SGESVDQ computes the singular value decomposition (SVD) of a real


 M-by-N matrix A, where M >= N. The SVD of A is written as


                                    [++]   [xx]   [x0]   [xx]


              A = U * SIGMA * V^*,  [++] = [xx] * [ox] * [xx]


                                    [++]   [xx]


 where SIGMA is an N-by-N diagonal matrix, U is an M-by-N orthonormal


 matrix, and V is an N-by-N orthogonal matrix. The diagonal elements


 of SIGMA are the singular values of A. The columns of U and V are the


 left and the right singular vectors of A, respectively.

JOBA

  JOBA is CHARACTER*1


  Specifies the level of accuracy in the computed SVD


  = 'A' The requested accuracy corresponds to having the backward


        error bounded by || delta A ||_F <= f(m,n) * EPS * || A ||_F,


        where EPS = SLAMCH('Epsilon'). This authorises CGESVDQ to


        truncate the computed triangular factor in a rank revealing


        QR factorization whenever the truncated part is below the


        threshold of the order of EPS * ||A||_F. This is aggressive


        truncation level.


  = 'M' Similarly as with 'A', but the truncation is more gentle: it


        is allowed only when there is a drop on the diagonal of the


        triangular factor in the QR factorization. This is medium


        truncation level.


  = 'H' High accuracy requested. No numerical rank determination based


        on the rank revealing QR factorization is attempted.


  = 'E' Same as 'H', and in addition the condition number of column


        scaled A is estimated and returned in  RWORK(1).


        N^(-1/4)*RWORK(1) <= ||pinv(A_scaled)||_2 <= N^(1/4)*RWORK(1)

JOBP

  JOBP is CHARACTER*1


  = 'P' The rows of A are ordered in decreasing order with respect to


        ||A(i,:)||_\infty. This enhances numerical accuracy at the cost


        of extra data movement. Recommended for numerical robustness.


  = 'N' No row pivoting.

JOBR

          JOBR is CHARACTER*1


          = 'T' After the initial pivoted QR factorization, SGESVD is applied to


          the transposed R**T of the computed triangular factor R. This involves


          some extra data movement (matrix transpositions). Useful for


          experiments, research and development.


          = 'N' The triangular factor R is given as input to SGESVD. This may be


          preferred as it involves less data movement.

JOBU

          JOBU is CHARACTER*1


          = 'A' All M left singular vectors are computed and returned in the


          matrix U. See the description of U.


          = 'S' or 'U' N = min(M,N) left singular vectors are computed and returned


          in the matrix U. See the description of U.


          = 'R' Numerical rank NUMRANK is determined and only NUMRANK left singular


          vectors are computed and returned in the matrix U.


          = 'F' The N left singular vectors are returned in factored form as the


          product of the Q factor from the initial QR factorization and the


          N left singular vectors of (R**T , 0)**T. If row pivoting is used,


          then the necessary information on the row pivoting is stored in


          IWORK(N+1:N+M-1).


          = 'N' The left singular vectors are not computed.

JOBV

          JOBV is CHARACTER*1


          = 'A', 'V' All N right singular vectors are computed and returned in


          the matrix V.


          = 'R' Numerical rank NUMRANK is determined and only NUMRANK right singular


          vectors are computed and returned in the matrix V. This option is


          allowed only if JOBU = 'R' or JOBU = 'N'; otherwise it is illegal.


          = 'N' The right singular vectors are not computed.

M

          M is INTEGER


          The number of rows of the input matrix A.  M >= 0.

N

          N is INTEGER


          The number of columns of the input matrix A.  M >= N >= 0.

A

          A is REAL array of dimensions LDA x N


          On entry, the input matrix A.


          On exit, if JOBU .NE. 'N' or JOBV .NE. 'N', the lower triangle of A contains


          the Householder vectors as stored by SGEQP3. If JOBU = 'F', these Householder


          vectors together with WORK(1:N) can be used to restore the Q factors from


          the initial pivoted QR factorization of A. See the description of U.

LDA

          LDA is INTEGER.


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

S

          S is REAL array of dimension N.


          The singular values of A, ordered so that S(i) >= S(i+1).

U

          U is REAL array, dimension


          LDU x M if JOBU = 'A'; see the description of LDU. In this case,


          on exit, U contains the M left singular vectors.


          LDU x N if JOBU = 'S', 'U', 'R' ; see the description of LDU. In this


          case, U contains the leading N or the leading NUMRANK left singular vectors.


          LDU x N if JOBU = 'F' ; see the description of LDU. In this case U


          contains N x N orthogonal matrix that can be used to form the left


          singular vectors.


          If JOBU = 'N', U is not referenced.

LDU

          LDU is INTEGER.


          The leading dimension of the array U.


          If JOBU = 'A', 'S', 'U', 'R',  LDU >= max(1,M).


          If JOBU = 'F',                 LDU >= max(1,N).


          Otherwise,                     LDU >= 1.

V

          V is REAL array, dimension


          LDV x N if JOBV = 'A', 'V', 'R' or if JOBA = 'E' .


          If JOBV = 'A', or 'V',  V contains the N-by-N orthogonal matrix  V**T;


          If JOBV = 'R', V contains the first NUMRANK rows of V**T (the right


          singular vectors, stored rowwise, of the NUMRANK largest singular values).


          If JOBV = 'N' and JOBA = 'E', V is used as a workspace.


          If JOBV = 'N', and JOBA.NE.'E', V is not referenced.

LDV

          LDV is INTEGER


          The leading dimension of the array V.


          If JOBV = 'A', 'V', 'R',  or JOBA = 'E', LDV >= max(1,N).


          Otherwise,                               LDV >= 1.

NUMRANK

          NUMRANK is INTEGER


          NUMRANK is the numerical rank first determined after the rank


          revealing QR factorization, following the strategy specified by the


          value of JOBA. If JOBV = 'R' and JOBU = 'R', only NUMRANK


          leading singular values and vectors are then requested in the call


          of SGESVD. The final value of NUMRANK might be further reduced if


          some singular values are computed as zeros.

IWORK

          IWORK is INTEGER array, dimension (max(1, LIWORK)).


          On exit, IWORK(1:N) contains column pivoting permutation of the


          rank revealing QR factorization.


          If JOBP = 'P', IWORK(N+1:N+M-1) contains the indices of the sequence


          of row swaps used in row pivoting. These can be used to restore the


          left singular vectors in the case JOBU = 'F'.


          If LIWORK, LWORK, or LRWORK = -1, then on exit, if INFO = 0,


          IWORK(1) returns the minimal LIWORK.

LIWORK

          LIWORK is INTEGER


          The dimension of the array IWORK.


          LIWORK >= N + M - 1,     if JOBP = 'P' and JOBA .NE. 'E';


          LIWORK >= N              if JOBP = 'N' and JOBA .NE. 'E';


          LIWORK >= N + M - 1 + N, if JOBP = 'P' and JOBA = 'E';


          LIWORK >= N + N          if JOBP = 'N' and JOBA = 'E'.


          If LIWORK = -1, then a workspace query is assumed; the routine


          only calculates and returns the optimal and minimal sizes


          for the WORK, IWORK, and RWORK arrays, and no error


          message related to LWORK is issued by XERBLA.

WORK

          WORK is REAL array, dimension (max(2, LWORK)), used as a workspace.


          On exit, if, on entry, LWORK.NE.-1, WORK(1:N) contains parameters


          needed to recover the Q factor from the QR factorization computed by


          SGEQP3.


          If LIWORK, LWORK, or LRWORK = -1, then on exit, if INFO = 0,


          WORK(1) returns the optimal LWORK, and


          WORK(2) returns the minimal LWORK.

LWORK

          LWORK is INTEGER


          The dimension of the array WORK. It is determined as follows:


          Let  LWQP3 = 3*N+1,  LWCON = 3*N, and let


          LWORQ = { MAX( N, 1 ),  if JOBU = 'R', 'S', or 'U'


                  { MAX( M, 1 ),  if JOBU = 'A'


          LWSVD = MAX( 5*N, 1 )


          LWLQF = MAX( N/2, 1 ), LWSVD2 = MAX( 5*(N/2), 1 ), LWORLQ = MAX( N, 1 ),


          LWQRF = MAX( N/2, 1 ), LWORQ2 = MAX( N, 1 )


          Then the minimal value of LWORK is:


          = MAX( N + LWQP3, LWSVD )        if only the singular values are needed;


          = MAX( N + LWQP3, LWCON, LWSVD ) if only the singular values are needed,


                                   and a scaled condition estimate requested;


          = N + MAX( LWQP3, LWSVD, LWORQ ) if the singular values and the left


                                   singular vectors are requested;


          = N + MAX( LWQP3, LWCON, LWSVD, LWORQ ) if the singular values and the left


                                   singular vectors are requested, and also


                                   a scaled condition estimate requested;


          = N + MAX( LWQP3, LWSVD )        if the singular values and the right


                                   singular vectors are requested;


          = N + MAX( LWQP3, LWCON, LWSVD ) if the singular values and the right


                                   singular vectors are requested, and also


                                   a scaled condition etimate requested;


          = N + MAX( LWQP3, LWSVD, LWORQ ) if the full SVD is requested with JOBV = 'R';


                                   independent of JOBR;


          = N + MAX( LWQP3, LWCON, LWSVD, LWORQ ) if the full SVD is requested,


                                   JOBV = 'R' and, also a scaled condition


                                   estimate requested; independent of JOBR;


          = MAX( N + MAX( LWQP3, LWSVD, LWORQ ),


         N + MAX( LWQP3, N/2+LWLQF, N/2+LWSVD2, N/2+LWORLQ, LWORQ) ) if the


                         full SVD is requested with JOBV = 'A' or 'V', and


                         JOBR ='N'


          = MAX( N + MAX( LWQP3, LWCON, LWSVD, LWORQ ),


         N + MAX( LWQP3, LWCON, N/2+LWLQF, N/2+LWSVD2, N/2+LWORLQ, LWORQ ) )


                         if the full SVD is requested with JOBV = 'A' or 'V', and


                         JOBR ='N', and also a scaled condition number estimate


                         requested.


          = MAX( N + MAX( LWQP3, LWSVD, LWORQ ),


         N + MAX( LWQP3, N/2+LWQRF, N/2+LWSVD2, N/2+LWORQ2, LWORQ ) ) if the


                         full SVD is requested with JOBV = 'A', 'V', and JOBR ='T'


          = MAX( N + MAX( LWQP3, LWCON, LWSVD, LWORQ ),


         N + MAX( LWQP3, LWCON, N/2+LWQRF, N/2+LWSVD2, N/2+LWORQ2, LWORQ ) )


                         if the full SVD is requested with JOBV = 'A' or 'V', and


                         JOBR ='T', and also a scaled condition number estimate


                         requested.


          Finally, LWORK must be at least two: LWORK = MAX( 2, LWORK ).


          If LWORK = -1, then a workspace query is assumed; the routine


          only calculates and returns the optimal and minimal sizes


          for the WORK, IWORK, and RWORK arrays, and no error


          message related to LWORK is issued by XERBLA.

RWORK

          RWORK is REAL array, dimension (max(1, LRWORK)).


          On exit,


          1. If JOBA = 'E', RWORK(1) contains an estimate of the condition


          number of column scaled A. If A = C * D where D is diagonal and C


          has unit columns in the Euclidean norm, then, assuming full column rank,


          N^(-1/4) * RWORK(1) <= ||pinv(C)||_2 <= N^(1/4) * RWORK(1).


          Otherwise, RWORK(1) = -1.


          2. RWORK(2) contains the number of singular values computed as


          exact zeros in SGESVD applied to the upper triangular or trapezoidal


          R (from the initial QR factorization). In case of early exit (no call to


          SGESVD, such as in the case of zero matrix) RWORK(2) = -1.


          If LIWORK, LWORK, or LRWORK = -1, then on exit, if INFO = 0,


          RWORK(1) returns the minimal LRWORK.

LRWORK

          LRWORK is INTEGER.


          The dimension of the array RWORK.


          If JOBP ='P', then LRWORK >= MAX(2, M).


          Otherwise, LRWORK >= 2


          If LRWORK = -1, then a workspace query is assumed; the routine


          only calculates and returns the optimal and minimal sizes


          for the WORK, IWORK, and RWORK arrays, and no error


          message related to LWORK is issued by XERBLA.

INFO

          INFO is INTEGER


          = 0:  successful exit.


          < 0:  if INFO = -i, the i-th argument had an illegal value.


          > 0:  if SBDSQR did not converge, INFO specifies how many superdiagonals


          of an intermediate bidiagonal form B (computed in SGESVD) did not


          converge to zero.

   1. The data movement (matrix transpose) is coded using simple nested


   DO-loops because BLAS and LAPACK do not provide corresponding subroutines.


   Those DO-loops are easily identified in this source code - by the CONTINUE


   statements labeled with 11**. In an optimized version of this code, the


   nested DO loops should be replaced with calls to an optimized subroutine.


   2. This code scales A by 1/SQRT(M) if the largest ABS(A(i,j)) could cause


   column norm overflow. This is the minial precaution and it is left to the


   SVD routine (CGESVD) to do its own preemptive scaling if potential over-


   or underflows are detected. To avoid repeated scanning of the array A,


   an optimal implementation would do all necessary scaling before calling


   CGESVD and the scaling in CGESVD can be switched off.


   3. Other comments related to code optimization are given in comments in the


   code, enclosed in [[double brackets]].

  Please report all bugs and send interesting examples and/or comments to


  drmac@math.hr. Thank you.

  [1] Zlatko Drmac, Algorithm 977: A QR-Preconditioned QR SVD Method for


      Computing the SVD with High Accuracy. ACM Trans. Math. Softw.


      44(1): 11:1-11:30 (2017)


  SIGMA library, xGESVDQ section updated February 2016.


  Developed and coded by Zlatko Drmac, Department of Mathematics


  University of Zagreb, Croatia, drmac@math.hr

 Developed and coded by Zlatko Drmac, Department of Mathematics


  University of Zagreb, Croatia, drmac@math.hr

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