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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
=
λ
Bx
(right generalized eigenvectors
x
)
and
y
H
A
=
λ
y
H
B
(left generalized eigenvectors
y
).
Table
"Computational Routines for Solving Generalized Nonsymmetric Eigenvalue Problems"
lists LAPACK routines used to solve the generalized nonsymmetric eigenvalue problems and the generalized Sylvester equation.
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.
Balances a pair of general real or complex matrices.
Forms the right or left eigenvectors of a generalized eigenvalue problem.
Reduces a pair of matrices to generalized upper Hessenberg form.
Implements the QZ method for finding the generalized eigenvalues of the matrix pair (H,T).
Computes some or all of the right and/or left generalized eigenvectors of a pair of upper triangular matrices
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.
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).
Solves the generalized Sylvester equation.
Estimates reciprocal condition numbers for specified eigenvalues and/or eigenvectors of a pair of matrices in generalized real Schur canonical form.

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