Developer Reference for Intel® oneAPI Math Kernel Library for C
Generalized Nonsymmetric Eigenvalue Problems: LAPACK Computational Routines
This topic describes LAPACK routines for solving generalized nonsymmetric eigenvalue problems, reordering the generalized Schur factorization of a pair of matrices, as well as performing a number of related computational tasks.
A generalized nonsymmetric eigenvalue problem is as follows: given a pair of nonsymmetric (or non-Hermitian) n -by- n matrices A and B , find the generalized eigenvalues λ and the corresponding generalized eigenvectors x and y that satisfy the equations
Ax = λBx (right generalized eigenvectors x )
and
y^{H}A = λy^{H}B (left generalized eigenvectors y ).
Computational Routines for Solving Generalized Nonsymmetric Eigenvalue Problems
Routine name |
Operation performed |
|---|---|
Reduces a pair of matrices to generalized upper Hessenberg form using orthogonal/unitary transformations. |
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Balances a pair of general real or complex matrices. |
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Forms the right or left eigenvectors of a generalized eigenvalue problem. |
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Reduces a pair of matrices to generalized upper Hessenberg form. |
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Implements the QZ method for finding the generalized eigenvalues of the matrix pair (H,T). |
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Computes some or all of the right and/or left generalized eigenvectors of a pair of upper triangular matrices |
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Reorders the generalized Schur decomposition of a pair of matrices (A,B) so that one diagonal block of (A,B) moves to another row index. |
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Reorders the generalized Schur decomposition of a pair of matrices (A,B) so that a selected cluster of eigenvalues appears in the leading diagonal blocks of (A,B). |
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Solves the generalized Sylvester equation. |
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Estimates reciprocal condition numbers for specified eigenvalues and/or eigenvectors of a pair of matrices in generalized real Schur canonical form. |