[] add metering mode to CLI

1 files changed, 2210 insertions, 0 deletions

diff --git a/contrib/libs/clapack/sgejsv.c b/contrib/libs/clapack/sgejsv.c
new file mode 100644
index 0000000000..57fe14814d
--- /dev/null
+++ b/contrib/libs/clapack/sgejsv.c
@@ -0,0 +1,2210 @@
+/* sgejsv.f -- translated by f2c (version 20061008).
+   You must link the resulting object file with libf2c:
+	on Microsoft Windows system, link with libf2c.lib;
+	on Linux or Unix systems, link with .../path/to/libf2c.a -lm
+	or, if you install libf2c.a in a standard place, with -lf2c -lm
+	-- in that order, at the end of the command line, as in
+		cc *.o -lf2c -lm
+	Source for libf2c is in /netlib/f2c/libf2c.zip, e.g.,
+
+		http://www.netlib.org/f2c/libf2c.zip
+*/
+
+#include "f2c.h"
+#include "blaswrap.h"
+
+/* Table of constant values */
+
+static integer c__1 = 1;
+static real c_b34 = 0.f;
+static real c_b35 = 1.f;
+static integer c__0 = 0;
+static integer c_n1 = -1;
+
+/* Subroutine */ int sgejsv_(char *joba, char *jobu, char *jobv, char *jobr, 
+	char *jobt, char *jobp, integer *m, integer *n, real *a, integer *lda, 
+	 real *sva, real *u, integer *ldu, real *v, integer *ldv, real *work, 
+	integer *lwork, integer *iwork, integer *info)
+{
+    /* System generated locals */
+    integer a_dim1, a_offset, u_dim1, u_offset, v_dim1, v_offset, i__1, i__2, 
+	    i__3, i__4, i__5, i__6, i__7, i__8, i__9, i__10;
+    real r__1, r__2, r__3, r__4;
+
+    /* Builtin functions */
+    double sqrt(doublereal), log(doublereal), r_sign(real *, real *);
+    integer i_nint(real *);
+
+    /* Local variables */
+    integer p, q, n1, nr;
+    real big, xsc, big1;
+    logical defr;
+    real aapp, aaqq;
+    logical kill;
+    integer ierr;
+    real temp1;
+    extern doublereal snrm2_(integer *, real *, integer *);
+    logical jracc;
+    extern logical lsame_(char *, char *);
+    extern /* Subroutine */ int sscal_(integer *, real *, real *, integer *);
+    real small, entra, sfmin;
+    logical lsvec;
+    real epsln;
+    logical rsvec;
+    extern /* Subroutine */ int scopy_(integer *, real *, integer *, real *, 
+	    integer *), sswap_(integer *, real *, integer *, real *, integer *
+);
+    logical l2aber;
+    extern /* Subroutine */ int strsm_(char *, char *, char *, char *, 
+	    integer *, integer *, real *, real *, integer *, real *, integer *
+);
+    real condr1, condr2, uscal1, uscal2;
+    logical l2kill, l2rank, l2tran;
+    extern /* Subroutine */ int sgeqp3_(integer *, integer *, real *, integer 
+	    *, integer *, real *, real *, integer *, integer *);
+    logical l2pert;
+    real scalem, sconda;
+    logical goscal;
+    real aatmin;
+    extern doublereal slamch_(char *);
+    real aatmax;
+    extern /* Subroutine */ int xerbla_(char *, integer *);
+    logical noscal;
+    extern /* Subroutine */ int sgelqf_(integer *, integer *, real *, integer 
+	    *, real *, real *, integer *, integer *);
+    extern integer isamax_(integer *, real *, integer *);
+    extern /* Subroutine */ int slascl_(char *, integer *, integer *, real *, 
+	    real *, integer *, integer *, real *, integer *, integer *), sgeqrf_(integer *, integer *, real *, integer *, real *, 
+	    real *, integer *, integer *), slacpy_(char *, integer *, integer 
+	    *, real *, integer *, real *, integer *), slaset_(char *, 
+	    integer *, integer *, real *, real *, real *, integer *);
+    real entrat;
+    logical almort;
+    real maxprj;
+    extern /* Subroutine */ int spocon_(char *, integer *, real *, integer *, 
+	    real *, real *, real *, integer *, integer *);
+    logical errest;
+    extern /* Subroutine */ int sgesvj_(char *, char *, char *, integer *, 
+	    integer *, real *, integer *, real *, integer *, real *, integer *
+, real *, integer *, integer *), slassq_(
+	    integer *, real *, integer *, real *, real *);
+    logical transp;
+    extern /* Subroutine */ int slaswp_(integer *, real *, integer *, integer 
+	    *, integer *, integer *, integer *), sorgqr_(integer *, integer *, 
+	     integer *, real *, integer *, real *, real *, integer *, integer 
+	    *), sormlq_(char *, char *, integer *, integer *, integer *, real 
+	    *, integer *, real *, real *, integer *, real *, integer *, 
+	    integer *), sormqr_(char *, char *, integer *, 
+	    integer *, integer *, real *, integer *, real *, real *, integer *
+, real *, integer *, integer *);
+    logical rowpiv;
+    real cond_ok__;
+    integer warning, numrank;
+
+
+/*  -- LAPACK routine (version 3.2)                                    -- */
+
+/*  -- Contributed by Zlatko Drmac of the University of Zagreb and     -- */
+/*  -- Kresimir Veselic of the Fernuniversitaet Hagen                  -- */
+/*  -- November 2008                                                   -- */
+
+/*  -- LAPACK is a software package provided by Univ. of Tennessee,    -- */
+/*  -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- */
+
+/* This routine is also part of SIGMA (version 1.23, October 23. 2008.) */
+/* SIGMA is a library of algorithms for highly accurate algorithms for */
+/* computation of SVD, PSVD, QSVD, (H,K)-SVD, and for solution of the */
+/* eigenvalue problems Hx = lambda M x, H M x = lambda x with H, M > 0. */
+
+/*     -#- Scalar Arguments -#- */
+
+
+/*     -#- Array Arguments -#- */
+
+/*     .. */
+
+/*  Purpose */
+/*  ~~~~~~~ */
+/*  SGEJSV computes the singular value decomposition (SVD) of a real M-by-N */
+/*  matrix [A], where M >= N. The SVD of [A] is written as */
+
+/*               [A] = [U] * [SIGMA] * [V]^t, */
+
+/*  where [SIGMA] is an N-by-N (M-by-N) matrix which is zero except for its N */
+/*  diagonal elements, [U] is an M-by-N (or M-by-M) 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. The matrices [U] and [V] */
+/*  are computed and stored in the arrays U and V, respectively. The diagonal */
+/*  of [SIGMA] is computed and stored in the array SVA. */
+
+/*  Further details */
+/*  ~~~~~~~~~~~~~~~ */
+/*  SGEJSV implements a preconditioned Jacobi SVD algorithm. It uses SGEQP3, */
+/*  SGEQRF, and SGELQF as preprocessors and preconditioners. Optionally, an */
+/*  additional row pivoting can be used as a preprocessor, which in some */
+/*  cases results in much higher accuracy. An example is matrix A with the */
+/*  structure A = D1 * C * D2, where D1, D2 are arbitrarily ill-conditioned */
+/*  diagonal matrices and C is well-conditioned matrix. In that case, complete */
+/*  pivoting in the first QR factorizations provides accuracy dependent on the */
+/*  condition number of C, and independent of D1, D2. Such higher accuracy is */
+/*  not completely understood theoretically, but it works well in practice. */
+/*  Further, if A can be written as A = B*D, with well-conditioned B and some */
+/*  diagonal D, then the high accuracy is guaranteed, both theoretically and */
+/*  in software, independent of D. For more details see [1], [2]. */
+/*     The computational range for the singular values can be the full range */
+/*  ( UNDERFLOW,OVERFLOW ), provided that the machine arithmetic and the BLAS */
+/*  & LAPACK routines called by SGEJSV are implemented to work in that range. */
+/*  If that is not the case, then the restriction for safe computation with */
+/*  the singular values in the range of normalized IEEE numbers is that the */
+/*  spectral condition number kappa(A)=sigma_max(A)/sigma_min(A) does not */
+/*  overflow. This code (SGEJSV) is best used in this restricted range, */
+/*  meaning that singular values of magnitude below ||A||_2 / SLAMCH('O') are */
+/*  returned as zeros. See JOBR for details on this. */
+/*     Further, this implementation is somewhat slower than the one described */
+/*  in [1,2] due to replacement of some non-LAPACK components, and because */
+/*  the choice of some tuning parameters in the iterative part (SGESVJ) is */
+/*  left to the implementer on a particular machine. */
+/*     The rank revealing QR factorization (in this code: SGEQP3) should be */
+/*  implemented as in [3]. We have a new version of SGEQP3 under development */
+/*  that is more robust than the current one in LAPACK, with a cleaner cut in */
+/*  rank defficient cases. It will be available in the SIGMA library [4]. */
+/*  If M is much larger than N, it is obvious that the inital QRF with */
+/*  column pivoting can be preprocessed by the QRF without pivoting. That */
+/*  well known trick is not used in SGEJSV because in some cases heavy row */
+/*  weighting can be treated with complete pivoting. The overhead in cases */
+/*  M much larger than N is then only due to pivoting, but the benefits in */
+/*  terms of accuracy have prevailed. The implementer/user can incorporate */
+/*  this extra QRF step easily. The implementer can also improve data movement */
+/*  (matrix transpose, matrix copy, matrix transposed copy) - this */
+/*  implementation of SGEJSV uses only the simplest, naive data movement. */
+
+/*  Contributors */
+/*  ~~~~~~~~~~~~ */
+/*  Zlatko Drmac (Zagreb, Croatia) and Kresimir Veselic (Hagen, Germany) */
+
+/*  References */
+/*  ~~~~~~~~~~ */
+/* [1] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm I. */
+/*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1322-1342. */
+/*     LAPACK Working note 169. */
+/* [2] Z. Drmac and K. Veselic: New fast and accurate Jacobi SVD algorithm II. */
+/*     SIAM J. Matrix Anal. Appl. Vol. 35, No. 2 (2008), pp. 1343-1362. */
+/*     LAPACK Working note 170. */
+/* [3] Z. Drmac and Z. Bujanovic: On the failure of rank-revealing QR */
+/*     factorization software - a case study. */
+/*     ACM Trans. math. Softw. Vol. 35, No 2 (2008), pp. 1-28. */
+/*     LAPACK Working note 176. */
+/* [4] Z. Drmac: SIGMA - mathematical software library for accurate SVD, PSV, */
+/*     QSVD, (H,K)-SVD computations. */
+/*     Department of Mathematics, University of Zagreb, 2008. */
+
+/*  Bugs, examples and comments */
+/*  ~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
+/*  Please report all bugs and send interesting examples and/or comments to */
+/*  drmac@math.hr. Thank you. */
+
+/*  Arguments */
+/*  ~~~~~~~~~ */
+/* ............................................................................ */
+/* . JOBA   (input) CHARACTER*1 */
+/* .        Specifies the level of accuracy: */
+/* .      = 'C': This option works well (high relative accuracy) if A = B * D, */
+/* .             with well-conditioned B and arbitrary diagonal matrix D. */
+/* .             The accuracy cannot be spoiled by COLUMN scaling. The */
+/* .             accuracy of the computed output depends on the condition of */
+/* .             B, and the procedure aims at the best theoretical accuracy. */
+/* .             The relative error max_{i=1:N}|d sigma_i| / sigma_i is */
+/* .             bounded by f(M,N)*epsilon* cond(B), independent of D. */
+/* .             The input matrix is preprocessed with the QRF with column */
+/* .             pivoting. This initial preprocessing and preconditioning by */
+/* .             a rank revealing QR factorization is common for all values of */
+/* .             JOBA. Additional actions are specified as follows: */
+/* .      = 'E': Computation as with 'C' with an additional estimate of the */
+/* .             condition number of B. It provides a realistic error bound. */
+/* .      = 'F': If A = D1 * C * D2 with ill-conditioned diagonal scalings */
+/* .             D1, D2, and well-conditioned matrix C, this option gives */
+/* .             higher accuracy than the 'C' option. If the structure of the */
+/* .             input matrix is not known, and relative accuracy is */
+/* .             desirable, then this option is advisable. The input matrix A */
+/* .             is preprocessed with QR factorization with FULL (row and */
+/* .             column) pivoting. */
+/* .      = 'G'  Computation as with 'F' with an additional estimate of the */
+/* .             condition number of B, where A=D*B. If A has heavily weighted */
+/* .             rows, then using this condition number gives too pessimistic */
+/* .             error bound. */
+/* .      = 'A': Small singular values are the noise and the matrix is treated */
+/* .             as numerically rank defficient. The error in the computed */
+/* .             singular values is bounded by f(m,n)*epsilon*||A||. */
+/* .             The computed SVD A = U * S * V^t restores A up to */
+/* .             f(m,n)*epsilon*||A||. */
+/* .             This gives the procedure the licence to discard (set to zero) */
+/* .             all singular values below N*epsilon*||A||. */
+/* .      = 'R': Similar as in 'A'. Rank revealing property of the initial */
+/* .             QR factorization is used do reveal (using triangular factor) */
+/* .             a gap sigma_{r+1} < epsilon * sigma_r in which case the */
+/* .             numerical RANK is declared to be r. The SVD is computed with */
+/* .             absolute error bounds, but more accurately than with 'A'. */
+/* . */
+/* . JOBU   (input) CHARACTER*1 */
+/* .        Specifies whether to compute the columns of U: */
+/* .      = 'U': N columns of U are returned in the array U. */
+/* .      = 'F': full set of M left sing. vectors is returned in the array U. */
+/* .      = 'W': U may be used as workspace of length M*N. See the description */
+/* .             of U. */
+/* .      = 'N': U is not computed. */
+/* . */
+/* . JOBV   (input) CHARACTER*1 */
+/* .        Specifies whether to compute the matrix V: */
+/* .      = 'V': N columns of V are returned in the array V; Jacobi rotations */
+/* .             are not explicitly accumulated. */
+/* .      = 'J': N columns of V are returned in the array V, but they are */
+/* .             computed as the product of Jacobi rotations. This option is */
+/* .             allowed only if JOBU .NE. 'N', i.e. in computing the full SVD. */
+/* .      = 'W': V may be used as workspace of length N*N. See the description */
+/* .             of V. */
+/* .      = 'N': V is not computed. */
+/* . */
+/* . JOBR   (input) CHARACTER*1 */
+/* .        Specifies the RANGE for the singular values. Issues the licence to */
+/* .        set to zero small positive singular values if they are outside */
+/* .        specified range. If A .NE. 0 is scaled so that the largest singular */
+/* .        value of c*A is around SQRT(BIG), BIG=SLAMCH('O'), then JOBR issues */
+/* .        the licence to kill columns of A whose norm in c*A is less than */
+/* .        SQRT(SFMIN) (for JOBR.EQ.'R'), or less than SMALL=SFMIN/EPSLN, */
+/* .        where SFMIN=SLAMCH('S'), EPSLN=SLAMCH('E'). */
+/* .      = 'N': Do not kill small columns of c*A. This option assumes that */
+/* .             BLAS and QR factorizations and triangular solvers are */
+/* .             implemented to work in that range. If the condition of A */
+/* .             is greater than BIG, use SGESVJ. */
+/* .      = 'R': RESTRICTED range for sigma(c*A) is [SQRT(SFMIN), SQRT(BIG)] */
+/* .             (roughly, as described above). This option is recommended. */
+/* .                                            ~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
+/* .        For computing the singular values in the FULL range [SFMIN,BIG] */
+/* .        use SGESVJ. */
+/* . */
+/* . JOBT   (input) CHARACTER*1 */
+/* .        If the matrix is square then the procedure may determine to use */
+/* .        transposed A if A^t seems to be better with respect to convergence. */
+/* .        If the matrix is not square, JOBT is ignored. This is subject to */
+/* .        changes in the future. */
+/* .        The decision is based on two values of entropy over the adjoint */
+/* .        orbit of A^t * A. See the descriptions of WORK(6) and WORK(7). */
+/* .      = 'T': transpose if entropy test indicates possibly faster */
+/* .        convergence of Jacobi process if A^t is taken as input. If A is */
+/* .        replaced with A^t, then the row pivoting is included automatically. */
+/* .      = 'N': do not speculate. */
+/* .        This option can be used to compute only the singular values, or the */
+/* .        full SVD (U, SIGMA and V). For only one set of singular vectors */
+/* .        (U or V), the caller should provide both U and V, as one of the */
+/* .        matrices is used as workspace if the matrix A is transposed. */
+/* .        The implementer can easily remove this constraint and make the */
+/* .        code more complicated. See the descriptions of U and V. */
+/* . */
+/* . JOBP   (input) CHARACTER*1 */
+/* .        Issues the licence to introduce structured perturbations to drown */
+/* .        denormalized numbers. This licence should be active if the */
+/* .        denormals are poorly implemented, causing slow computation, */
+/* .        especially in cases of fast convergence (!). For details see [1,2]. */
+/* .        For the sake of simplicity, this perturbations are included only */
+/* .        when the full SVD or only the singular values are requested. The */
+/* .        implementer/user can easily add the perturbation for the cases of */
+/* .        computing one set of singular vectors. */
+/* .      = 'P': introduce perturbation */
+/* .      = 'N': do not perturb */
+/* ............................................................................ */
+
+/*  M      (input) INTEGER */
+/*         The number of rows of the input matrix A.  M >= 0. */
+
+/*  N      (input) INTEGER */
+/*         The number of columns of the input matrix A. M >= N >= 0. */
+
+/*  A       (input/workspace) REAL array, dimension (LDA,N) */
+/*          On entry, the M-by-N matrix A. */
+
+/*  LDA     (input) INTEGER */
+/*          The leading dimension of the array A.  LDA >= max(1,M). */
+
+/*  SVA     (workspace/output) REAL array, dimension (N) */
+/*          On exit, */
+/*          - For WORK(1)/WORK(2) = ONE: The singular values of A. During the */
+/*            computation SVA contains Euclidean column norms of the */
+/*            iterated matrices in the array A. */
+/*          - For WORK(1) .NE. WORK(2): The singular values of A are */
+/*            (WORK(1)/WORK(2)) * SVA(1:N). This factored form is used if */
+/*            sigma_max(A) overflows or if small singular values have been */
+/*            saved from underflow by scaling the input matrix A. */
+/*          - If JOBR='R' then some of the singular values may be returned */
+/*            as exact zeros obtained by "set to zero" because they are */
+/*            below the numerical rank threshold or are denormalized numbers. */
+
+/*  U       (workspace/output) REAL array, dimension ( LDU, N ) */
+/*          If JOBU = 'U', then U contains on exit the M-by-N matrix of */
+/*                         the left singular vectors. */
+/*          If JOBU = 'F', then U contains on exit the M-by-M matrix of */
+/*                         the left singular vectors, including an ONB */
+/*                         of the orthogonal complement of the Range(A). */
+/*          If JOBU = 'W'  .AND. (JOBV.EQ.'V' .AND. JOBT.EQ.'T' .AND. M.EQ.N), */
+/*                         then U is used as workspace if the procedure */
+/*                         replaces A with A^t. In that case, [V] is computed */
+/*                         in U as left singular vectors of A^t and then */
+/*                         copied back to the V array. This 'W' option is just */
+/*                         a reminder to the caller that in this case U is */
+/*                         reserved as workspace of length N*N. */
+/*          If JOBU = 'N'  U is not referenced. */
+
+/* LDU      (input) INTEGER */
+/*          The leading dimension of the array U,  LDU >= 1. */
+/*          IF  JOBU = 'U' or 'F' or 'W',  then LDU >= M. */
+
+/*  V       (workspace/output) REAL array, dimension ( LDV, N ) */
+/*          If JOBV = 'V', 'J' then V contains on exit the N-by-N matrix of */
+/*                         the right singular vectors; */
+/*          If JOBV = 'W', AND (JOBU.EQ.'U' AND JOBT.EQ.'T' AND M.EQ.N), */
+/*                         then V is used as workspace if the pprocedure */
+/*                         replaces A with A^t. In that case, [U] is computed */
+/*                         in V as right singular vectors of A^t and then */
+/*                         copied back to the U array. This 'W' option is just */
+/*                         a reminder to the caller that in this case V is */
+/*                         reserved as workspace of length N*N. */
+/*          If JOBV = 'N'  V is not referenced. */
+
+/*  LDV     (input) INTEGER */
+/*          The leading dimension of the array V,  LDV >= 1. */
+/*          If JOBV = 'V' or 'J' or 'W', then LDV >= N. */
+
+/*  WORK    (workspace/output) REAL array, dimension at least LWORK. */
+/*          On exit, */
+/*          WORK(1) = SCALE = WORK(2) / WORK(1) is the scaling factor such */
+/*                    that SCALE*SVA(1:N) are the computed singular values */
+/*                    of A. (See the description of SVA().) */
+/*          WORK(2) = See the description of WORK(1). */
+/*          WORK(3) = SCONDA is an estimate for the condition number of */
+/*                    column equilibrated A. (If JOBA .EQ. 'E' or 'G') */
+/*                    SCONDA is an estimate of SQRT(||(R^t * R)^(-1)||_1). */
+/*                    It is computed using SPOCON. It holds */
+/*                    N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA */
+/*                    where R is the triangular factor from the QRF of A. */
+/*                    However, if R is truncated and the numerical rank is */
+/*                    determined to be strictly smaller than N, SCONDA is */
+/*                    returned as -1, thus indicating that the smallest */
+/*                    singular values might be lost. */
+
+/*          If full SVD is needed, the following two condition numbers are */
+/*          useful for the analysis of the algorithm. They are provied for */
+/*          a developer/implementer who is familiar with the details of */
+/*          the method. */
+
+/*          WORK(4) = an estimate of the scaled condition number of the */
+/*                    triangular factor in the first QR factorization. */
+/*          WORK(5) = an estimate of the scaled condition number of the */
+/*                    triangular factor in the second QR factorization. */
+/*          The following two parameters are computed if JOBT .EQ. 'T'. */
+/*          They are provided for a developer/implementer who is familiar */
+/*          with the details of the method. */
+
+/*          WORK(6) = the entropy of A^t*A :: this is the Shannon entropy */
+/*                    of diag(A^t*A) / Trace(A^t*A) taken as point in the */
+/*                    probability simplex. */
+/*          WORK(7) = the entropy of A*A^t. */
+
+/*  LWORK   (input) INTEGER */
+/*          Length of WORK to confirm proper allocation of work space. */
+/*          LWORK depends on the job: */
+
+/*          If only SIGMA is needed ( JOBU.EQ.'N', JOBV.EQ.'N' ) and */
+/*            -> .. no scaled condition estimate required ( JOBE.EQ.'N'): */
+/*               LWORK >= max(2*M+N,4*N+1,7). This is the minimal requirement. */
+/*               For optimal performance (blocked code) the optimal value */
+/*               is LWORK >= max(2*M+N,3*N+(N+1)*NB,7). Here NB is the optimal */
+/*               block size for xGEQP3/xGEQRF. */
+/*            -> .. an estimate of the scaled condition number of A is */
+/*               required (JOBA='E', 'G'). In this case, LWORK is the maximum */
+/*               of the above and N*N+4*N, i.e. LWORK >= max(2*M+N,N*N+4N,7). */
+
+/*          If SIGMA and the right singular vectors are needed (JOBV.EQ.'V'), */
+/*            -> the minimal requirement is LWORK >= max(2*N+M,7). */
+/*            -> For optimal performance, LWORK >= max(2*N+M,2*N+N*NB,7), */
+/*               where NB is the optimal block size. */
+
+/*          If SIGMA and the left singular vectors are needed */
+/*            -> the minimal requirement is LWORK >= max(2*N+M,7). */
+/*            -> For optimal performance, LWORK >= max(2*N+M,2*N+N*NB,7), */
+/*               where NB is the optimal block size. */
+
+/*          If full SVD is needed ( JOBU.EQ.'U' or 'F', JOBV.EQ.'V' ) and */
+/*            -> .. the singular vectors are computed without explicit */
+/*               accumulation of the Jacobi rotations, LWORK >= 6*N+2*N*N */
+/*            -> .. in the iterative part, the Jacobi rotations are */
+/*               explicitly accumulated (option, see the description of JOBV), */
+/*               then the minimal requirement is LWORK >= max(M+3*N+N*N,7). */
+/*               For better performance, if NB is the optimal block size, */
+/*               LWORK >= max(3*N+N*N+M,3*N+N*N+N*NB,7). */
+
+/*  IWORK   (workspace/output) INTEGER array, dimension M+3*N. */
+/*          On exit, */
+/*          IWORK(1) = the numerical rank determined after the initial */
+/*                     QR factorization with pivoting. See the descriptions */
+/*                     of JOBA and JOBR. */
+/*          IWORK(2) = the number of the computed nonzero singular values */
+/*          IWORK(3) = if nonzero, a warning message: */
+/*                     If IWORK(3).EQ.1 then some of the column norms of A */
+/*                     were denormalized floats. The requested high accuracy */
+/*                     is not warranted by the data. */
+
+/*  INFO    (output) INTEGER */
+/*           < 0  : if INFO = -i, then the i-th argument had an illegal value. */
+/*           = 0 :  successfull exit; */
+/*           > 0 :  SGEJSV  did not converge in the maximal allowed number */
+/*                  of sweeps. The computed values may be inaccurate. */
+
+/* ............................................................................ */
+
+/*     Local Parameters: */
+
+
+/*     Local Scalars: */
+
+
+/*     Intrinsic Functions: */
+
+
+/*     External Functions: */
+
+
+/*     External Subroutines ( BLAS, LAPACK ): */
+
+
+
+/* ............................................................................ */
+
+/*     Test the input arguments */
+
+    /* Parameter adjustments */
+    --sva;
+    a_dim1 = *lda;
+    a_offset = 1 + a_dim1;
+    a -= a_offset;
+    u_dim1 = *ldu;
+    u_offset = 1 + u_dim1;
+    u -= u_offset;
+    v_dim1 = *ldv;
+    v_offset = 1 + v_dim1;
+    v -= v_offset;
+    --work;
+    --iwork;
+
+    /* Function Body */
+    lsvec = lsame_(jobu, "U") || lsame_(jobu, "F");
+    jracc = lsame_(jobv, "J");
+    rsvec = lsame_(jobv, "V") || jracc;
+    rowpiv = lsame_(joba, "F") || lsame_(joba, "G");
+    l2rank = lsame_(joba, "R");
+    l2aber = lsame_(joba, "A");
+    errest = lsame_(joba, "E") || lsame_(joba, "G");
+    l2tran = lsame_(jobt, "T");
+    l2kill = lsame_(jobr, "R");
+    defr = lsame_(jobr, "N");
+    l2pert = lsame_(jobp, "P");
+
+    if (! (rowpiv || l2rank || l2aber || errest || lsame_(joba, "C"))) {
+	*info = -1;
+    } else if (! (lsvec || lsame_(jobu, "N") || lsame_(
+	    jobu, "W"))) {
+	*info = -2;
+    } else if (! (rsvec || lsame_(jobv, "N") || lsame_(
+	    jobv, "W")) || jracc && ! lsvec) {
+	*info = -3;
+    } else if (! (l2kill || defr)) {
+	*info = -4;
+    } else if (! (l2tran || lsame_(jobt, "N"))) {
+	*info = -5;
+    } else if (! (l2pert || lsame_(jobp, "N"))) {
+	*info = -6;
+    } else if (*m < 0) {
+	*info = -7;
+    } else if (*n < 0 || *n > *m) {
+	*info = -8;
+    } else if (*lda < *m) {
+	*info = -10;
+    } else if (lsvec && *ldu < *m) {
+	*info = -13;
+    } else if (rsvec && *ldv < *n) {
+	*info = -14;
+    } else /* if(complicated condition) */ {
+/* Computing MAX */
+	i__1 = 7, i__2 = (*n << 2) + 1, i__1 = max(i__1,i__2), i__2 = (*m << 
+		1) + *n;
+/* Computing MAX */
+	i__3 = 7, i__4 = (*n << 2) + *n * *n, i__3 = max(i__3,i__4), i__4 = (*
+		m << 1) + *n;
+/* Computing MAX */
+	i__5 = 7, i__6 = (*n << 1) + *m;
+/* Computing MAX */
+	i__7 = 7, i__8 = (*n << 1) + *m;
+/* Computing MAX */
+	i__9 = 7, i__10 = *m + *n * 3 + *n * *n;
+	if (! (lsvec || rsvec || errest) && *lwork < max(i__1,i__2) || ! (
+		lsvec || lsvec) && errest && *lwork < max(i__3,i__4) || lsvec 
+		&& ! rsvec && *lwork < max(i__5,i__6) || rsvec && ! lsvec && *
+		lwork < max(i__7,i__8) || lsvec && rsvec && ! jracc && *lwork 
+		< *n * 6 + (*n << 1) * *n || lsvec && rsvec && jracc && *
+		lwork < max(i__9,i__10)) {
+	    *info = -17;
+	} else {
+/*        #:) */
+	    *info = 0;
+	}
+    }
+
+    if (*info != 0) {
+/*       #:( */
+	i__1 = -(*info);
+	xerbla_("SGEJSV", &i__1);
+    }
+
+/*     Quick return for void matrix (Y3K safe) */
+/* #:) */
+    if (*m == 0 || *n == 0) {
+	return 0;
+    }
+
+/*     Determine whether the matrix U should be M x N or M x M */
+
+    if (lsvec) {
+	n1 = *n;
+	if (lsame_(jobu, "F")) {
+	    n1 = *m;
+	}
+    }
+
+/*     Set numerical parameters */
+
+/* !    NOTE: Make sure SLAMCH() does not fail on the target architecture. */
+
+    epsln = slamch_("Epsilon");
+    sfmin = slamch_("SafeMinimum");
+    small = sfmin / epsln;
+    big = slamch_("O");
+
+/*     Initialize SVA(1:N) = diag( ||A e_i||_2 )_1^N */
+
+/* (!)  If necessary, scale SVA() to protect the largest norm from */
+/*     overflow. It is possible that this scaling pushes the smallest */
+/*     column norm left from the underflow threshold (extreme case). */
+
+    scalem = 1.f / sqrt((real) (*m) * (real) (*n));
+    noscal = TRUE_;
+    goscal = TRUE_;
+    i__1 = *n;
+    for (p = 1; p <= i__1; ++p) {
+	aapp = 0.f;
+	aaqq = 0.f;
+	slassq_(m, &a[p * a_dim1 + 1], &c__1, &aapp, &aaqq);
+	if (aapp > big) {
+	    *info = -9;
+	    i__2 = -(*info);
+	    xerbla_("SGEJSV", &i__2);
+	    return 0;
+	}
+	aaqq = sqrt(aaqq);
+	if (aapp < big / aaqq && noscal) {
+	    sva[p] = aapp * aaqq;
+	} else {
+	    noscal = FALSE_;
+	    sva[p] = aapp * (aaqq * scalem);
+	    if (goscal) {
+		goscal = FALSE_;
+		i__2 = p - 1;
+		sscal_(&i__2, &scalem, &sva[1], &c__1);
+	    }
+	}
+/* L1874: */
+    }
+
+    if (noscal) {
+	scalem = 1.f;
+    }
+
+    aapp = 0.f;
+    aaqq = big;
+    i__1 = *n;
+    for (p = 1; p <= i__1; ++p) {
+/* Computing MAX */
+	r__1 = aapp, r__2 = sva[p];
+	aapp = dmax(r__1,r__2);
+	if (sva[p] != 0.f) {
+/* Computing MIN */
+	    r__1 = aaqq, r__2 = sva[p];
+	    aaqq = dmin(r__1,r__2);
+	}
+/* L4781: */
+    }
+
+/*     Quick return for zero M x N matrix */
+/* #:) */
+    if (aapp == 0.f) {
+	if (lsvec) {
+	    slaset_("G", m, &n1, &c_b34, &c_b35, &u[u_offset], ldu)
+		    ;
+	}
+	if (rsvec) {
+	    slaset_("G", n, n, &c_b34, &c_b35, &v[v_offset], ldv);
+	}
+	work[1] = 1.f;
+	work[2] = 1.f;
+	if (errest) {
+	    work[3] = 1.f;
+	}
+	if (lsvec && rsvec) {
+	    work[4] = 1.f;
+	    work[5] = 1.f;
+	}
+	if (l2tran) {
+	    work[6] = 0.f;
+	    work[7] = 0.f;
+	}
+	iwork[1] = 0;
+	iwork[2] = 0;
+	return 0;
+    }
+
+/*     Issue warning if denormalized column norms detected. Override the */
+/*     high relative accuracy request. Issue licence to kill columns */
+/*     (set them to zero) whose norm is less than sigma_max / BIG (roughly). */
+/* #:( */
+    warning = 0;
+    if (aaqq <= sfmin) {
+	l2rank = TRUE_;
+	l2kill = TRUE_;
+	warning = 1;
+    }
+
+/*     Quick return for one-column matrix */
+/* #:) */
+    if (*n == 1) {
+
+	if (lsvec) {
+	    slascl_("G", &c__0, &c__0, &sva[1], &scalem, m, &c__1, &a[a_dim1 
+		    + 1], lda, &ierr);
+	    slacpy_("A", m, &c__1, &a[a_offset], lda, &u[u_offset], ldu);
+/*           computing all M left singular vectors of the M x 1 matrix */
+	    if (n1 != *n) {
+		i__1 = *lwork - *n;
+		sgeqrf_(m, n, &u[u_offset], ldu, &work[1], &work[*n + 1], &
+			i__1, &ierr);
+		i__1 = *lwork - *n;
+		sorgqr_(m, &n1, &c__1, &u[u_offset], ldu, &work[1], &work[*n 
+			+ 1], &i__1, &ierr);
+		scopy_(m, &a[a_dim1 + 1], &c__1, &u[u_dim1 + 1], &c__1);
+	    }
+	}
+	if (rsvec) {
+	    v[v_dim1 + 1] = 1.f;
+	}
+	if (sva[1] < big * scalem) {
+	    sva[1] /= scalem;
+	    scalem = 1.f;
+	}
+	work[1] = 1.f / scalem;
+	work[2] = 1.f;
+	if (sva[1] != 0.f) {
+	    iwork[1] = 1;
+	    if (sva[1] / scalem >= sfmin) {
+		iwork[2] = 1;
+	    } else {
+		iwork[2] = 0;
+	    }
+	} else {
+	    iwork[1] = 0;
+	    iwork[2] = 0;
+	}
+	if (errest) {
+	    work[3] = 1.f;
+	}
+	if (lsvec && rsvec) {
+	    work[4] = 1.f;
+	    work[5] = 1.f;
+	}
+	if (l2tran) {
+	    work[6] = 0.f;
+	    work[7] = 0.f;
+	}
+	return 0;
+
+    }
+
+    transp = FALSE_;
+    l2tran = l2tran && *m == *n;
+
+    aatmax = -1.f;
+    aatmin = big;
+    if (rowpiv || l2tran) {
+
+/*     Compute the row norms, needed to determine row pivoting sequence */
+/*     (in the case of heavily row weighted A, row pivoting is strongly */
+/*     advised) and to collect information needed to compare the */
+/*     structures of A * A^t and A^t * A (in the case L2TRAN.EQ..TRUE.). */
+
+	if (l2tran) {
+	    i__1 = *m;
+	    for (p = 1; p <= i__1; ++p) {
+		xsc = 0.f;
+		temp1 = 0.f;
+		slassq_(n, &a[p + a_dim1], lda, &xsc, &temp1);
+/*              SLASSQ gets both the ell_2 and the ell_infinity norm */
+/*              in one pass through the vector */
+		work[*m + *n + p] = xsc * scalem;
+		work[*n + p] = xsc * (scalem * sqrt(temp1));
+/* Computing MAX */
+		r__1 = aatmax, r__2 = work[*n + p];
+		aatmax = dmax(r__1,r__2);
+		if (work[*n + p] != 0.f) {
+/* Computing MIN */
+		    r__1 = aatmin, r__2 = work[*n + p];
+		    aatmin = dmin(r__1,r__2);
+		}
+/* L1950: */
+	    }
+	} else {
+	    i__1 = *m;
+	    for (p = 1; p <= i__1; ++p) {
+		work[*m + *n + p] = scalem * (r__1 = a[p + isamax_(n, &a[p + 
+			a_dim1], lda) * a_dim1], dabs(r__1));
+/* Computing MAX */
+		r__1 = aatmax, r__2 = work[*m + *n + p];
+		aatmax = dmax(r__1,r__2);
+/* Computing MIN */
+		r__1 = aatmin, r__2 = work[*m + *n + p];
+		aatmin = dmin(r__1,r__2);
+/* L1904: */
+	    }
+	}
+
+    }
+
+/*     For square matrix A try to determine whether A^t  would be  better */
+/*     input for the preconditioned Jacobi SVD, with faster convergence. */
+/*     The decision is based on an O(N) function of the vector of column */
+/*     and row norms of A, based on the Shannon entropy. This should give */
+/*     the right choice in most cases when the difference actually matters. */
+/*     It may fail and pick the slower converging side. */
+
+    entra = 0.f;
+    entrat = 0.f;
+    if (l2tran) {
+
+	xsc = 0.f;
+	temp1 = 0.f;
+	slassq_(n, &sva[1], &c__1, &xsc, &temp1);
+	temp1 = 1.f / temp1;
+
+	entra = 0.f;
+	i__1 = *n;
+	for (p = 1; p <= i__1; ++p) {
+/* Computing 2nd power */
+	    r__1 = sva[p] / xsc;
+	    big1 = r__1 * r__1 * temp1;
+	    if (big1 != 0.f) {
+		entra += big1 * log(big1);
+	    }
+/* L1113: */
+	}
+	entra = -entra / log((real) (*n));
+
+/*        Now, SVA().^2/Trace(A^t * A) is a point in the probability simplex. */
+/*        It is derived from the diagonal of  A^t * A.  Do the same with the */
+/*        diagonal of A * A^t, compute the entropy of the corresponding */
+/*        probability distribution. Note that A * A^t and A^t * A have the */
+/*        same trace. */
+
+	entrat = 0.f;
+	i__1 = *n + *m;
+	for (p = *n + 1; p <= i__1; ++p) {
+/* Computing 2nd power */
+	    r__1 = work[p] / xsc;
+	    big1 = r__1 * r__1 * temp1;
+	    if (big1 != 0.f) {
+		entrat += big1 * log(big1);
+	    }
+/* L1114: */
+	}
+	entrat = -entrat / log((real) (*m));
+
+/*        Analyze the entropies and decide A or A^t. Smaller entropy */
+/*        usually means better input for the algorithm. */
+
+	transp = entrat < entra;
+
+/*        If A^t is better than A, transpose A. */
+
+	if (transp) {
+/*           In an optimal implementation, this trivial transpose */
+/*           should be replaced with faster transpose. */
+	    i__1 = *n - 1;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = *n;
+		for (q = p + 1; q <= i__2; ++q) {
+		    temp1 = a[q + p * a_dim1];
+		    a[q + p * a_dim1] = a[p + q * a_dim1];
+		    a[p + q * a_dim1] = temp1;
+/* L1116: */
+		}
+/* L1115: */
+	    }
+	    i__1 = *n;
+	    for (p = 1; p <= i__1; ++p) {
+		work[*m + *n + p] = sva[p];
+		sva[p] = work[*n + p];
+/* L1117: */
+	    }
+	    temp1 = aapp;
+	    aapp = aatmax;
+	    aatmax = temp1;
+	    temp1 = aaqq;
+	    aaqq = aatmin;
+	    aatmin = temp1;
+	    kill = lsvec;
+	    lsvec = rsvec;
+	    rsvec = kill;
+
+	    rowpiv = TRUE_;
+	}
+
+    }
+/*     END IF L2TRAN */
+
+/*     Scale the matrix so that its maximal singular value remains less */
+/*     than SQRT(BIG) -- the matrix is scaled so that its maximal column */
+/*     has Euclidean norm equal to SQRT(BIG/N). The only reason to keep */
+/*     SQRT(BIG) instead of BIG is the fact that SGEJSV uses LAPACK and */
+/*     BLAS routines that, in some implementations, are not capable of */
+/*     working in the full interval [SFMIN,BIG] and that they may provoke */
+/*     overflows in the intermediate results. If the singular values spread */
+/*     from SFMIN to BIG, then SGESVJ will compute them. So, in that case, */
+/*     one should use SGESVJ instead of SGEJSV. */
+
+    big1 = sqrt(big);
+    temp1 = sqrt(big / (real) (*n));
+
+    slascl_("G", &c__0, &c__0, &aapp, &temp1, n, &c__1, &sva[1], n, &ierr);
+    if (aaqq > aapp * sfmin) {
+	aaqq = aaqq / aapp * temp1;
+    } else {
+	aaqq = aaqq * temp1 / aapp;
+    }
+    temp1 *= scalem;
+    slascl_("G", &c__0, &c__0, &aapp, &temp1, m, n, &a[a_offset], lda, &ierr);
+
+/*     To undo scaling at the end of this procedure, multiply the */
+/*     computed singular values with USCAL2 / USCAL1. */
+
+    uscal1 = temp1;
+    uscal2 = aapp;
+
+    if (l2kill) {
+/*        L2KILL enforces computation of nonzero singular values in */
+/*        the restricted range of condition number of the initial A, */
+/*        sigma_max(A) / sigma_min(A) approx. SQRT(BIG)/SQRT(SFMIN). */
+	xsc = sqrt(sfmin);
+    } else {
+	xsc = small;
+
+/*        Now, if the condition number of A is too big, */
+/*        sigma_max(A) / sigma_min(A) .GT. SQRT(BIG/N) * EPSLN / SFMIN, */
+/*        as a precaution measure, the full SVD is computed using SGESVJ */
+/*        with accumulated Jacobi rotations. This provides numerically */
+/*        more robust computation, at the cost of slightly increased run */
+/*        time. Depending on the concrete implementation of BLAS and LAPACK */
+/*        (i.e. how they behave in presence of extreme ill-conditioning) the */
+/*        implementor may decide to remove this switch. */
+	if (aaqq < sqrt(sfmin) && lsvec && rsvec) {
+	    jracc = TRUE_;
+	}
+
+    }
+    if (aaqq < xsc) {
+	i__1 = *n;
+	for (p = 1; p <= i__1; ++p) {
+	    if (sva[p] < xsc) {
+		slaset_("A", m, &c__1, &c_b34, &c_b34, &a[p * a_dim1 + 1], 
+			lda);
+		sva[p] = 0.f;
+	    }
+/* L700: */
+	}
+    }
+
+/*     Preconditioning using QR factorization with pivoting */
+
+    if (rowpiv) {
+/*        Optional row permutation (Bjoerck row pivoting): */
+/*        A result by Cox and Higham shows that the Bjoerck's */
+/*        row pivoting combined with standard column pivoting */
+/*        has similar effect as Powell-Reid complete pivoting. */
+/*        The ell-infinity norms of A are made nonincreasing. */
+	i__1 = *m - 1;
+	for (p = 1; p <= i__1; ++p) {
+	    i__2 = *m - p + 1;
+	    q = isamax_(&i__2, &work[*m + *n + p], &c__1) + p - 1;
+	    iwork[(*n << 1) + p] = q;
+	    if (p != q) {
+		temp1 = work[*m + *n + p];
+		work[*m + *n + p] = work[*m + *n + q];
+		work[*m + *n + q] = temp1;
+	    }
+/* L1952: */
+	}
+	i__1 = *m - 1;
+	slaswp_(n, &a[a_offset], lda, &c__1, &i__1, &iwork[(*n << 1) + 1], &
+		c__1);
+    }
+
+/*     End of the preparation phase (scaling, optional sorting and */
+/*     transposing, optional flushing of small columns). */
+
+/*     Preconditioning */
+
+/*     If the full SVD is needed, the right singular vectors are computed */
+/*     from a matrix equation, and for that we need theoretical analysis */
+/*     of the Businger-Golub pivoting. So we use SGEQP3 as the first RR QRF. */
+/*     In all other cases the first RR QRF can be chosen by other criteria */
+/*     (eg speed by replacing global with restricted window pivoting, such */
+/*     as in SGEQPX from TOMS # 782). Good results will be obtained using */
+/*     SGEQPX with properly (!) chosen numerical parameters. */
+/*     Any improvement of SGEQP3 improves overal performance of SGEJSV. */
+
+/*     A * P1 = Q1 * [ R1^t 0]^t: */
+    i__1 = *n;
+    for (p = 1; p <= i__1; ++p) {
+/*        .. all columns are free columns */
+	iwork[p] = 0;
+/* L1963: */
+    }
+    i__1 = *lwork - *n;
+    sgeqp3_(m, n, &a[a_offset], lda, &iwork[1], &work[1], &work[*n + 1], &
+	    i__1, &ierr);
+
+/*     The upper triangular matrix R1 from the first QRF is inspected for */
+/*     rank deficiency and possibilities for deflation, or possible */
+/*     ill-conditioning. Depending on the user specified flag L2RANK, */
+/*     the procedure explores possibilities to reduce the numerical */
+/*     rank by inspecting the computed upper triangular factor. If */
+/*     L2RANK or L2ABER are up, then SGEJSV will compute the SVD of */
+/*     A + dA, where ||dA|| <= f(M,N)*EPSLN. */
+
+    nr = 1;
+    if (l2aber) {
+/*        Standard absolute error bound suffices. All sigma_i with */
+/*        sigma_i < N*EPSLN*||A|| are flushed to zero. This is an */
+/*        agressive enforcement of lower numerical rank by introducing a */
+/*        backward error of the order of N*EPSLN*||A||. */
+	temp1 = sqrt((real) (*n)) * epsln;
+	i__1 = *n;
+	for (p = 2; p <= i__1; ++p) {
+	    if ((r__2 = a[p + p * a_dim1], dabs(r__2)) >= temp1 * (r__1 = a[
+		    a_dim1 + 1], dabs(r__1))) {
+		++nr;
+	    } else {
+		goto L3002;
+	    }
+/* L3001: */
+	}
+L3002:
+	;
+    } else if (l2rank) {
+/*        .. similarly as above, only slightly more gentle (less agressive). */
+/*        Sudden drop on the diagonal of R1 is used as the criterion for */
+/*        close-to-rank-defficient. */
+	temp1 = sqrt(sfmin);
+	i__1 = *n;
+	for (p = 2; p <= i__1; ++p) {
+	    if ((r__2 = a[p + p * a_dim1], dabs(r__2)) < epsln * (r__1 = a[p 
+		    - 1 + (p - 1) * a_dim1], dabs(r__1)) || (r__3 = a[p + p * 
+		    a_dim1], dabs(r__3)) < small || l2kill && (r__4 = a[p + p 
+		    * a_dim1], dabs(r__4)) < temp1) {
+		goto L3402;
+	    }
+	    ++nr;
+/* L3401: */
+	}
+L3402:
+
+	;
+    } else {
+/*        The goal is high relative accuracy. However, if the matrix */
+/*        has high scaled condition number the relative accuracy is in */
+/*        general not feasible. Later on, a condition number estimator */
+/*        will be deployed to estimate the scaled condition number. */
+/*        Here we just remove the underflowed part of the triangular */
+/*        factor. This prevents the situation in which the code is */
+/*        working hard to get the accuracy not warranted by the data. */
+	temp1 = sqrt(sfmin);
+	i__1 = *n;
+	for (p = 2; p <= i__1; ++p) {
+	    if ((r__1 = a[p + p * a_dim1], dabs(r__1)) < small || l2kill && (
+		    r__2 = a[p + p * a_dim1], dabs(r__2)) < temp1) {
+		goto L3302;
+	    }
+	    ++nr;
+/* L3301: */
+	}
+L3302:
+
+	;
+    }
+
+    almort = FALSE_;
+    if (nr == *n) {
+	maxprj = 1.f;
+	i__1 = *n;
+	for (p = 2; p <= i__1; ++p) {
+	    temp1 = (r__1 = a[p + p * a_dim1], dabs(r__1)) / sva[iwork[p]];
+	    maxprj = dmin(maxprj,temp1);
+/* L3051: */
+	}
+/* Computing 2nd power */
+	r__1 = maxprj;
+	if (r__1 * r__1 >= 1.f - (real) (*n) * epsln) {
+	    almort = TRUE_;
+	}
+    }
+
+
+    sconda = -1.f;
+    condr1 = -1.f;
+    condr2 = -1.f;
+
+    if (errest) {
+	if (*n == nr) {
+	    if (rsvec) {
+/*              .. V is available as workspace */
+		slacpy_("U", n, n, &a[a_offset], lda, &v[v_offset], ldv);
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    temp1 = sva[iwork[p]];
+		    r__1 = 1.f / temp1;
+		    sscal_(&p, &r__1, &v[p * v_dim1 + 1], &c__1);
+/* L3053: */
+		}
+		spocon_("U", n, &v[v_offset], ldv, &c_b35, &temp1, &work[*n + 
+			1], &iwork[(*n << 1) + *m + 1], &ierr);
+	    } else if (lsvec) {
+/*              .. U is available as workspace */
+		slacpy_("U", n, n, &a[a_offset], lda, &u[u_offset], ldu);
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    temp1 = sva[iwork[p]];
+		    r__1 = 1.f / temp1;
+		    sscal_(&p, &r__1, &u[p * u_dim1 + 1], &c__1);
+/* L3054: */
+		}
+		spocon_("U", n, &u[u_offset], ldu, &c_b35, &temp1, &work[*n + 
+			1], &iwork[(*n << 1) + *m + 1], &ierr);
+	    } else {
+		slacpy_("U", n, n, &a[a_offset], lda, &work[*n + 1], n);
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    temp1 = sva[iwork[p]];
+		    r__1 = 1.f / temp1;
+		    sscal_(&p, &r__1, &work[*n + (p - 1) * *n + 1], &c__1);
+/* L3052: */
+		}
+/*           .. the columns of R are scaled to have unit Euclidean lengths. */
+		spocon_("U", n, &work[*n + 1], n, &c_b35, &temp1, &work[*n + *
+			n * *n + 1], &iwork[(*n << 1) + *m + 1], &ierr);
+	    }
+	    sconda = 1.f / sqrt(temp1);
+/*           SCONDA is an estimate of SQRT(||(R^t * R)^(-1)||_1). */
+/*           N^(-1/4) * SCONDA <= ||R^(-1)||_2 <= N^(1/4) * SCONDA */
+	} else {
+	    sconda = -1.f;
+	}
+    }
+
+    l2pert = l2pert && (r__1 = a[a_dim1 + 1] / a[nr + nr * a_dim1], dabs(r__1)
+	    ) > sqrt(big1);
+/*     If there is no violent scaling, artificial perturbation is not needed. */
+
+/*     Phase 3: */
+
+    if (! (rsvec || lsvec)) {
+
+/*         Singular Values only */
+
+/*         .. transpose A(1:NR,1:N) */
+/* Computing MIN */
+	i__2 = *n - 1;
+	i__1 = min(i__2,nr);
+	for (p = 1; p <= i__1; ++p) {
+	    i__2 = *n - p;
+	    scopy_(&i__2, &a[p + (p + 1) * a_dim1], lda, &a[p + 1 + p * 
+		    a_dim1], &c__1);
+/* L1946: */
+	}
+
+/*        The following two DO-loops introduce small relative perturbation */
+/*        into the strict upper triangle of the lower triangular matrix. */
+/*        Small entries below the main diagonal are also changed. */
+/*        This modification is useful if the computing environment does not */
+/*        provide/allow FLUSH TO ZERO underflow, for it prevents many */
+/*        annoying denormalized numbers in case of strongly scaled matrices. */
+/*        The perturbation is structured so that it does not introduce any */
+/*        new perturbation of the singular values, and it does not destroy */
+/*        the job done by the preconditioner. */
+/*        The licence for this perturbation is in the variable L2PERT, which */
+/*        should be .FALSE. if FLUSH TO ZERO underflow is active. */
+
+	if (! almort) {
+
+	    if (l2pert) {
+/*              XSC = SQRT(SMALL) */
+		xsc = epsln / (real) (*n);
+		i__1 = nr;
+		for (q = 1; q <= i__1; ++q) {
+		    temp1 = xsc * (r__1 = a[q + q * a_dim1], dabs(r__1));
+		    i__2 = *n;
+		    for (p = 1; p <= i__2; ++p) {
+			if (p > q && (r__1 = a[p + q * a_dim1], dabs(r__1)) <=
+				 temp1 || p < q) {
+			    a[p + q * a_dim1] = r_sign(&temp1, &a[p + q * 
+				    a_dim1]);
+			}
+/* L4949: */
+		    }
+/* L4947: */
+		}
+	    } else {
+		i__1 = nr - 1;
+		i__2 = nr - 1;
+		slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &a[(a_dim1 << 1) + 
+			1], lda);
+	    }
+
+/*            .. second preconditioning using the QR factorization */
+
+	    i__1 = *lwork - *n;
+	    sgeqrf_(n, &nr, &a[a_offset], lda, &work[1], &work[*n + 1], &i__1, 
+		     &ierr);
+
+/*           .. and transpose upper to lower triangular */
+	    i__1 = nr - 1;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = nr - p;
+		scopy_(&i__2, &a[p + (p + 1) * a_dim1], lda, &a[p + 1 + p * 
+			a_dim1], &c__1);
+/* L1948: */
+	    }
+
+	}
+
+/*           Row-cyclic Jacobi SVD algorithm with column pivoting */
+
+/*           .. again some perturbation (a "background noise") is added */
+/*           to drown denormals */
+	if (l2pert) {
+/*              XSC = SQRT(SMALL) */
+	    xsc = epsln / (real) (*n);
+	    i__1 = nr;
+	    for (q = 1; q <= i__1; ++q) {
+		temp1 = xsc * (r__1 = a[q + q * a_dim1], dabs(r__1));
+		i__2 = nr;
+		for (p = 1; p <= i__2; ++p) {
+		    if (p > q && (r__1 = a[p + q * a_dim1], dabs(r__1)) <= 
+			    temp1 || p < q) {
+			a[p + q * a_dim1] = r_sign(&temp1, &a[p + q * a_dim1])
+				;
+		    }
+/* L1949: */
+		}
+/* L1947: */
+	    }
+	} else {
+	    i__1 = nr - 1;
+	    i__2 = nr - 1;
+	    slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &a[(a_dim1 << 1) + 1], 
+		    lda);
+	}
+
+/*           .. and one-sided Jacobi rotations are started on a lower */
+/*           triangular matrix (plus perturbation which is ignored in */
+/*           the part which destroys triangular form (confusing?!)) */
+
+	sgesvj_("L", "NoU", "NoV", &nr, &nr, &a[a_offset], lda, &sva[1], n, &
+		v[v_offset], ldv, &work[1], lwork, info);
+
+	scalem = work[1];
+	numrank = i_nint(&work[2]);
+
+
+    } else if (rsvec && ! lsvec) {
+
+/*        -> Singular Values and Right Singular Vectors <- */
+
+	if (almort) {
+
+/*           .. in this case NR equals N */
+	    i__1 = nr;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = *n - p + 1;
+		scopy_(&i__2, &a[p + p * a_dim1], lda, &v[p + p * v_dim1], &
+			c__1);
+/* L1998: */
+	    }
+	    i__1 = nr - 1;
+	    i__2 = nr - 1;
+	    slaset_("Upper", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 1) + 
+		    1], ldv);
+
+	    sgesvj_("L", "U", "N", n, &nr, &v[v_offset], ldv, &sva[1], &nr, &
+		    a[a_offset], lda, &work[1], lwork, info);
+	    scalem = work[1];
+	    numrank = i_nint(&work[2]);
+	} else {
+
+/*        .. two more QR factorizations ( one QRF is not enough, two require */
+/*        accumulated product of Jacobi rotations, three are perfect ) */
+
+	    i__1 = nr - 1;
+	    i__2 = nr - 1;
+	    slaset_("Lower", &i__1, &i__2, &c_b34, &c_b34, &a[a_dim1 + 2], 
+		    lda);
+	    i__1 = *lwork - *n;
+	    sgelqf_(&nr, n, &a[a_offset], lda, &work[1], &work[*n + 1], &i__1, 
+		     &ierr);
+	    slacpy_("Lower", &nr, &nr, &a[a_offset], lda, &v[v_offset], ldv);
+	    i__1 = nr - 1;
+	    i__2 = nr - 1;
+	    slaset_("Upper", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 1) + 
+		    1], ldv);
+	    i__1 = *lwork - (*n << 1);
+	    sgeqrf_(&nr, &nr, &v[v_offset], ldv, &work[*n + 1], &work[(*n << 
+		    1) + 1], &i__1, &ierr);
+	    i__1 = nr;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = nr - p + 1;
+		scopy_(&i__2, &v[p + p * v_dim1], ldv, &v[p + p * v_dim1], &
+			c__1);
+/* L8998: */
+	    }
+	    i__1 = nr - 1;
+	    i__2 = nr - 1;
+	    slaset_("Upper", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 1) + 
+		    1], ldv);
+
+	    sgesvj_("Lower", "U", "N", &nr, &nr, &v[v_offset], ldv, &sva[1], &
+		    nr, &u[u_offset], ldu, &work[*n + 1], lwork, info);
+	    scalem = work[*n + 1];
+	    numrank = i_nint(&work[*n + 2]);
+	    if (nr < *n) {
+		i__1 = *n - nr;
+		slaset_("A", &i__1, &nr, &c_b34, &c_b34, &v[nr + 1 + v_dim1], 
+			ldv);
+		i__1 = *n - nr;
+		slaset_("A", &nr, &i__1, &c_b34, &c_b34, &v[(nr + 1) * v_dim1 
+			+ 1], ldv);
+		i__1 = *n - nr;
+		i__2 = *n - nr;
+		slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &v[nr + 1 + (nr + 
+			1) * v_dim1], ldv);
+	    }
+
+	    i__1 = *lwork - *n;
+	    sormlq_("Left", "Transpose", n, n, &nr, &a[a_offset], lda, &work[
+		    1], &v[v_offset], ldv, &work[*n + 1], &i__1, &ierr);
+
+	}
+
+	i__1 = *n;
+	for (p = 1; p <= i__1; ++p) {
+	    scopy_(n, &v[p + v_dim1], ldv, &a[iwork[p] + a_dim1], lda);
+/* L8991: */
+	}
+	slacpy_("All", n, n, &a[a_offset], lda, &v[v_offset], ldv);
+
+	if (transp) {
+	    slacpy_("All", n, n, &v[v_offset], ldv, &u[u_offset], ldu);
+	}
+
+    } else if (lsvec && ! rsvec) {
+
+/*        -#- Singular Values and Left Singular Vectors                 -#- */
+
+/*        .. second preconditioning step to avoid need to accumulate */
+/*        Jacobi rotations in the Jacobi iterations. */
+	i__1 = nr;
+	for (p = 1; p <= i__1; ++p) {
+	    i__2 = *n - p + 1;
+	    scopy_(&i__2, &a[p + p * a_dim1], lda, &u[p + p * u_dim1], &c__1);
+/* L1965: */
+	}
+	i__1 = nr - 1;
+	i__2 = nr - 1;
+	slaset_("Upper", &i__1, &i__2, &c_b34, &c_b34, &u[(u_dim1 << 1) + 1], 
+		ldu);
+
+	i__1 = *lwork - (*n << 1);
+	sgeqrf_(n, &nr, &u[u_offset], ldu, &work[*n + 1], &work[(*n << 1) + 1]
+, &i__1, &ierr);
+
+	i__1 = nr - 1;
+	for (p = 1; p <= i__1; ++p) {
+	    i__2 = nr - p;
+	    scopy_(&i__2, &u[p + (p + 1) * u_dim1], ldu, &u[p + 1 + p * 
+		    u_dim1], &c__1);
+/* L1967: */
+	}
+	i__1 = nr - 1;
+	i__2 = nr - 1;
+	slaset_("Upper", &i__1, &i__2, &c_b34, &c_b34, &u[(u_dim1 << 1) + 1], 
+		ldu);
+
+	i__1 = *lwork - *n;
+	sgesvj_("Lower", "U", "N", &nr, &nr, &u[u_offset], ldu, &sva[1], &nr, 
+		&a[a_offset], lda, &work[*n + 1], &i__1, info);
+	scalem = work[*n + 1];
+	numrank = i_nint(&work[*n + 2]);
+
+	if (nr < *m) {
+	    i__1 = *m - nr;
+	    slaset_("A", &i__1, &nr, &c_b34, &c_b34, &u[nr + 1 + u_dim1], ldu);
+	    if (nr < n1) {
+		i__1 = n1 - nr;
+		slaset_("A", &nr, &i__1, &c_b34, &c_b34, &u[(nr + 1) * u_dim1 
+			+ 1], ldu);
+		i__1 = *m - nr;
+		i__2 = n1 - nr;
+		slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &u[nr + 1 + (nr + 
+			1) * u_dim1], ldu);
+	    }
+	}
+
+	i__1 = *lwork - *n;
+	sormqr_("Left", "No Tr", m, &n1, n, &a[a_offset], lda, &work[1], &u[
+		u_offset], ldu, &work[*n + 1], &i__1, &ierr);
+
+	if (rowpiv) {
+	    i__1 = *m - 1;
+	    slaswp_(&n1, &u[u_offset], ldu, &c__1, &i__1, &iwork[(*n << 1) + 
+		    1], &c_n1);
+	}
+
+	i__1 = n1;
+	for (p = 1; p <= i__1; ++p) {
+	    xsc = 1.f / snrm2_(m, &u[p * u_dim1 + 1], &c__1);
+	    sscal_(m, &xsc, &u[p * u_dim1 + 1], &c__1);
+/* L1974: */
+	}
+
+	if (transp) {
+	    slacpy_("All", n, n, &u[u_offset], ldu, &v[v_offset], ldv);
+	}
+
+    } else {
+
+/*        -#- Full SVD -#- */
+
+	if (! jracc) {
+
+	    if (! almort) {
+
+/*           Second Preconditioning Step (QRF [with pivoting]) */
+/*           Note that the composition of TRANSPOSE, QRF and TRANSPOSE is */
+/*           equivalent to an LQF CALL. Since in many libraries the QRF */
+/*           seems to be better optimized than the LQF, we do explicit */
+/*           transpose and use the QRF. This is subject to changes in an */
+/*           optimized implementation of SGEJSV. */
+
+		i__1 = nr;
+		for (p = 1; p <= i__1; ++p) {
+		    i__2 = *n - p + 1;
+		    scopy_(&i__2, &a[p + p * a_dim1], lda, &v[p + p * v_dim1], 
+			     &c__1);
+/* L1968: */
+		}
+
+/*           .. the following two loops perturb small entries to avoid */
+/*           denormals in the second QR factorization, where they are */
+/*           as good as zeros. This is done to avoid painfully slow */
+/*           computation with denormals. The relative size of the perturbation */
+/*           is a parameter that can be changed by the implementer. */
+/*           This perturbation device will be obsolete on machines with */
+/*           properly implemented arithmetic. */
+/*           To switch it off, set L2PERT=.FALSE. To remove it from  the */
+/*           code, remove the action under L2PERT=.TRUE., leave the ELSE part. */
+/*           The following two loops should be blocked and fused with the */
+/*           transposed copy above. */
+
+		if (l2pert) {
+		    xsc = sqrt(small);
+		    i__1 = nr;
+		    for (q = 1; q <= i__1; ++q) {
+			temp1 = xsc * (r__1 = v[q + q * v_dim1], dabs(r__1));
+			i__2 = *n;
+			for (p = 1; p <= i__2; ++p) {
+			    if (p > q && (r__1 = v[p + q * v_dim1], dabs(r__1)
+				    ) <= temp1 || p < q) {
+				v[p + q * v_dim1] = r_sign(&temp1, &v[p + q * 
+					v_dim1]);
+			    }
+			    if (p < q) {
+				v[p + q * v_dim1] = -v[p + q * v_dim1];
+			    }
+/* L2968: */
+			}
+/* L2969: */
+		    }
+		} else {
+		    i__1 = nr - 1;
+		    i__2 = nr - 1;
+		    slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 
+			    1) + 1], ldv);
+		}
+
+/*           Estimate the row scaled condition number of R1 */
+/*           (If R1 is rectangular, N > NR, then the condition number */
+/*           of the leading NR x NR submatrix is estimated.) */
+
+		slacpy_("L", &nr, &nr, &v[v_offset], ldv, &work[(*n << 1) + 1]
+, &nr);
+		i__1 = nr;
+		for (p = 1; p <= i__1; ++p) {
+		    i__2 = nr - p + 1;
+		    temp1 = snrm2_(&i__2, &work[(*n << 1) + (p - 1) * nr + p], 
+			     &c__1);
+		    i__2 = nr - p + 1;
+		    r__1 = 1.f / temp1;
+		    sscal_(&i__2, &r__1, &work[(*n << 1) + (p - 1) * nr + p], 
+			    &c__1);
+/* L3950: */
+		}
+		spocon_("Lower", &nr, &work[(*n << 1) + 1], &nr, &c_b35, &
+			temp1, &work[(*n << 1) + nr * nr + 1], &iwork[*m + (*
+			n << 1) + 1], &ierr);
+		condr1 = 1.f / sqrt(temp1);
+/*           .. here need a second oppinion on the condition number */
+/*           .. then assume worst case scenario */
+/*           R1 is OK for inverse <=> CONDR1 .LT. FLOAT(N) */
+/*           more conservative    <=> CONDR1 .LT. SQRT(FLOAT(N)) */
+
+		cond_ok__ = sqrt((real) nr);
+/* [TP]       COND_OK is a tuning parameter. */
+		if (condr1 < cond_ok__) {
+/*              .. the second QRF without pivoting. Note: in an optimized */
+/*              implementation, this QRF should be implemented as the QRF */
+/*              of a lower triangular matrix. */
+/*              R1^t = Q2 * R2 */
+		    i__1 = *lwork - (*n << 1);
+		    sgeqrf_(n, &nr, &v[v_offset], ldv, &work[*n + 1], &work[(*
+			    n << 1) + 1], &i__1, &ierr);
+
+		    if (l2pert) {
+			xsc = sqrt(small) / epsln;
+			i__1 = nr;
+			for (p = 2; p <= i__1; ++p) {
+			    i__2 = p - 1;
+			    for (q = 1; q <= i__2; ++q) {
+/* Computing MIN */
+				r__3 = (r__1 = v[p + p * v_dim1], dabs(r__1)),
+					 r__4 = (r__2 = v[q + q * v_dim1], 
+					dabs(r__2));
+				temp1 = xsc * dmin(r__3,r__4);
+				if ((r__1 = v[q + p * v_dim1], dabs(r__1)) <= 
+					temp1) {
+				    v[q + p * v_dim1] = r_sign(&temp1, &v[q + 
+					    p * v_dim1]);
+				}
+/* L3958: */
+			    }
+/* L3959: */
+			}
+		    }
+
+		    if (nr != *n) {
+			slacpy_("A", n, &nr, &v[v_offset], ldv, &work[(*n << 
+				1) + 1], n);
+		    }
+/*              .. save ... */
+
+/*           .. this transposed copy should be better than naive */
+		    i__1 = nr - 1;
+		    for (p = 1; p <= i__1; ++p) {
+			i__2 = nr - p;
+			scopy_(&i__2, &v[p + (p + 1) * v_dim1], ldv, &v[p + 1 
+				+ p * v_dim1], &c__1);
+/* L1969: */
+		    }
+
+		    condr2 = condr1;
+
+		} else {
+
+/*              .. ill-conditioned case: second QRF with pivoting */
+/*              Note that windowed pivoting would be equaly good */
+/*              numerically, and more run-time efficient. So, in */
+/*              an optimal implementation, the next call to SGEQP3 */
+/*              should be replaced with eg. CALL SGEQPX (ACM TOMS #782) */
+/*              with properly (carefully) chosen parameters. */
+
+/*              R1^t * P2 = Q2 * R2 */
+		    i__1 = nr;
+		    for (p = 1; p <= i__1; ++p) {
+			iwork[*n + p] = 0;
+/* L3003: */
+		    }
+		    i__1 = *lwork - (*n << 1);
+		    sgeqp3_(n, &nr, &v[v_offset], ldv, &iwork[*n + 1], &work[*
+			    n + 1], &work[(*n << 1) + 1], &i__1, &ierr);
+/* *               CALL SGEQRF( N, NR, V, LDV, WORK(N+1), WORK(2*N+1), */
+/* *     &              LWORK-2*N, IERR ) */
+		    if (l2pert) {
+			xsc = sqrt(small);
+			i__1 = nr;
+			for (p = 2; p <= i__1; ++p) {
+			    i__2 = p - 1;
+			    for (q = 1; q <= i__2; ++q) {
+/* Computing MIN */
+				r__3 = (r__1 = v[p + p * v_dim1], dabs(r__1)),
+					 r__4 = (r__2 = v[q + q * v_dim1], 
+					dabs(r__2));
+				temp1 = xsc * dmin(r__3,r__4);
+				if ((r__1 = v[q + p * v_dim1], dabs(r__1)) <= 
+					temp1) {
+				    v[q + p * v_dim1] = r_sign(&temp1, &v[q + 
+					    p * v_dim1]);
+				}
+/* L3968: */
+			    }
+/* L3969: */
+			}
+		    }
+
+		    slacpy_("A", n, &nr, &v[v_offset], ldv, &work[(*n << 1) + 
+			    1], n);
+
+		    if (l2pert) {
+			xsc = sqrt(small);
+			i__1 = nr;
+			for (p = 2; p <= i__1; ++p) {
+			    i__2 = p - 1;
+			    for (q = 1; q <= i__2; ++q) {
+/* Computing MIN */
+				r__3 = (r__1 = v[p + p * v_dim1], dabs(r__1)),
+					 r__4 = (r__2 = v[q + q * v_dim1], 
+					dabs(r__2));
+				temp1 = xsc * dmin(r__3,r__4);
+				v[p + q * v_dim1] = -r_sign(&temp1, &v[q + p *
+					 v_dim1]);
+/* L8971: */
+			    }
+/* L8970: */
+			}
+		    } else {
+			i__1 = nr - 1;
+			i__2 = nr - 1;
+			slaset_("L", &i__1, &i__2, &c_b34, &c_b34, &v[v_dim1 
+				+ 2], ldv);
+		    }
+/*              Now, compute R2 = L3 * Q3, the LQ factorization. */
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sgelqf_(&nr, &nr, &v[v_offset], ldv, &work[(*n << 1) + *n 
+			    * nr + 1], &work[(*n << 1) + *n * nr + nr + 1], &
+			    i__1, &ierr);
+/*              .. and estimate the condition number */
+		    slacpy_("L", &nr, &nr, &v[v_offset], ldv, &work[(*n << 1) 
+			    + *n * nr + nr + 1], &nr);
+		    i__1 = nr;
+		    for (p = 1; p <= i__1; ++p) {
+			temp1 = snrm2_(&p, &work[(*n << 1) + *n * nr + nr + p]
+, &nr);
+			r__1 = 1.f / temp1;
+			sscal_(&p, &r__1, &work[(*n << 1) + *n * nr + nr + p], 
+				 &nr);
+/* L4950: */
+		    }
+		    spocon_("L", &nr, &work[(*n << 1) + *n * nr + nr + 1], &
+			    nr, &c_b35, &temp1, &work[(*n << 1) + *n * nr + 
+			    nr + nr * nr + 1], &iwork[*m + (*n << 1) + 1], &
+			    ierr);
+		    condr2 = 1.f / sqrt(temp1);
+
+		    if (condr2 >= cond_ok__) {
+/*                 .. save the Householder vectors used for Q3 */
+/*                 (this overwrittes the copy of R2, as it will not be */
+/*                 needed in this branch, but it does not overwritte the */
+/*                 Huseholder vectors of Q2.). */
+			slacpy_("U", &nr, &nr, &v[v_offset], ldv, &work[(*n <<
+				 1) + 1], n);
+/*                 .. and the rest of the information on Q3 is in */
+/*                 WORK(2*N+N*NR+1:2*N+N*NR+N) */
+		    }
+
+		}
+
+		if (l2pert) {
+		    xsc = sqrt(small);
+		    i__1 = nr;
+		    for (q = 2; q <= i__1; ++q) {
+			temp1 = xsc * v[q + q * v_dim1];
+			i__2 = q - 1;
+			for (p = 1; p <= i__2; ++p) {
+/*                    V(p,q) = - SIGN( TEMP1, V(q,p) ) */
+			    v[p + q * v_dim1] = -r_sign(&temp1, &v[p + q * 
+				    v_dim1]);
+/* L4969: */
+			}
+/* L4968: */
+		    }
+		} else {
+		    i__1 = nr - 1;
+		    i__2 = nr - 1;
+		    slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 
+			    1) + 1], ldv);
+		}
+
+/*        Second preconditioning finished; continue with Jacobi SVD */
+/*        The input matrix is lower trinagular. */
+
+/*        Recover the right singular vectors as solution of a well */
+/*        conditioned triangular matrix equation. */
+
+		if (condr1 < cond_ok__) {
+
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sgesvj_("L", "U", "N", &nr, &nr, &v[v_offset], ldv, &sva[
+			    1], &nr, &u[u_offset], ldu, &work[(*n << 1) + *n *
+			     nr + nr + 1], &i__1, info);
+		    scalem = work[(*n << 1) + *n * nr + nr + 1];
+		    numrank = i_nint(&work[(*n << 1) + *n * nr + nr + 2]);
+		    i__1 = nr;
+		    for (p = 1; p <= i__1; ++p) {
+			scopy_(&nr, &v[p * v_dim1 + 1], &c__1, &u[p * u_dim1 
+				+ 1], &c__1);
+			sscal_(&nr, &sva[p], &v[p * v_dim1 + 1], &c__1);
+/* L3970: */
+		    }
+/*        .. pick the right matrix equation and solve it */
+
+		    if (nr == *n) {
+/* :))             .. best case, R1 is inverted. The solution of this matrix */
+/*                 equation is Q2*V2 = the product of the Jacobi rotations */
+/*                 used in SGESVJ, premultiplied with the orthogonal matrix */
+/*                 from the second QR factorization. */
+			strsm_("L", "U", "N", "N", &nr, &nr, &c_b35, &a[
+				a_offset], lda, &v[v_offset], ldv);
+		    } else {
+/*                 .. R1 is well conditioned, but non-square. Transpose(R2) */
+/*                 is inverted to get the product of the Jacobi rotations */
+/*                 used in SGESVJ. The Q-factor from the second QR */
+/*                 factorization is then built in explicitly. */
+			strsm_("L", "U", "T", "N", &nr, &nr, &c_b35, &work[(*
+				n << 1) + 1], n, &v[v_offset], ldv);
+			if (nr < *n) {
+			    i__1 = *n - nr;
+			    slaset_("A", &i__1, &nr, &c_b34, &c_b34, &v[nr + 
+				    1 + v_dim1], ldv);
+			    i__1 = *n - nr;
+			    slaset_("A", &nr, &i__1, &c_b34, &c_b34, &v[(nr + 
+				    1) * v_dim1 + 1], ldv);
+			    i__1 = *n - nr;
+			    i__2 = *n - nr;
+			    slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &v[nr 
+				    + 1 + (nr + 1) * v_dim1], ldv);
+			}
+			i__1 = *lwork - (*n << 1) - *n * nr - nr;
+			sormqr_("L", "N", n, n, &nr, &work[(*n << 1) + 1], n, 
+				&work[*n + 1], &v[v_offset], ldv, &work[(*n <<
+				 1) + *n * nr + nr + 1], &i__1, &ierr);
+		    }
+
+		} else if (condr2 < cond_ok__) {
+
+/* :)           .. the input matrix A is very likely a relative of */
+/*              the Kahan matrix :) */
+/*              The matrix R2 is inverted. The solution of the matrix equation */
+/*              is Q3^T*V3 = the product of the Jacobi rotations (appplied to */
+/*              the lower triangular L3 from the LQ factorization of */
+/*              R2=L3*Q3), pre-multiplied with the transposed Q3. */
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sgesvj_("L", "U", "N", &nr, &nr, &v[v_offset], ldv, &sva[
+			    1], &nr, &u[u_offset], ldu, &work[(*n << 1) + *n *
+			     nr + nr + 1], &i__1, info);
+		    scalem = work[(*n << 1) + *n * nr + nr + 1];
+		    numrank = i_nint(&work[(*n << 1) + *n * nr + nr + 2]);
+		    i__1 = nr;
+		    for (p = 1; p <= i__1; ++p) {
+			scopy_(&nr, &v[p * v_dim1 + 1], &c__1, &u[p * u_dim1 
+				+ 1], &c__1);
+			sscal_(&nr, &sva[p], &u[p * u_dim1 + 1], &c__1);
+/* L3870: */
+		    }
+		    strsm_("L", "U", "N", "N", &nr, &nr, &c_b35, &work[(*n << 
+			    1) + 1], n, &u[u_offset], ldu);
+/*              .. apply the permutation from the second QR factorization */
+		    i__1 = nr;
+		    for (q = 1; q <= i__1; ++q) {
+			i__2 = nr;
+			for (p = 1; p <= i__2; ++p) {
+			    work[(*n << 1) + *n * nr + nr + iwork[*n + p]] = 
+				    u[p + q * u_dim1];
+/* L872: */
+			}
+			i__2 = nr;
+			for (p = 1; p <= i__2; ++p) {
+			    u[p + q * u_dim1] = work[(*n << 1) + *n * nr + nr 
+				    + p];
+/* L874: */
+			}
+/* L873: */
+		    }
+		    if (nr < *n) {
+			i__1 = *n - nr;
+			slaset_("A", &i__1, &nr, &c_b34, &c_b34, &v[nr + 1 + 
+				v_dim1], ldv);
+			i__1 = *n - nr;
+			slaset_("A", &nr, &i__1, &c_b34, &c_b34, &v[(nr + 1) *
+				 v_dim1 + 1], ldv);
+			i__1 = *n - nr;
+			i__2 = *n - nr;
+			slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &v[nr + 1 
+				+ (nr + 1) * v_dim1], ldv);
+		    }
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sormqr_("L", "N", n, n, &nr, &work[(*n << 1) + 1], n, &
+			    work[*n + 1], &v[v_offset], ldv, &work[(*n << 1) 
+			    + *n * nr + nr + 1], &i__1, &ierr);
+		} else {
+/*              Last line of defense. */
+/* #:(          This is a rather pathological case: no scaled condition */
+/*              improvement after two pivoted QR factorizations. Other */
+/*              possibility is that the rank revealing QR factorization */
+/*              or the condition estimator has failed, or the COND_OK */
+/*              is set very close to ONE (which is unnecessary). Normally, */
+/*              this branch should never be executed, but in rare cases of */
+/*              failure of the RRQR or condition estimator, the last line of */
+/*              defense ensures that SGEJSV completes the task. */
+/*              Compute the full SVD of L3 using SGESVJ with explicit */
+/*              accumulation of Jacobi rotations. */
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sgesvj_("L", "U", "V", &nr, &nr, &v[v_offset], ldv, &sva[
+			    1], &nr, &u[u_offset], ldu, &work[(*n << 1) + *n *
+			     nr + nr + 1], &i__1, info);
+		    scalem = work[(*n << 1) + *n * nr + nr + 1];
+		    numrank = i_nint(&work[(*n << 1) + *n * nr + nr + 2]);
+		    if (nr < *n) {
+			i__1 = *n - nr;
+			slaset_("A", &i__1, &nr, &c_b34, &c_b34, &v[nr + 1 + 
+				v_dim1], ldv);
+			i__1 = *n - nr;
+			slaset_("A", &nr, &i__1, &c_b34, &c_b34, &v[(nr + 1) *
+				 v_dim1 + 1], ldv);
+			i__1 = *n - nr;
+			i__2 = *n - nr;
+			slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &v[nr + 1 
+				+ (nr + 1) * v_dim1], ldv);
+		    }
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sormqr_("L", "N", n, n, &nr, &work[(*n << 1) + 1], n, &
+			    work[*n + 1], &v[v_offset], ldv, &work[(*n << 1) 
+			    + *n * nr + nr + 1], &i__1, &ierr);
+
+		    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+		    sormlq_("L", "T", &nr, &nr, &nr, &work[(*n << 1) + 1], n, 
+			    &work[(*n << 1) + *n * nr + 1], &u[u_offset], ldu, 
+			     &work[(*n << 1) + *n * nr + nr + 1], &i__1, &
+			    ierr);
+		    i__1 = nr;
+		    for (q = 1; q <= i__1; ++q) {
+			i__2 = nr;
+			for (p = 1; p <= i__2; ++p) {
+			    work[(*n << 1) + *n * nr + nr + iwork[*n + p]] = 
+				    u[p + q * u_dim1];
+/* L772: */
+			}
+			i__2 = nr;
+			for (p = 1; p <= i__2; ++p) {
+			    u[p + q * u_dim1] = work[(*n << 1) + *n * nr + nr 
+				    + p];
+/* L774: */
+			}
+/* L773: */
+		    }
+
+		}
+
+/*           Permute the rows of V using the (column) permutation from the */
+/*           first QRF. Also, scale the columns to make them unit in */
+/*           Euclidean norm. This applies to all cases. */
+
+		temp1 = sqrt((real) (*n)) * epsln;
+		i__1 = *n;
+		for (q = 1; q <= i__1; ++q) {
+		    i__2 = *n;
+		    for (p = 1; p <= i__2; ++p) {
+			work[(*n << 1) + *n * nr + nr + iwork[p]] = v[p + q * 
+				v_dim1];
+/* L972: */
+		    }
+		    i__2 = *n;
+		    for (p = 1; p <= i__2; ++p) {
+			v[p + q * v_dim1] = work[(*n << 1) + *n * nr + nr + p]
+				;
+/* L973: */
+		    }
+		    xsc = 1.f / snrm2_(n, &v[q * v_dim1 + 1], &c__1);
+		    if (xsc < 1.f - temp1 || xsc > temp1 + 1.f) {
+			sscal_(n, &xsc, &v[q * v_dim1 + 1], &c__1);
+		    }
+/* L1972: */
+		}
+/*           At this moment, V contains the right singular vectors of A. */
+/*           Next, assemble the left singular vector matrix U (M x N). */
+		if (nr < *m) {
+		    i__1 = *m - nr;
+		    slaset_("A", &i__1, &nr, &c_b34, &c_b34, &u[nr + 1 + 
+			    u_dim1], ldu);
+		    if (nr < n1) {
+			i__1 = n1 - nr;
+			slaset_("A", &nr, &i__1, &c_b34, &c_b34, &u[(nr + 1) *
+				 u_dim1 + 1], ldu);
+			i__1 = *m - nr;
+			i__2 = n1 - nr;
+			slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &u[nr + 1 
+				+ (nr + 1) * u_dim1], ldu);
+		    }
+		}
+
+/*           The Q matrix from the first QRF is built into the left singular */
+/*           matrix U. This applies to all cases. */
+
+		i__1 = *lwork - *n;
+		sormqr_("Left", "No_Tr", m, &n1, n, &a[a_offset], lda, &work[
+			1], &u[u_offset], ldu, &work[*n + 1], &i__1, &ierr);
+/*           The columns of U are normalized. The cost is O(M*N) flops. */
+		temp1 = sqrt((real) (*m)) * epsln;
+		i__1 = nr;
+		for (p = 1; p <= i__1; ++p) {
+		    xsc = 1.f / snrm2_(m, &u[p * u_dim1 + 1], &c__1);
+		    if (xsc < 1.f - temp1 || xsc > temp1 + 1.f) {
+			sscal_(m, &xsc, &u[p * u_dim1 + 1], &c__1);
+		    }
+/* L1973: */
+		}
+
+/*           If the initial QRF is computed with row pivoting, the left */
+/*           singular vectors must be adjusted. */
+
+		if (rowpiv) {
+		    i__1 = *m - 1;
+		    slaswp_(&n1, &u[u_offset], ldu, &c__1, &i__1, &iwork[(*n 
+			    << 1) + 1], &c_n1);
+		}
+
+	    } else {
+
+/*        .. the initial matrix A has almost orthogonal columns and */
+/*        the second QRF is not needed */
+
+		slacpy_("Upper", n, n, &a[a_offset], lda, &work[*n + 1], n);
+		if (l2pert) {
+		    xsc = sqrt(small);
+		    i__1 = *n;
+		    for (p = 2; p <= i__1; ++p) {
+			temp1 = xsc * work[*n + (p - 1) * *n + p];
+			i__2 = p - 1;
+			for (q = 1; q <= i__2; ++q) {
+			    work[*n + (q - 1) * *n + p] = -r_sign(&temp1, &
+				    work[*n + (p - 1) * *n + q]);
+/* L5971: */
+			}
+/* L5970: */
+		    }
+		} else {
+		    i__1 = *n - 1;
+		    i__2 = *n - 1;
+		    slaset_("Lower", &i__1, &i__2, &c_b34, &c_b34, &work[*n + 
+			    2], n);
+		}
+
+		i__1 = *lwork - *n - *n * *n;
+		sgesvj_("Upper", "U", "N", n, n, &work[*n + 1], n, &sva[1], n, 
+			 &u[u_offset], ldu, &work[*n + *n * *n + 1], &i__1, 
+			info);
+
+		scalem = work[*n + *n * *n + 1];
+		numrank = i_nint(&work[*n + *n * *n + 2]);
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    scopy_(n, &work[*n + (p - 1) * *n + 1], &c__1, &u[p * 
+			    u_dim1 + 1], &c__1);
+		    sscal_(n, &sva[p], &work[*n + (p - 1) * *n + 1], &c__1);
+/* L6970: */
+		}
+
+		strsm_("Left", "Upper", "NoTrans", "No UD", n, n, &c_b35, &a[
+			a_offset], lda, &work[*n + 1], n);
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    scopy_(n, &work[*n + p], n, &v[iwork[p] + v_dim1], ldv);
+/* L6972: */
+		}
+		temp1 = sqrt((real) (*n)) * epsln;
+		i__1 = *n;
+		for (p = 1; p <= i__1; ++p) {
+		    xsc = 1.f / snrm2_(n, &v[p * v_dim1 + 1], &c__1);
+		    if (xsc < 1.f - temp1 || xsc > temp1 + 1.f) {
+			sscal_(n, &xsc, &v[p * v_dim1 + 1], &c__1);
+		    }
+/* L6971: */
+		}
+
+/*           Assemble the left singular vector matrix U (M x N). */
+
+		if (*n < *m) {
+		    i__1 = *m - *n;
+		    slaset_("A", &i__1, n, &c_b34, &c_b34, &u[nr + 1 + u_dim1]
+, ldu);
+		    if (*n < n1) {
+			i__1 = n1 - *n;
+			slaset_("A", n, &i__1, &c_b34, &c_b34, &u[(*n + 1) * 
+				u_dim1 + 1], ldu);
+			i__1 = *m - *n;
+			i__2 = n1 - *n;
+			slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &u[nr + 1 
+				+ (*n + 1) * u_dim1], ldu);
+		    }
+		}
+		i__1 = *lwork - *n;
+		sormqr_("Left", "No Tr", m, &n1, n, &a[a_offset], lda, &work[
+			1], &u[u_offset], ldu, &work[*n + 1], &i__1, &ierr);
+		temp1 = sqrt((real) (*m)) * epsln;
+		i__1 = n1;
+		for (p = 1; p <= i__1; ++p) {
+		    xsc = 1.f / snrm2_(m, &u[p * u_dim1 + 1], &c__1);
+		    if (xsc < 1.f - temp1 || xsc > temp1 + 1.f) {
+			sscal_(m, &xsc, &u[p * u_dim1 + 1], &c__1);
+		    }
+/* L6973: */
+		}
+
+		if (rowpiv) {
+		    i__1 = *m - 1;
+		    slaswp_(&n1, &u[u_offset], ldu, &c__1, &i__1, &iwork[(*n 
+			    << 1) + 1], &c_n1);
+		}
+
+	    }
+
+/*        end of the  >> almost orthogonal case <<  in the full SVD */
+
+	} else {
+
+/*        This branch deploys a preconditioned Jacobi SVD with explicitly */
+/*        accumulated rotations. It is included as optional, mainly for */
+/*        experimental purposes. It does perfom well, and can also be used. */
+/*        In this implementation, this branch will be automatically activated */
+/*        if the  condition number sigma_max(A) / sigma_min(A) is predicted */
+/*        to be greater than the overflow threshold. This is because the */
+/*        a posteriori computation of the singular vectors assumes robust */
+/*        implementation of BLAS and some LAPACK procedures, capable of working */
+/*        in presence of extreme values. Since that is not always the case, ... */
+
+	    i__1 = nr;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = *n - p + 1;
+		scopy_(&i__2, &a[p + p * a_dim1], lda, &v[p + p * v_dim1], &
+			c__1);
+/* L7968: */
+	    }
+
+	    if (l2pert) {
+		xsc = sqrt(small / epsln);
+		i__1 = nr;
+		for (q = 1; q <= i__1; ++q) {
+		    temp1 = xsc * (r__1 = v[q + q * v_dim1], dabs(r__1));
+		    i__2 = *n;
+		    for (p = 1; p <= i__2; ++p) {
+			if (p > q && (r__1 = v[p + q * v_dim1], dabs(r__1)) <=
+				 temp1 || p < q) {
+			    v[p + q * v_dim1] = r_sign(&temp1, &v[p + q * 
+				    v_dim1]);
+			}
+			if (p < q) {
+			    v[p + q * v_dim1] = -v[p + q * v_dim1];
+			}
+/* L5968: */
+		    }
+/* L5969: */
+		}
+	    } else {
+		i__1 = nr - 1;
+		i__2 = nr - 1;
+		slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &v[(v_dim1 << 1) + 
+			1], ldv);
+	    }
+	    i__1 = *lwork - (*n << 1);
+	    sgeqrf_(n, &nr, &v[v_offset], ldv, &work[*n + 1], &work[(*n << 1) 
+		    + 1], &i__1, &ierr);
+	    slacpy_("L", n, &nr, &v[v_offset], ldv, &work[(*n << 1) + 1], n);
+
+	    i__1 = nr;
+	    for (p = 1; p <= i__1; ++p) {
+		i__2 = nr - p + 1;
+		scopy_(&i__2, &v[p + p * v_dim1], ldv, &u[p + p * u_dim1], &
+			c__1);
+/* L7969: */
+	    }
+	    if (l2pert) {
+		xsc = sqrt(small / epsln);
+		i__1 = nr;
+		for (q = 2; q <= i__1; ++q) {
+		    i__2 = q - 1;
+		    for (p = 1; p <= i__2; ++p) {
+/* Computing MIN */
+			r__3 = (r__1 = u[p + p * u_dim1], dabs(r__1)), r__4 = 
+				(r__2 = u[q + q * u_dim1], dabs(r__2));
+			temp1 = xsc * dmin(r__3,r__4);
+			u[p + q * u_dim1] = -r_sign(&temp1, &u[q + p * u_dim1]
+				);
+/* L9971: */
+		    }
+/* L9970: */
+		}
+	    } else {
+		i__1 = nr - 1;
+		i__2 = nr - 1;
+		slaset_("U", &i__1, &i__2, &c_b34, &c_b34, &u[(u_dim1 << 1) + 
+			1], ldu);
+	    }
+	    i__1 = *lwork - (*n << 1) - *n * nr;
+	    sgesvj_("L", "U", "V", &nr, &nr, &u[u_offset], ldu, &sva[1], n, &
+		    v[v_offset], ldv, &work[(*n << 1) + *n * nr + 1], &i__1, 
+		    info);
+	    scalem = work[(*n << 1) + *n * nr + 1];
+	    numrank = i_nint(&work[(*n << 1) + *n * nr + 2]);
+	    if (nr < *n) {
+		i__1 = *n - nr;
+		slaset_("A", &i__1, &nr, &c_b34, &c_b34, &v[nr + 1 + v_dim1], 
+			ldv);
+		i__1 = *n - nr;
+		slaset_("A", &nr, &i__1, &c_b34, &c_b34, &v[(nr + 1) * v_dim1 
+			+ 1], ldv);
+		i__1 = *n - nr;
+		i__2 = *n - nr;
+		slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &v[nr + 1 + (nr + 
+			1) * v_dim1], ldv);
+	    }
+	    i__1 = *lwork - (*n << 1) - *n * nr - nr;
+	    sormqr_("L", "N", n, n, &nr, &work[(*n << 1) + 1], n, &work[*n + 
+		    1], &v[v_offset], ldv, &work[(*n << 1) + *n * nr + nr + 1]
+, &i__1, &ierr);
+
+/*           Permute the rows of V using the (column) permutation from the */
+/*           first QRF. Also, scale the columns to make them unit in */
+/*           Euclidean norm. This applies to all cases. */
+
+	    temp1 = sqrt((real) (*n)) * epsln;
+	    i__1 = *n;
+	    for (q = 1; q <= i__1; ++q) {
+		i__2 = *n;
+		for (p = 1; p <= i__2; ++p) {
+		    work[(*n << 1) + *n * nr + nr + iwork[p]] = v[p + q * 
+			    v_dim1];
+/* L8972: */
+		}
+		i__2 = *n;
+		for (p = 1; p <= i__2; ++p) {
+		    v[p + q * v_dim1] = work[(*n << 1) + *n * nr + nr + p];
+/* L8973: */
+		}
+		xsc = 1.f / snrm2_(n, &v[q * v_dim1 + 1], &c__1);
+		if (xsc < 1.f - temp1 || xsc > temp1 + 1.f) {
+		    sscal_(n, &xsc, &v[q * v_dim1 + 1], &c__1);
+		}
+/* L7972: */
+	    }
+
+/*           At this moment, V contains the right singular vectors of A. */
+/*           Next, assemble the left singular vector matrix U (M x N). */
+
+	    if (*n < *m) {
+		i__1 = *m - *n;
+		slaset_("A", &i__1, n, &c_b34, &c_b34, &u[nr + 1 + u_dim1], 
+			ldu);
+		if (*n < n1) {
+		    i__1 = n1 - *n;
+		    slaset_("A", n, &i__1, &c_b34, &c_b34, &u[(*n + 1) * 
+			    u_dim1 + 1], ldu);
+		    i__1 = *m - *n;
+		    i__2 = n1 - *n;
+		    slaset_("A", &i__1, &i__2, &c_b34, &c_b35, &u[nr + 1 + (*
+			    n + 1) * u_dim1], ldu);
+		}
+	    }
+
+	    i__1 = *lwork - *n;
+	    sormqr_("Left", "No Tr", m, &n1, n, &a[a_offset], lda, &work[1], &
+		    u[u_offset], ldu, &work[*n + 1], &i__1, &ierr);
+
+	    if (rowpiv) {
+		i__1 = *m - 1;
+		slaswp_(&n1, &u[u_offset], ldu, &c__1, &i__1, &iwork[(*n << 1)
+			 + 1], &c_n1);
+	    }
+
+
+	}
+	if (transp) {
+/*           .. swap U and V because the procedure worked on A^t */
+	    i__1 = *n;
+	    for (p = 1; p <= i__1; ++p) {
+		sswap_(n, &u[p * u_dim1 + 1], &c__1, &v[p * v_dim1 + 1], &
+			c__1);
+/* L6974: */
+	    }
+	}
+
+    }
+/*     end of the full SVD */
+
+/*     Undo scaling, if necessary (and possible) */
+
+    if (uscal2 <= big / sva[1] * uscal1) {
+	slascl_("G", &c__0, &c__0, &uscal1, &uscal2, &nr, &c__1, &sva[1], n, &
+		ierr);
+	uscal1 = 1.f;
+	uscal2 = 1.f;
+    }
+
+    if (nr < *n) {
+	i__1 = *n;
+	for (p = nr + 1; p <= i__1; ++p) {
+	    sva[p] = 0.f;
+/* L3004: */
+	}
+    }
+
+    work[1] = uscal2 * scalem;
+    work[2] = uscal1;
+    if (errest) {
+	work[3] = sconda;
+    }
+    if (lsvec && rsvec) {
+	work[4] = condr1;
+	work[5] = condr2;
+    }
+    if (l2tran) {
+	work[6] = entra;
+	work[7] = entrat;
+    }
+
+    iwork[1] = nr;
+    iwork[2] = numrank;
+    iwork[3] = warning;
+
+    return 0;
+/*     .. */
+/*     .. END OF SGEJSV */
+/*     .. */
+} /* sgejsv_ */