Generalized Nonsymmetric Eigenproblem Operation Types
The generalized nonsymmetric eigenvalue problem can be solved via the
generalized Schur decomposition
of the matrix pair (A, B), defined in the real case as
where Q and Z are orthogonal matrices, T is upper triangular,
and S is an upper quasi-triangular matrix with 1-by-1 and 2-by-2 diagonal
blocks, the 2-by-2 blocks corresponding to complex conjugate pairs of eigenvalues
of (A, B). In the complex case, the generalized Schur decomposition is
where Q and Z are unitary and S and T are both upper triangular.
The columns of Q and Z are called left and right generalized Schur
vectors
and span pairs of deflating subspaces of A and B
[93].
Deflating subspaces are a generalization of invariant subspaces:
For each k
,
the first k columns of Z span a right
deflating subspace mapped by both A and B into a left deflating subspace
spanned by the first k columns of Q.
More formally, let
Q = (Q1, Q2) and
Z = (Z1, Z2) be a conformal
partitioning with respect to the cluster of k eigenvalues in the
(1,1)-block of (S, T), i.e. where Q1 and Z1 both have k columns,
and S11 and T11 below are both k-by-k,
Then subspaces
and
form a pair of (left and right) deflating subspaces associated with the
cluster of
(S11,T11), satisfying
and
[94,95].
It is possible to order the generalized Schur form so that
(S11, T11) has any desired subset of k eigenvalues,
taken from the set of n eigenvalues of (S,T).
As for the standard nonsymmetric eigenproblem,
two pairs of drivers are provided,
one pair focusing on the generalized Schur decomposition, and the other pair
on the eigenvalues and eigenvectors as shown in Table 2.6:
- xGGES:
a simple driver that computes all or part of the
generalized Schur decomposition of (A, B), with optional
ordering of the eigenvalues;
- xGGESX:
an expert driver that can additionally compute condition
numbers for the average of a selected subset of eigenvalues,
and for the corresponding pair of deflating subspaces;
- xGGEV:
a simple driver that computes all the generalized
eigenvalues of (A, B), and optionally the left or right
eigenvectors (or both);
- xGGEVX:
an expert driver that can additionally balance the
matrix pair to improve the conditioning of the eigenvalues and
eigenvectors, and compute condition numbers for the
eigenvalues and/or left and right eigenvectors (or both).
To save space in Table 2.6, the word ``generalized'' is
omitted before Schur decomposition, eigenvalues/vectors and singular
values/vectors.
The subroutines xGGES and xGGEV are improved versions of the drivers,
xGEGS and xGEGV, respectively. xGEGS and xGEGV have been retained for
compatibility with Release 2.0 of LAPACK, but we omit references to these
routines in the remainder of this users' guide.
1999-12-26