Three types of problems must be considered.
In all cases A and B
are real symmetric (or complex Hermitian) and B is positive definite.
These decompositions are computed for real symmetric matrices
by the driver routines
PxSYGVX (see section 3.2.4).
These decompositions are computed for complex Hermitian matrices
by the driver routines
PxHEGVX (see subsection 3.2.4).
In each of the following three decompositions,
is real and diagonal with diagonal entries
, and
the columns 
of Z are linearly independent vectors.
The 
are called
eigenvalues and the are
eigenvectors.
.
The eigendecomposition may be written 
and
(or 
and 
if A and B are complex).
This may also be written 
.
.
The eigendecomposition may be written 
and
(
and if A
and B are complex).
This may also be written 
.
.
The eigendecomposition may be written
and 
( and 
if A
and B are complex).
This may also be written 
.
The approximate error bounds
for the computed eigenvalues 
are
The approximate error
bounds
for the computed eigenvectors 
,
which bound the acute angles between the computed eigenvectors and true
eigenvectors , are
These bounds are computed differently, depending on which of the above three
problems are to be solved. The following code fragments show how.
EPSMCH = PSLAMCH( ICTXT, 'E' )
UNFL = PSLAMCH( ICTXT, 'U' )
* Solve the eigenproblem A - lambda B (ITYPE = 1)
ITYPE = 1
* Compute the norms of A and B
ANORM = PSLANSY( '1', UPLO, N, A, IA, JA, DESCA, WORK )
BNORM = PSLANSY( '1', UPLO, N, B, IB, JB, DESCB, WORK )
* The eigenvalues are returned in W
* The eigenvectors are returned in A
SUBROUTINE PSSYGVX( ITYPE, 'V', 'A', UPLO, N, A, IA, JA,
$ DESCA, B, IB, JB, DESCB, VL, VU, IL, IU,
$ UNFL, M, NZ, W, -1.0, Z, IZ, JZ, DESCZ,
$ WORK, LWORK, IWORK, LIWORK, IFAIL, ICLUSTR,
$ GAP, INFO )
IF( INFO.GT.0 ) THEN
PRINT *,'PSSYGVX did not converge, or B not positive definite'
ELSE IF( N.GT.0 ) THEN
* Get reciprocal condition number RCONDB of Cholesky factor of B
CALL PSTRCON( '1', UPLO, 'N', N, B, IB, JB, DESCB, RCONDB,
$ WORK, LWORK, IWORK, LIWORK, INFO )
RCONDB = MAX( RCONDB, EPSMCH )
CALL SDISNA( 'Eigenvectors', N, N, W, RCONDZ, INFO )
DO 10 I = 1, N
EERRBD( I ) = ( EPSMCH / RCONDB**2 ) * ( ANORM / BNORM +
$ ABS( W( I ) ) )
ZERRBD( I ) = ( EPSMCH / RCONDB**3 ) * ( ( ANORM / BNORM )
$ / RCONDZ( I ) + ( ABS( W( I ) ) /
$ RCONDZ( I ) ) * RCONDB )
10 CONTINUE
END IF
For example, if 
,
then 
,
, and
, and
the approximate eigenvalues, approximate error bounds,
and true errors are
shown below.
EPSMCH = PSLAMCH( ICTXT, 'E' )
* Solve the eigenproblem A*B - lambda I (ITYPE = 2)
ITYPE = 2
* Compute the norms of A and B
ANORM = PSLANSY( '1', UPLO, N, A, IA, JA, DESCA, WORK )
BNORM = PSLANSY( '1', UPLO, N, B, IB, JB, DESCB, WORK )
* The eigenvalues are returned in W
* The eigenvectors are returned in A
SUBROUTINE PSSYGVX( ITYPE, 'V', 'A', UPLO, N, A, IA, JA,
$ DESCA, B, IB, JB, DESCB, VL, VU, IL, IU,
$ UNFL, M, NZ, W, -1.0, Z, IZ, JZ, DESCZ,
$ WORK, LWORK, IWORK, LIWORK, IFAIL, ICLUSTR,
$ GAP, INFO )
IF( INFO.GT.0 .AND. INFO.LE.N ) THEN
PRINT *,'PSSYGVX did not converge'
ELSE IF( INFO.GT.N ) THEN
PRINT *,'B not positive definite'
ELSE IF( N.GT.0 ) THEN
* Get reciprocal condition number RCONDB of Cholesky factor of B
CALL PSTRCON( '1', UPLO, 'N', N, B, IB, JB, DESCB, RCONDB,
$ WORK, LWORK, IWORK, LIWORK, INFO )
RCONDB = MAX( RCONDB, EPSMCH )
CALL SDISNA( 'Eigenvectors', N, N, W, RCONDZ, INFO )
DO 10 I = 1, N
EERRBD(I) = ( ANORM * BNORM ) * EPSMCH +
$ ( EPSMCH / RCONDB**2 ) * ABS( W( I ) )
ZERRBD(I) = ( EPSMCH / RCONDB ) * ( ( ANORM * BNORM ) /
$ RCONDZ( I ) + 1.0 / RCONDB )
10 CONTINUE
END IF
For the same A and B as above, the approximate eigenvalues, approximate error bounds, and true errors are shown below.
Further Details: Error Bounds for the Generalized Symmetric Definite Eigenproblem
The error analysis of the driver routine PxSYGVX, or PxHEGVX in the complex case
(see section 3.2.4),
goes as follows.
In all cases
is
the absolute gap
between and the nearest other eigenvalue.
.
The computed eigenvalues 
can differ
from true eigenvalues by at most about
The angular difference between the computed eigenvector
and a true eigenvector is
or .
The computed eigenvalues can differ
from true eigenvalues by at most about
The angular difference between the computed eigenvector
and a true eigenvector is
The code fragments above replace p(n) by 1 and make sure neither RCONDB nor RCONDZ is so small as to cause overflow when used as divisors in the expressions for error bounds.
These error bounds are large when B is ill-conditioned with respect to
inversion (
is large). Often, the eigenvalues
and eigenvectors are much better conditioned than indicated here.
We mention two ways to get tighter bounds.
The first way is effective when the diagonal entries of B differ
widely in magnitude:
. Let 
be a diagonal matrix.
Then replace B by DBD and A by DAD in the above bounds.
or .
Let
be a diagonal matrix.
Then replace B by DBD and A by 
in the above bounds.
The second way to get tighter bounds does not actually supply guaranteed
bounds, but its estimates are often better in practice.
It is not guaranteed because it assumes the algorithm is backward stable,
which is not necessarily true when B is ill-conditioned.
It estimates the chordal distance between a
true eigenvalue and a computed eigenvalue :
To interpret this measure, we write 
and 
. Then
.
In other words, if represents the one-dimensional subspace
consisting of the line through the origin with slope ,
and represents the analogous subspace 
, then
is the sine of the acute angle 
between these
subspaces.
Thus, 
is bounded by one and is small when both arguments are
large.
It applies only to the first problem, :
Suppose a computed eigenvalue. Let
be the unit eigenvector (
) for the exact eigenvalue
is small compared with
, and if
is small compared with
, we have
Thusis a condition number for eigenvalue
