Eigenvalues and -vectors
Eigenvalues λi of a square or hermitian matrix A are those scalar values for which vectors vi≠0 exist such that
In both cases the Eigenvalues λ are real. Considering the sets of all Eigenvalues λ1,...,λk and Eigenvectors v1,...,vk of a matrix A we may write
where Λ is the diagonal matrix whose diagonal elements are the Eigenvalues and V is the orthogonal (or unitary) matrix whose columns are the corresponding Eigenvectors.
The case of non-symmetric matrices A is largely identical to the symmetric case, except that the Eigenvalues λ may be complex. Moreover, in the symmetric case there is no difference between right Eigenvectors (see above) and left Eigenvecors given by
Non square matrices
Eigenvectors and Eigenvalues are only defined for square matrices. For general m x n matrices a related concept exists, the so called singular value decomposition (SVD). For more details please check the class reference for
Another extension of Eigenproblems are the so called generalized Eigenvalues and Eigenvectors. These are given by the solutions to
where A and B are symmetric (or hermitian) matrices and B is positive definite. This again may be generalized to the non-symmetric case or general m x n matrices (i.e. generalized SVD).
For more details on Eigenvalues, Eigenvectors, and related concepts see e.g. the correponding Wikipedia article. Furthermore, you might want to consult the original LAPACK Documentation providing details about the calculations.
Finding Eigenvectors and Eigenvalues in ILNumerics®
ILNumerics currently supports the solution of square symmetric and non-symmetric Eigenproblems (SVD as generalization to the non square case is support as well), and the solution of generalized Eigenproblems. Please see the API documentation for further details:
|Standard (non-symmetric) Eigenproblem|
|Symmetric Eigenproblems and Generalized Eigenproblems|