/*  -- translated by f2c (version 20100827).
   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"

/* Table of constant values */

static doublereal c_b15 = -.125;
static integer c__1 = 1;
static doublereal c_b49 = 1.;
static doublereal c_b72 = -1.;

/* Subroutine */ int splicingdbdsqr_(char *uplo, integer *n, integer *ncvt, integer *
	nru, integer *ncc, doublereal *d__, doublereal *e, doublereal *vt, 
	integer *ldvt, doublereal *u, integer *ldu, doublereal *c__, integer *
	ldc, doublereal *work, integer *info)
{
    /* System generated locals */
    integer c_dim1, c_offset, u_dim1, u_offset, vt_dim1, vt_offset, i__1, 
	    i__2;
    doublereal d__1, d__2, d__3, d__4;

    /* Builtin functions */
    double pow_dd(doublereal *, doublereal *), sqrt(doublereal), d_sign(
	    doublereal *, doublereal *);

    /* Local variables */
    static doublereal f, g, h__;
    static integer i__, j, m;
    static doublereal r__, cs;
    static integer ll;
    static doublereal sn, mu;
    static integer nm1, nm12, nm13, lll;
    static doublereal eps, sll, tol, abse;
    static integer idir;
    static doublereal abss;
    static integer oldm;
    static doublereal cosl;
    static integer isub, iter;
    static doublereal unfl, sinl, cosr, smin, smax, sinr;
    extern /* Subroutine */ int splicingdrot_(integer *, doublereal *, integer *, 
	    doublereal *, integer *, doublereal *, doublereal *), splicingdlas2_(
	    doublereal *, doublereal *, doublereal *, doublereal *, 
	    doublereal *), splicingdscal_(integer *, doublereal *, doublereal *, 
	    integer *);
    extern logical splicinglsame_(char *, char *);
    static doublereal oldcs;
    extern /* Subroutine */ int splicingdlasr_(char *, char *, char *, integer *, 
	    integer *, doublereal *, doublereal *, doublereal *, integer *);
    static integer oldll;
    static doublereal shift, sigmn, oldsn;
    extern /* Subroutine */ int splicingdswap_(integer *, doublereal *, integer *, 
	    doublereal *, integer *);
    static integer maxit;
    static doublereal sminl, sigmx;
    static logical lower;
    extern /* Subroutine */ int splicingdlasq1_(integer *, doublereal *, doublereal *,
	     doublereal *, integer *), splicingdlasv2_(doublereal *, doublereal *, 
	    doublereal *, doublereal *, doublereal *, doublereal *, 
	    doublereal *, doublereal *, doublereal *);
    extern doublereal splicingdlamch_(char *);
    extern /* Subroutine */ int splicingdlartg_(doublereal *, doublereal *, 
	    doublereal *, doublereal *, doublereal *), splicingxerbla_(char *, 
	    integer *, ftnlen);
    static doublereal sminoa, thresh;
    static logical rotate;
    static doublereal tolmul;


/*  -- LAPACK routine (version 3.2) --   
    -- LAPACK is a software package provided by Univ. of Tennessee,    --   
    -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--   
       January 2007   


    Purpose   
    =======   

    DBDSQR computes the singular values and, optionally, the right and/or   
    left singular vectors from the singular value decomposition (SVD) of   
    a real N-by-N (upper or lower) bidiagonal matrix B using the implicit   
    zero-shift QR algorithm.  The SVD of B has the form   

       B = Q * S * P**T   

    where S is the diagonal matrix of singular values, Q is an orthogonal   
    matrix of left singular vectors, and P is an orthogonal matrix of   
    right singular vectors.  If left singular vectors are requested, this   
    subroutine actually returns U*Q instead of Q, and, if right singular   
    vectors are requested, this subroutine returns P**T*VT instead of   
    P**T, for given real input matrices U and VT.  When U and VT are the   
    orthogonal matrices that reduce a general matrix A to bidiagonal   
    form:  A = U*B*VT, as computed by DGEBRD, then   

       A = (U*Q) * S * (P**T*VT)   

    is the SVD of A.  Optionally, the subroutine may also compute Q**T*C   
    for a given real input matrix C.   

    See "Computing  Small Singular Values of Bidiagonal Matrices With   
    Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan,   
    LAPACK Working Note #3 (or SIAM J. Sci. Statist. Comput. vol. 11,   
    no. 5, pp. 873-912, Sept 1990) and   
    "Accurate singular values and differential qd algorithms," by   
    B. Parlett and V. Fernando, Technical Report CPAM-554, Mathematics   
    Department, University of California at Berkeley, July 1992   
    for a detailed description of the algorithm.   

    Arguments   
    =========   

    UPLO    (input) CHARACTER*1   
            = 'U':  B is upper bidiagonal;   
            = 'L':  B is lower bidiagonal.   

    N       (input) INTEGER   
            The order of the matrix B.  N >= 0.   

    NCVT    (input) INTEGER   
            The number of columns of the matrix VT. NCVT >= 0.   

    NRU     (input) INTEGER   
            The number of rows of the matrix U. NRU >= 0.   

    NCC     (input) INTEGER   
            The number of columns of the matrix C. NCC >= 0.   

    D       (input/output) DOUBLE PRECISION array, dimension (N)   
            On entry, the n diagonal elements of the bidiagonal matrix B.   
            On exit, if INFO=0, the singular values of B in decreasing   
            order.   

    E       (input/output) DOUBLE PRECISION array, dimension (N-1)   
            On entry, the N-1 offdiagonal elements of the bidiagonal   
            matrix B.   
            On exit, if INFO = 0, E is destroyed; if INFO > 0, D and E   
            will contain the diagonal and superdiagonal elements of a   
            bidiagonal matrix orthogonally equivalent to the one given   
            as input.   

    VT      (input/output) DOUBLE PRECISION array, dimension (LDVT, NCVT)   
            On entry, an N-by-NCVT matrix VT.   
            On exit, VT is overwritten by P**T * VT.   
            Not referenced if NCVT = 0.   

    LDVT    (input) INTEGER   
            The leading dimension of the array VT.   
            LDVT >= max(1,N) if NCVT > 0; LDVT >= 1 if NCVT = 0.   

    U       (input/output) DOUBLE PRECISION array, dimension (LDU, N)   
            On entry, an NRU-by-N matrix U.   
            On exit, U is overwritten by U * Q.   
            Not referenced if NRU = 0.   

    LDU     (input) INTEGER   
            The leading dimension of the array U.  LDU >= max(1,NRU).   

    C       (input/output) DOUBLE PRECISION array, dimension (LDC, NCC)   
            On entry, an N-by-NCC matrix C.   
            On exit, C is overwritten by Q**T * C.   
            Not referenced if NCC = 0.   

    LDC     (input) INTEGER   
            The leading dimension of the array C.   
            LDC >= max(1,N) if NCC > 0; LDC >=1 if NCC = 0.   

    WORK    (workspace) DOUBLE PRECISION array, dimension (4*N)   

    INFO    (output) INTEGER   
            = 0:  successful exit   
            < 0:  If INFO = -i, the i-th argument had an illegal value   
            > 0:   
               if NCVT = NRU = NCC = 0,   
                  = 1, a split was marked by a positive value in E   
                  = 2, current block of Z not diagonalized after 30*N   
                       iterations (in inner while loop)   
                  = 3, termination criterion of outer while loop not met   
                       (program created more than N unreduced blocks)   
               else NCVT = NRU = NCC = 0,   
                     the algorithm did not converge; D and E contain the   
                     elements of a bidiagonal matrix which is orthogonally   
                     similar to the input matrix B;  if INFO = i, i   
                     elements of E have not converged to zero.   

    Internal Parameters   
    ===================   

    TOLMUL  DOUBLE PRECISION, default = max(10,min(100,EPS**(-1/8)))   
            TOLMUL controls the convergence criterion of the QR loop.   
            If it is positive, TOLMUL*EPS is the desired relative   
               precision in the computed singular values.   
            If it is negative, abs(TOLMUL*EPS*sigma_max) is the   
               desired absolute accuracy in the computed singular   
               values (corresponds to relative accuracy   
               abs(TOLMUL*EPS) in the largest singular value.   
            abs(TOLMUL) should be between 1 and 1/EPS, and preferably   
               between 10 (for fast convergence) and .1/EPS   
               (for there to be some accuracy in the results).   
            Default is to lose at either one eighth or 2 of the   
               available decimal digits in each computed singular value   
               (whichever is smaller).   

    MAXITR  INTEGER, default = 6   
            MAXITR controls the maximum number of passes of the   
            algorithm through its inner loop. The algorithms stops   
            (and so fails to converge) if the number of passes   
            through the inner loop exceeds MAXITR*N**2.   

    =====================================================================   


       Test the input parameters.   

       Parameter adjustments */
    --d__;
    --e;
    vt_dim1 = *ldvt;
    vt_offset = 1 + vt_dim1;
    vt -= vt_offset;
    u_dim1 = *ldu;
    u_offset = 1 + u_dim1;
    u -= u_offset;
    c_dim1 = *ldc;
    c_offset = 1 + c_dim1;
    c__ -= c_offset;
    --work;

    /* Function Body */
    *info = 0;
    lower = splicinglsame_(uplo, "L");
    if (! splicinglsame_(uplo, "U") && ! lower) {
	*info = -1;
    } else if (*n < 0) {
	*info = -2;
    } else if (*ncvt < 0) {
	*info = -3;
    } else if (*nru < 0) {
	*info = -4;
    } else if (*ncc < 0) {
	*info = -5;
    } else if (*ncvt == 0 && *ldvt < 1 || *ncvt > 0 && *ldvt < max(1,*n)) {
	*info = -9;
    } else if (*ldu < max(1,*nru)) {
	*info = -11;
    } else if (*ncc == 0 && *ldc < 1 || *ncc > 0 && *ldc < max(1,*n)) {
	*info = -13;
    }
    if (*info != 0) {
	i__1 = -(*info);
	splicingxerbla_("DBDSQR", &i__1, (ftnlen)6);
	return 0;
    }
    if (*n == 0) {
	return 0;
    }
    if (*n == 1) {
	goto L160;
    }

/*     ROTATE is true if any singular vectors desired, false otherwise */

    rotate = *ncvt > 0 || *nru > 0 || *ncc > 0;

/*     If no singular vectors desired, use qd algorithm */

    if (! rotate) {
	splicingdlasq1_(n, &d__[1], &e[1], &work[1], info);
	return 0;
    }

    nm1 = *n - 1;
    nm12 = nm1 + nm1;
    nm13 = nm12 + nm1;
    idir = 0;

/*     Get machine constants */

    eps = splicingdlamch_("Epsilon");
    unfl = splicingdlamch_("Safe minimum");

/*     If matrix lower bidiagonal, rotate to be upper bidiagonal   
       by applying Givens rotations on the left */

    if (lower) {
	i__1 = *n - 1;
	for (i__ = 1; i__ <= i__1; ++i__) {
	    splicingdlartg_(&d__[i__], &e[i__], &cs, &sn, &r__);
	    d__[i__] = r__;
	    e[i__] = sn * d__[i__ + 1];
	    d__[i__ + 1] = cs * d__[i__ + 1];
	    work[i__] = cs;
	    work[nm1 + i__] = sn;
/* L10: */
	}

/*        Update singular vectors if desired */

	if (*nru > 0) {
	    splicingdlasr_("R", "V", "F", nru, n, &work[1], &work[*n], &u[u_offset], 
		    ldu);
	}
	if (*ncc > 0) {
	    splicingdlasr_("L", "V", "F", n, ncc, &work[1], &work[*n], &c__[c_offset],
		     ldc);
	}
    }

/*     Compute singular values to relative accuracy TOL   
       (By setting TOL to be negative, algorithm will compute   
       singular values to absolute accuracy ABS(TOL)*norm(input matrix))   

   Computing MAX   
   Computing MIN */
    d__3 = 100., d__4 = pow_dd(&eps, &c_b15);
    d__1 = 10., d__2 = min(d__3,d__4);
    tolmul = max(d__1,d__2);
    tol = tolmul * eps;

/*     Compute approximate maximum, minimum singular values */

    smax = 0.;
    i__1 = *n;
    for (i__ = 1; i__ <= i__1; ++i__) {
/* Computing MAX */
	d__2 = smax, d__3 = (d__1 = d__[i__], abs(d__1));
	smax = max(d__2,d__3);
/* L20: */
    }
    i__1 = *n - 1;
    for (i__ = 1; i__ <= i__1; ++i__) {
/* Computing MAX */
	d__2 = smax, d__3 = (d__1 = e[i__], abs(d__1));
	smax = max(d__2,d__3);
/* L30: */
    }
    sminl = 0.;
    if (tol >= 0.) {

/*        Relative accuracy desired */

	sminoa = abs(d__[1]);
	if (sminoa == 0.) {
	    goto L50;
	}
	mu = sminoa;
	i__1 = *n;
	for (i__ = 2; i__ <= i__1; ++i__) {
	    mu = (d__2 = d__[i__], abs(d__2)) * (mu / (mu + (d__1 = e[i__ - 1]
		    , abs(d__1))));
	    sminoa = min(sminoa,mu);
	    if (sminoa == 0.) {
		goto L50;
	    }
/* L40: */
	}
L50:
	sminoa /= sqrt((doublereal) (*n));
/* Computing MAX */
	d__1 = tol * sminoa, d__2 = *n * 6 * *n * unfl;
	thresh = max(d__1,d__2);
    } else {

/*        Absolute accuracy desired   

   Computing MAX */
	d__1 = abs(tol) * smax, d__2 = *n * 6 * *n * unfl;
	thresh = max(d__1,d__2);
    }

/*     Prepare for main iteration loop for the singular values   
       (MAXIT is the maximum number of passes through the inner   
       loop permitted before nonconvergence signalled.) */

    maxit = *n * 6 * *n;
    iter = 0;
    oldll = -1;
    oldm = -1;

/*     M points to last element of unconverged part of matrix */

    m = *n;

/*     Begin main iteration loop */

L60:

/*     Check for convergence or exceeding iteration count */

    if (m <= 1) {
	goto L160;
    }
    if (iter > maxit) {
	goto L200;
    }

/*     Find diagonal block of matrix to work on */

    if (tol < 0. && (d__1 = d__[m], abs(d__1)) <= thresh) {
	d__[m] = 0.;
    }
    smax = (d__1 = d__[m], abs(d__1));
    smin = smax;
    i__1 = m - 1;
    for (lll = 1; lll <= i__1; ++lll) {
	ll = m - lll;
	abss = (d__1 = d__[ll], abs(d__1));
	abse = (d__1 = e[ll], abs(d__1));
	if (tol < 0. && abss <= thresh) {
	    d__[ll] = 0.;
	}
	if (abse <= thresh) {
	    goto L80;
	}
	smin = min(smin,abss);
/* Computing MAX */
	d__1 = max(smax,abss);
	smax = max(d__1,abse);
/* L70: */
    }
    ll = 0;
    goto L90;
L80:
    e[ll] = 0.;

/*     Matrix splits since E(LL) = 0 */

    if (ll == m - 1) {

/*        Convergence of bottom singular value, return to top of loop */

	--m;
	goto L60;
    }
L90:
    ++ll;

/*     E(LL) through E(M-1) are nonzero, E(LL-1) is zero */

    if (ll == m - 1) {

/*        2 by 2 block, handle separately */

	splicingdlasv2_(&d__[m - 1], &e[m - 1], &d__[m], &sigmn, &sigmx, &sinr, &cosr,
		 &sinl, &cosl);
	d__[m - 1] = sigmx;
	e[m - 1] = 0.;
	d__[m] = sigmn;

/*        Compute singular vectors, if desired */

	if (*ncvt > 0) {
	    splicingdrot_(ncvt, &vt[m - 1 + vt_dim1], ldvt, &vt[m + vt_dim1], ldvt, &
		    cosr, &sinr);
	}
	if (*nru > 0) {
	    splicingdrot_(nru, &u[(m - 1) * u_dim1 + 1], &c__1, &u[m * u_dim1 + 1], &
		    c__1, &cosl, &sinl);
	}
	if (*ncc > 0) {
	    splicingdrot_(ncc, &c__[m - 1 + c_dim1], ldc, &c__[m + c_dim1], ldc, &
		    cosl, &sinl);
	}
	m += -2;
	goto L60;
    }

/*     If working on new submatrix, choose shift direction   
       (from larger end diagonal element towards smaller) */

    if (ll > oldm || m < oldll) {
	if ((d__1 = d__[ll], abs(d__1)) >= (d__2 = d__[m], abs(d__2))) {

/*           Chase bulge from top (big end) to bottom (small end) */

	    idir = 1;
	} else {

/*           Chase bulge from bottom (big end) to top (small end) */

	    idir = 2;
	}
    }

/*     Apply convergence tests */

    if (idir == 1) {

/*        Run convergence test in forward direction   
          First apply standard test to bottom of matrix */

	if ((d__2 = e[m - 1], abs(d__2)) <= abs(tol) * (d__1 = d__[m], abs(
		d__1)) || tol < 0. && (d__3 = e[m - 1], abs(d__3)) <= thresh) 
		{
	    e[m - 1] = 0.;
	    goto L60;
	}

	if (tol >= 0.) {

/*           If relative accuracy desired,   
             apply convergence criterion forward */

	    mu = (d__1 = d__[ll], abs(d__1));
	    sminl = mu;
	    i__1 = m - 1;
	    for (lll = ll; lll <= i__1; ++lll) {
		if ((d__1 = e[lll], abs(d__1)) <= tol * mu) {
		    e[lll] = 0.;
		    goto L60;
		}
		mu = (d__2 = d__[lll + 1], abs(d__2)) * (mu / (mu + (d__1 = e[
			lll], abs(d__1))));
		sminl = min(sminl,mu);
/* L100: */
	    }
	}

    } else {

/*        Run convergence test in backward direction   
          First apply standard test to top of matrix */

	if ((d__2 = e[ll], abs(d__2)) <= abs(tol) * (d__1 = d__[ll], abs(d__1)
		) || tol < 0. && (d__3 = e[ll], abs(d__3)) <= thresh) {
	    e[ll] = 0.;
	    goto L60;
	}

	if (tol >= 0.) {

/*           If relative accuracy desired,   
             apply convergence criterion backward */

	    mu = (d__1 = d__[m], abs(d__1));
	    sminl = mu;
	    i__1 = ll;
	    for (lll = m - 1; lll >= i__1; --lll) {
		if ((d__1 = e[lll], abs(d__1)) <= tol * mu) {
		    e[lll] = 0.;
		    goto L60;
		}
		mu = (d__2 = d__[lll], abs(d__2)) * (mu / (mu + (d__1 = e[lll]
			, abs(d__1))));
		sminl = min(sminl,mu);
/* L110: */
	    }
	}
    }
    oldll = ll;
    oldm = m;

/*     Compute shift.  First, test if shifting would ruin relative   
       accuracy, and if so set the shift to zero.   

   Computing MAX */
    d__1 = eps, d__2 = tol * .01;
    if (tol >= 0. && *n * tol * (sminl / smax) <= max(d__1,d__2)) {

/*        Use a zero shift to avoid loss of relative accuracy */

	shift = 0.;
    } else {

/*        Compute the shift from 2-by-2 block at end of matrix */

	if (idir == 1) {
	    sll = (d__1 = d__[ll], abs(d__1));
	    splicingdlas2_(&d__[m - 1], &e[m - 1], &d__[m], &shift, &r__);
	} else {
	    sll = (d__1 = d__[m], abs(d__1));
	    splicingdlas2_(&d__[ll], &e[ll], &d__[ll + 1], &shift, &r__);
	}

/*        Test if shift negligible, and if so set to zero */

	if (sll > 0.) {
/* Computing 2nd power */
	    d__1 = shift / sll;
	    if (d__1 * d__1 < eps) {
		shift = 0.;
	    }
	}
    }

/*     Increment iteration count */

    iter = iter + m - ll;

/*     If SHIFT = 0, do simplified QR iteration */

    if (shift == 0.) {
	if (idir == 1) {

/*           Chase bulge from top to bottom   
             Save cosines and sines for later singular vector updates */

	    cs = 1.;
	    oldcs = 1.;
	    i__1 = m - 1;
	    for (i__ = ll; i__ <= i__1; ++i__) {
		d__1 = d__[i__] * cs;
		splicingdlartg_(&d__1, &e[i__], &cs, &sn, &r__);
		if (i__ > ll) {
		    e[i__ - 1] = oldsn * r__;
		}
		d__1 = oldcs * r__;
		d__2 = d__[i__ + 1] * sn;
		splicingdlartg_(&d__1, &d__2, &oldcs, &oldsn, &d__[i__]);
		work[i__ - ll + 1] = cs;
		work[i__ - ll + 1 + nm1] = sn;
		work[i__ - ll + 1 + nm12] = oldcs;
		work[i__ - ll + 1 + nm13] = oldsn;
/* L120: */
	    }
	    h__ = d__[m] * cs;
	    d__[m] = h__ * oldcs;
	    e[m - 1] = h__ * oldsn;

/*           Update singular vectors */

	    if (*ncvt > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "F", &i__1, ncvt, &work[1], &work[*n], &vt[
			ll + vt_dim1], ldvt);
	    }
	    if (*nru > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("R", "V", "F", nru, &i__1, &work[nm12 + 1], &work[nm13 
			+ 1], &u[ll * u_dim1 + 1], ldu);
	    }
	    if (*ncc > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "F", &i__1, ncc, &work[nm12 + 1], &work[nm13 
			+ 1], &c__[ll + c_dim1], ldc);
	    }

/*           Test convergence */

	    if ((d__1 = e[m - 1], abs(d__1)) <= thresh) {
		e[m - 1] = 0.;
	    }

	} else {

/*           Chase bulge from bottom to top   
             Save cosines and sines for later singular vector updates */

	    cs = 1.;
	    oldcs = 1.;
	    i__1 = ll + 1;
	    for (i__ = m; i__ >= i__1; --i__) {
		d__1 = d__[i__] * cs;
		splicingdlartg_(&d__1, &e[i__ - 1], &cs, &sn, &r__);
		if (i__ < m) {
		    e[i__] = oldsn * r__;
		}
		d__1 = oldcs * r__;
		d__2 = d__[i__ - 1] * sn;
		splicingdlartg_(&d__1, &d__2, &oldcs, &oldsn, &d__[i__]);
		work[i__ - ll] = cs;
		work[i__ - ll + nm1] = -sn;
		work[i__ - ll + nm12] = oldcs;
		work[i__ - ll + nm13] = -oldsn;
/* L130: */
	    }
	    h__ = d__[ll] * cs;
	    d__[ll] = h__ * oldcs;
	    e[ll] = h__ * oldsn;

/*           Update singular vectors */

	    if (*ncvt > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "B", &i__1, ncvt, &work[nm12 + 1], &work[
			nm13 + 1], &vt[ll + vt_dim1], ldvt);
	    }
	    if (*nru > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("R", "V", "B", nru, &i__1, &work[1], &work[*n], &u[ll *
			 u_dim1 + 1], ldu);
	    }
	    if (*ncc > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "B", &i__1, ncc, &work[1], &work[*n], &c__[
			ll + c_dim1], ldc);
	    }

/*           Test convergence */

	    if ((d__1 = e[ll], abs(d__1)) <= thresh) {
		e[ll] = 0.;
	    }
	}
    } else {

/*        Use nonzero shift */

	if (idir == 1) {

/*           Chase bulge from top to bottom   
             Save cosines and sines for later singular vector updates */

	    f = ((d__1 = d__[ll], abs(d__1)) - shift) * (d_sign(&c_b49, &d__[
		    ll]) + shift / d__[ll]);
	    g = e[ll];
	    i__1 = m - 1;
	    for (i__ = ll; i__ <= i__1; ++i__) {
		splicingdlartg_(&f, &g, &cosr, &sinr, &r__);
		if (i__ > ll) {
		    e[i__ - 1] = r__;
		}
		f = cosr * d__[i__] + sinr * e[i__];
		e[i__] = cosr * e[i__] - sinr * d__[i__];
		g = sinr * d__[i__ + 1];
		d__[i__ + 1] = cosr * d__[i__ + 1];
		splicingdlartg_(&f, &g, &cosl, &sinl, &r__);
		d__[i__] = r__;
		f = cosl * e[i__] + sinl * d__[i__ + 1];
		d__[i__ + 1] = cosl * d__[i__ + 1] - sinl * e[i__];
		if (i__ < m - 1) {
		    g = sinl * e[i__ + 1];
		    e[i__ + 1] = cosl * e[i__ + 1];
		}
		work[i__ - ll + 1] = cosr;
		work[i__ - ll + 1 + nm1] = sinr;
		work[i__ - ll + 1 + nm12] = cosl;
		work[i__ - ll + 1 + nm13] = sinl;
/* L140: */
	    }
	    e[m - 1] = f;

/*           Update singular vectors */

	    if (*ncvt > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "F", &i__1, ncvt, &work[1], &work[*n], &vt[
			ll + vt_dim1], ldvt);
	    }
	    if (*nru > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("R", "V", "F", nru, &i__1, &work[nm12 + 1], &work[nm13 
			+ 1], &u[ll * u_dim1 + 1], ldu);
	    }
	    if (*ncc > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "F", &i__1, ncc, &work[nm12 + 1], &work[nm13 
			+ 1], &c__[ll + c_dim1], ldc);
	    }

/*           Test convergence */

	    if ((d__1 = e[m - 1], abs(d__1)) <= thresh) {
		e[m - 1] = 0.;
	    }

	} else {

/*           Chase bulge from bottom to top   
             Save cosines and sines for later singular vector updates */

	    f = ((d__1 = d__[m], abs(d__1)) - shift) * (d_sign(&c_b49, &d__[m]
		    ) + shift / d__[m]);
	    g = e[m - 1];
	    i__1 = ll + 1;
	    for (i__ = m; i__ >= i__1; --i__) {
		splicingdlartg_(&f, &g, &cosr, &sinr, &r__);
		if (i__ < m) {
		    e[i__] = r__;
		}
		f = cosr * d__[i__] + sinr * e[i__ - 1];
		e[i__ - 1] = cosr * e[i__ - 1] - sinr * d__[i__];
		g = sinr * d__[i__ - 1];
		d__[i__ - 1] = cosr * d__[i__ - 1];
		splicingdlartg_(&f, &g, &cosl, &sinl, &r__);
		d__[i__] = r__;
		f = cosl * e[i__ - 1] + sinl * d__[i__ - 1];
		d__[i__ - 1] = cosl * d__[i__ - 1] - sinl * e[i__ - 1];
		if (i__ > ll + 1) {
		    g = sinl * e[i__ - 2];
		    e[i__ - 2] = cosl * e[i__ - 2];
		}
		work[i__ - ll] = cosr;
		work[i__ - ll + nm1] = -sinr;
		work[i__ - ll + nm12] = cosl;
		work[i__ - ll + nm13] = -sinl;
/* L150: */
	    }
	    e[ll] = f;

/*           Test convergence */

	    if ((d__1 = e[ll], abs(d__1)) <= thresh) {
		e[ll] = 0.;
	    }

/*           Update singular vectors if desired */

	    if (*ncvt > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "B", &i__1, ncvt, &work[nm12 + 1], &work[
			nm13 + 1], &vt[ll + vt_dim1], ldvt);
	    }
	    if (*nru > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("R", "V", "B", nru, &i__1, &work[1], &work[*n], &u[ll *
			 u_dim1 + 1], ldu);
	    }
	    if (*ncc > 0) {
		i__1 = m - ll + 1;
		splicingdlasr_("L", "V", "B", &i__1, ncc, &work[1], &work[*n], &c__[
			ll + c_dim1], ldc);
	    }
	}
    }

/*     QR iteration finished, go back and check convergence */

    goto L60;

/*     All singular values converged, so make them positive */

L160:
    i__1 = *n;
    for (i__ = 1; i__ <= i__1; ++i__) {
	if (d__[i__] < 0.) {
	    d__[i__] = -d__[i__];

/*           Change sign of singular vectors, if desired */

	    if (*ncvt > 0) {
		splicingdscal_(ncvt, &c_b72, &vt[i__ + vt_dim1], ldvt);
	    }
	}
/* L170: */
    }

/*     Sort the singular values into decreasing order (insertion sort on   
       singular values, but only one transposition per singular vector) */

    i__1 = *n - 1;
    for (i__ = 1; i__ <= i__1; ++i__) {

/*        Scan for smallest D(I) */

	isub = 1;
	smin = d__[1];
	i__2 = *n + 1 - i__;
	for (j = 2; j <= i__2; ++j) {
	    if (d__[j] <= smin) {
		isub = j;
		smin = d__[j];
	    }
/* L180: */
	}
	if (isub != *n + 1 - i__) {

/*           Swap singular values and vectors */

	    d__[isub] = d__[*n + 1 - i__];
	    d__[*n + 1 - i__] = smin;
	    if (*ncvt > 0) {
		splicingdswap_(ncvt, &vt[isub + vt_dim1], ldvt, &vt[*n + 1 - i__ + 
			vt_dim1], ldvt);
	    }
	    if (*nru > 0) {
		splicingdswap_(nru, &u[isub * u_dim1 + 1], &c__1, &u[(*n + 1 - i__) * 
			u_dim1 + 1], &c__1);
	    }
	    if (*ncc > 0) {
		splicingdswap_(ncc, &c__[isub + c_dim1], ldc, &c__[*n + 1 - i__ + 
			c_dim1], ldc);
	    }
	}
/* L190: */
    }
    goto L220;

/*     Maximum number of iterations exceeded, failure to converge */

L200:
    *info = 0;
    i__1 = *n - 1;
    for (i__ = 1; i__ <= i__1; ++i__) {
	if (e[i__] != 0.) {
	    ++(*info);
	}
/* L210: */
    }
L220:
    return 0;

/*     End of DBDSQR */

} /* splicingdbdsqr_ */