/home/abui.../sggsvd3.f(3)

📦 version 3.12.0, in opensuse-leap 16.0

💅 NAME 💅

Okay bestie, this is the manual page for /home/abui.../sggsvd3.f(3) — a Library Functions Manual situation. The NAME line is literally this source-file path:

/home/abuild/rpmbuild/BUILD/lapack-static-3.12.0-build/lapack-3.12.0/SRC/sggsvd3.f

✨ SYNOPSIS ✨

Functions/Subroutines

subroutine SGGSVD3 (jobu, jobv, jobq, m, n, p, k, l, a, lda, b, ldb,
    alpha, beta, u, ldu, v, ldv, q, ldq, work, lwork, iwork, info)

SGGSVD3 computes the singular value decomposition (SVD) for OTHER matrices. Yes, OTHER. Main-character OTHER matrices. 👑

🌸 Function/Subroutine Documentation 🌸

subroutine SGGSVD3

subroutine SGGSVD3 (character jobu, character jobv, character jobq, integer
    m, integer n, integer p, integer k, integer l, real, dimension( lda, * )
    a, integer lda, real, dimension( ldb, * ) b, integer ldb, real,
    dimension( * ) alpha, real, dimension( * ) beta, real, dimension( ldu, *
    ) u, integer ldu, real, dimension( ldv, * ) v, integer ldv, real,
    dimension( ldq, * ) q, integer ldq, real, dimension( * ) work, integer
    lwork, integer, dimension( * ) iwork, integer info)

SGGSVD3 computes the singular value decomposition (SVD) for OTHER matrices. It is giving generalized-SVD excellence, no cap.

Purpose

SGGSVD3 computes the generalized singular value decomposition (GSVD) of an M-by-N real matrix A and P-by-N real matrix B:

      U**T*A*Q = D1*( 0 R ),    V**T*B*Q = D2*( 0 R )

where U, V and Q are orthogonal matrices.

Let K+L = the effective numerical rank of the matrix (AT,BT)**T, then R is a K+L-by-K+L nonsingular upper triangular matrix, D1 and D2 are M-by-(K+L) and P-by-(K+L) matrices and of the following structures, respectively:

If M-K-L >= 0,

                    K  L
       D1 =     K ( I  0 )
                L ( 0  C )
            M-K-L ( 0  0 )

                  K  L
       D2 =   L ( 0  S )
            P-L ( 0  0 )

                N-K-L  K    L
  ( 0 R ) = K (  0   R11  R12 )
            L (  0    0   R22 )

where

  C = diag( ALPHA(K+1), ... , ALPHA(K+L) ),
  S = diag( BETA(K+1),  ... , BETA(K+L) ),
  C**2 + S**2 = I.

  R is stored in A(1:K+L,N-K-L+1:N) on exit.

If M-K-L < 0,

                  K M-K K+L-M
       D1 =   K ( I  0    0   )
            M-K ( 0  C    0   )

                    K M-K K+L-M
       D2 =   M-K ( 0  S    0  )
            K+L-M ( 0  0    I  )
              P-L ( 0  0    0  )

                   N-K-L  K   M-K  K+L-M
  ( 0 R ) =     K ( 0    R11  R12  R13  )
              M-K ( 0     0   R22  R23  )
            K+L-M ( 0     0    0   R33  )

where

  C = diag( ALPHA(K+1), ... , ALPHA(M) ),
  S = diag( BETA(K+1),  ... , BETA(M) ),
  C**2 + S**2 = I.

  (R11 R12 R13 ) is stored in A(1:M, N-K-L+1:N), and R33 is stored
  ( 0  R22 R23 )
  in B(M-K+1:L,N+M-K-L+1:N) on exit.

The routine computes C, S, R, and optionally the orthogonal transformation matrices U, V and Q.

In particular, if B is an N-by-N nonsingular matrix, then the GSVD of A and B implicitly gives the SVD of A*inv(B):

                     A*inv(B) = U*(D1*inv(D2))*V**T.

If ( AT,BT)**T has orthonormal columns, then the GSVD of A and B is also equal to the CS decomposition of A and B. Furthermore, the GSVD can be used to derive the solution of the eigenvalue problem:

                     A**T*A x = lambda* B**T*B x.

In some literature, the GSVD of A and B is presented in the form

                 U**T*A*X = ( 0 D1 ),   V**T*B*X = ( 0 D2 )

where U and V are orthogonal and X is nonsingular, D1 and D2 are “diagonal”. The former GSVD form can be converted to the latter form by taking the nonsingular matrix X as

                     X = Q*( I   0    )
                           ( 0 inv(R) ).

🎀 Parameters 🎀

  • JOBU — JOBU is CHARACTER*1. = 'U': Orthogonal matrix U is computed; = 'N': U is not computed. Pick your fighter. 💅
  • JOBV — JOBV is CHARACTER*1. = 'V': Orthogonal matrix V is computed; = 'N': V is not computed.
  • JOBQ — JOBQ is CHARACTER*1. = 'Q': Orthogonal matrix Q is computed; = 'N': Q is not computed.
  • M — M is INTEGER. The number of rows of the matrix A. M >= 0.
  • N — N is INTEGER. The number of columns of the matrices A and B. N >= 0.
  • P — P is INTEGER. The number of rows of the matrix B. P >= 0.
  • K — K is INTEGER. That’s the whole note in the original, bestie.
  • L — L is INTEGER.

On exit, K and L specify the dimension of the subblocks described in Purpose. K + L = effective numerical rank of (A**T,B**T)**T.

  • A — A is REAL array, dimension (LDA,N). On entry, the M-by-N matrix A. On exit, A contains the triangular matrix R, or part of R. See Purpose for details.
  • LDA — LDA is INTEGER. The leading dimension of the array A. LDA >= max(1,M).
  • B — B is REAL array, dimension (LDB,N). On entry, the P-by-N matrix B. On exit, B contains the triangular matrix R if M-K-L < 0. See Purpose for details.
  • LDB — LDB is INTEGER. The leading dimension of the array B. LDB >= max(1,P).
  • ALPHA — ALPHA is REAL array, dimension (N).
  • BETA — BETA is REAL array, dimension (N).

On exit, ALPHA and BETA contain the generalized singular value pairs of A and B:

  ALPHA(1:K) = 1,
  BETA(1:K)  = 0,

and if M-K-L >= 0,

  ALPHA(K+1:K+L) = C,
  BETA(K+1:K+L)  = S,

or if M-K-L < 0,

  ALPHA(K+1:M)=C, ALPHA(M+1:K+L)=0
  BETA(K+1:M) =S, BETA(M+1:K+L) =1

and

  ALPHA(K+L+1:N) = 0
  BETA(K+L+1:N)  = 0
  • U — U is REAL array, dimension (LDU,M). If JOBU = 'U', U contains the M-by-M orthogonal matrix U. If JOBU = 'N', U is not referenced.
  • LDU — LDU is INTEGER. The leading dimension of the array U. LDU >= max(1,M) if JOBU = 'U'; LDU >= 1 otherwise.
  • V — V is REAL array, dimension (LDV,P). If JOBV = 'V', V contains the P-by-P orthogonal matrix V. If JOBV = 'N', V is not referenced.
  • LDV — LDV is INTEGER. The leading dimension of the array V. LDV >= max(1,P) if JOBV = 'V'; LDV >= 1 otherwise.
  • Q — Q is REAL array, dimension (LDQ,N). If JOBQ = 'Q', Q contains the N-by-N orthogonal matrix Q. If JOBQ = 'N', Q is not referenced.
  • LDQ — LDQ is INTEGER. The leading dimension of the array Q. LDQ >= max(1,N) if JOBQ = 'Q'; LDQ >= 1 otherwise.
  • WORK — WORK is REAL array, dimension (MAX(1,LWORK)). On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
  • LWORK — LWORK is INTEGER. The dimension of the array WORK.

If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA.

  • IWORK — IWORK is INTEGER array, dimension (N). On exit, IWORK stores the sorting information. More precisely, the following loop will sort ALPHA:
for I = K+1, min(M,K+L)
    swap ALPHA(I) and ALPHA(IWORK(I))
endfor

such that ALPHA(1) >= ALPHA(2) >= ... >= ALPHA(N).

  • INFO — INFO is INTEGER. = 0: successful exit. < 0: if INFO = -i, the i-th argument had an illegal value. > 0: if INFO = 1, the Jacobi-type procedure failed to converge. For further details, see subroutine STGSJA.

🧚 Internal Parameters 🧚

TOLA    REAL
TOLB    REAL

TOLA and TOLB are the thresholds to determine the effective rank of (AT,BT)**T. Generally, they are set to:

         TOLA = MAX(M,N)*norm(A)*MACHEPS,
         TOLB = MAX(P,N)*norm(B)*MACHEPS.

The size of TOLA and TOLB may affect the size of backward errors of the decomposition. Threshold drama, but make it numerical. 🫶

👑 Author 👑

Univ. of Tennessee

Univ. of California Berkeley

Univ. of Colorado Denver

NAG Ltd.

Contributors:

Ming Gu and Huan Ren, Computer Science Division, University of California at Berkeley, USA

Further Details:

SGGSVD3 replaces the deprecated subroutine SGGSVD. Yes — deprecated means old-school, so say it with your chest: SGGSVD is the deprecated one, and SGGSVD3 is the replacement. 💀✨

Definition at line 346 of file sggsvd3.f.

🎀 Author (the Doxygen one) 🎀

Generated automatically by Doxygen for LAPACK from the source code.

LAPACK Version 3.12.0 — footer path: /home/abui.../sggsvd3.f(3). No date line was listed, so no invented date energy. Bye bestie, go decompose something iconic 💅💖🦋

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