
  • Echelon matrices#23536
  • Eigenspaces#23683
  • Eigenvalues and eigenvectors#23680
  • Eigenvalues and operations on matrices#23688
  • Eigenvectors of a symmetric matrix with different eigenvalues are orthogonal.#23574
  • Eigenvectors with distinct eigenvalues are linearly independent.#23694
  • Elementary matrices#23556
  • Elementary matrices are invertible/nonsingular.#23560
  • Equation operations on a linear system give an equivalent system.#23425
  • Equivalence theorem for injective linear transformations: The columns of the matrix of T are linearly independent.#24086
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