ctgsna(3S)

CTGSNA - estimate reciprocal condition numbers for specified eigenvalues and/or eigenvectors of a matrix pair (A, B)

As shipped in IRIX 6.5.19. Added in IRIX 6.5.15.

NAME
     CTGSNA - estimate reciprocal condition numbers for specified eigenvalues
     and/or eigenvectors of a matrix pair (A, B)

SYNOPSIS
     SUBROUTINE CTGSNA( JOB, HOWMNY, SELECT, N, A, LDA, B, LDB, VL, LDVL, VR,
                        LDVR, S, DIF, MM, M, WORK, LWORK, IWORK, INFO )

         CHARACTER      HOWMNY, JOB

         INTEGER        INFO, LDA, LDB, LDVL, LDVR, LWORK, M, MM, N

         LOGICAL        SELECT( * )

         INTEGER        IWORK( * )

         REAL           DIF( * ), S( * )

         COMPLEX        A( LDA, * ), B( LDB, * ), VL( LDVL, * ), VR( LDVR, *
                        ), WORK( * )

IMPLEMENTATION
     These routines are part of the SCSL Scientific Library and can be loaded
     using either the -lscs or the -lscs_mp option.  The -lscs_mp option
     directs the linker to use the multi-processor version of the library.

     When linking to SCSL with -lscs or -lscs_mp, the default integer size is
     4 bytes (32 bits). Another version of SCSL is available in which integers
     are 8 bytes (64 bits).  This version allows the user access to larger
     memory sizes and helps when porting legacy Cray codes.  It can be loaded
     by using the -lscs_i8 option or the -lscs_i8_mp option. A program may use
     only one of the two versions; 4-byte integer and 8-byte integer library
     calls cannot be mixed.

PURPOSE
     CTGSNA estimates reciprocal condition numbers for specified eigenvalues
     and/or eigenvectors of a matrix pair (A, B). (A, B) must be in
     generalized Schur canonical form, that is, A and B are both upper
     triangular.


ARGUMENTS
     JOB     (input) CHARACTER*1
             Specifies whether condition numbers are required for eigenvalues
             (S) or eigenvectors (DIF):
             = 'E': for eigenvalues only (S);
             = 'V': for eigenvectors only (DIF);
             = 'B': for both eigenvalues and eigenvectors (S and DIF).

     HOWMNY  (input) CHARACTER*1
             = 'A': compute condition numbers for all eigenpairs;
             = 'S': compute condition numbers for selected eigenpairs
             specified by the array SELECT.

     SELECT  (input) LOGICAL array, dimension (N)
             If HOWMNY = 'S', SELECT specifies the eigenpairs for which
             condition numbers are required. To select condition numbers for
             the corresponding j-th eigenvalue and/or eigenvector, SELECT(j)
             must be set to .TRUE..  If HOWMNY = 'A', SELECT is not
             referenced.

     N       (input) INTEGER
             The order of the square matrix pair (A, B). N >= 0.

     A       (input) COMPLEX array, dimension (LDA,N)
             The upper triangular matrix A in the pair (A,B).

     LDA     (input) INTEGER
             The leading dimension of the array A. LDA >= max(1,N).

     B       (input) COMPLEX array, dimension (LDB,N)
             The upper triangular matrix B in the pair (A, B).

     LDB     (input) INTEGER
             The leading dimension of the array B. LDB >= max(1,N).

     VL      (input) COMPLEX array, dimension (LDVL,M)
             IF JOB = 'E' or 'B', VL must contain left eigenvectors of (A, B),
             corresponding to the eigenpairs specified by HOWMNY and SELECT.
             The eigenvectors must be stored in consecutive columns of VL, as
             returned by CTGEVC.  If JOB = 'V', VL is not referenced.

     LDVL    (input) INTEGER
             The leading dimension of the array VL. LDVL >= 1; and If JOB =
             'E' or 'B', LDVL >= N.

     VR      (input) COMPLEX array, dimension (LDVR,M)
             IF JOB = 'E' or 'B', VR must contain right eigenvectors of (A,
             B), corresponding to the eigenpairs specified by HOWMNY and
             SELECT.  The eigenvectors must be stored in consecutive columns
             of VR, as returned by CTGEVC.  If JOB = 'V', VR is not
             referenced.

     LDVR    (input) INTEGER
             The leading dimension of the array VR. LDVR >= 1; If JOB = 'E' or
             'B', LDVR >= N.

     S       (output) REAL array, dimension (MM)
             If JOB = 'E' or 'B', the reciprocal condition numbers of the
             selected eigenvalues, stored in consecutive elements of the
             array.  If JOB = 'V', S is not referenced.

     DIF     (output) REAL array, dimension (MM)
             If JOB = 'V' or 'B', the estimated reciprocal condition numbers
             of the selected eigenvectors, stored in consecutive elements of
             the array.  If the eigenvalues cannot be reordered to compute
             DIF(j), DIF(j) is set to 0; this can only occur when the true
             value would be very small anyway.  For each eigenvalue/vector
             specified by SELECT, DIF stores a Frobenius norm-based estimate
             of Difl.  If JOB = 'E', DIF is not referenced.

     MM      (input) INTEGER
             The number of elements in the arrays S and DIF. MM >= M.

     M       (output) INTEGER
             The number of elements of the arrays S and DIF used to store the
             specified condition numbers; for each selected eigenvalue one
             element is used. If HOWMNY = 'A', M is set to N.

     WORK    (workspace/output) COMPLEX array, dimension (LWORK)
             If JOB = 'E', WORK is not referenced.  Otherwise, on exit, if
             INFO = 0, WORK(1) returns the optimal LWORK.

     LWORK  (input) INTEGER
            The dimension of the array WORK. LWORK >= 1.  If JOB = 'V' or 'B',
            LWORK >= 2*N*N.

     IWORK   (workspace) INTEGER array, dimension (N+2)
             If JOB = 'E', IWORK is not referenced.

     INFO    (output) INTEGER
             = 0: Successful exit
             < 0: If INFO = -i, the i-th argument had an illegal value

FURTHER DETAILS
     The reciprocal of the condition number of the i-th generalized eigenvalue
     w = (a, b) is defined as

             S(I) = (|v'Au|**2 + |v'Bu|**2)**(1/2) / (norm(u)*norm(v))

     where u and v are the right and left eigenvectors of (A, B) corresponding
     to w; |z| denotes the absolute value of the complex number, and norm(u)
     denotes the 2-norm of the vector u. The pair (a, b) corresponds to an
     eigenvalue w = a/b (= v'Au/v'Bu) of the matrix pair (A, B). If both a and
     b equal zero, then (A,B) is singular and S(I) = -1 is returned.

     An approximate error bound on the chordal distance between the i-th
     computed generalized eigenvalue w and the corresponding exact eigenvalue
     lambda is

             chord(w, lambda) <=   EPS * norm(A, B) / S(I),

     where EPS is the machine precision.

     The reciprocal of the condition number of the right eigenvector u and
     left eigenvector v corresponding to the generalized eigenvalue w is
     defined as follows. Suppose

                      (A, B) = ( a   *  ) ( b  *  )  1
                               ( 0  A22 ),( 0 B22 )  n-1
                                 1  n-1     1 n-1

     Then the reciprocal condition number DIF(I) is

             Difl[(a, b), (A22, B22)]  = sigma-min( Zl )

     where sigma-min(Zl) denotes the smallest singular value of

            Zl = [ kron(a, In-1) -kron(1, A22) ]
                 [ kron(b, In-1) -kron(1, B22) ].

     Here In-1 is the identity matrix of size n-1 and X' is the conjugate
     transpose of X. kron(X, Y) is the Kronecker product between the matrices
     X and Y.

     We approximate the smallest singular value of Zl with an upper bound.
     This is done by CLATDF.

     An approximate error bound for a computed eigenvector VL(i) or VR(i) is
     given by

                         EPS * norm(A, B) / DIF(i).

     See ref. [2-3] for more details and further references.

     Based on contributions by
        Bo Kagstrom and Peter Poromaa, Department of Computing Science,
        Umea University, S-901 87 Umea, Sweden.

     References
     ==========

     [1] B. Kagstrom; A Direct Method for Reordering Eigenvalues in the
         Generalized Real Schur Form of a Regular Matrix Pair (A, B), in
         M.S. Moonen et al (eds), Linear Algebra for Large Scale and
         Real-Time Applications, Kluwer Academic Publ. 1993, pp 195-218.

     [2] B. Kagstrom and P. Poromaa; Computing Eigenspaces with Specified
         Eigenvalues of a Regular Matrix Pair (A, B) and Condition
         Estimation: Theory, Algorithms and Software, Report
         UMINF - 94.04, Department of Computing Science, Umea University,
         S-901 87 Umea, Sweden, 1994. Also as LAPACK Working Note 87.
         To appear in Numerical Algorithms, 1996.

     [3] B. Kagstrom and P. Poromaa, LAPACK-Style Algorithms and Software
         for Solving the Generalized Sylvester Equation and Estimating the
         Separation between Regular Matrix Pairs, Report UMINF - 93.23,
         Department of Computing Science, Umea University, S-901 87 Umea,
         Sweden, December 1993, Revised April 1994, Also as LAPACK Working
         Note 75.
         To appear in ACM Trans. on Math. Software, Vol 22, No 1, 1996.


SEE ALSO
     INTRO_LAPACK(3S), INTRO_SCSL(3S)

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