Eigenvalue vs Eigendecomposition - What's the difference?
eigenvalue | eigendecomposition |
(linear algebra) A scalar, , such that there exists a vector (the corresponding eigenvector) for which the image of under a given linear operator is equal to the image of under multiplication by ; i.e.
(linear algebra) The factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors.
In linear algebra|lang=en terms the difference between eigenvalue and eigendecomposition
is that eigenvalue is (linear algebra) a scalar, , such that there exists a vector (the corresponding eigenvector) for which the image of under a given linear operator is equal to the image of under multiplication by ; ie while eigendecomposition is (linear algebra) the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors.As nouns the difference between eigenvalue and eigendecomposition
is that eigenvalue is (linear algebra) a scalar, , such that there exists a vector (the corresponding eigenvector) for which the image of under a given linear operator is equal to the image of under multiplication by ; ie while eigendecomposition is (linear algebra) the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors.eigenvalue
English
Noun
(en noun)- ''The eigenvalues of a square transformation matrix may be found by solving .