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

\begin{displaymath}
A = Q S Z^T, \quad B = Q T Z^T
\end{displaymath}

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

\begin{displaymath}
A = Q S Z^H, \quad B = Q T Z^H
\end{displaymath}

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 $(1 \leq k \leq n)$, 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,

\begin{displaymath}
\left( \begin{array}{c} Q^H_1 \\ Q^H_2 \end{array} \right)
...
...rray}{cc} T_{11} & T_{12} \\
0 & T_{22} \end{array} \right).
\end{displaymath}

Then subspaces ${\cal{L}} = \mbox{span}(Q_1)$ and ${\cal{R}} = \mbox{span}(Z_1)$ form a pair of (left and right) deflating subspaces associated with the cluster of (S11,T11), satisfying ${\cal{L}} = A{\cal{R}} + B{\cal{R}}$ and $\mbox{dim}(\cal{L}) = \mbox{dim}(\cal{R})$ [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:

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