/* Subroutine */ int dlapll_(integer *n, doublereal *x, integer *incx,
doublereal *y, integer *incy, doublereal *ssmin)
{
/* System generated locals */
integer i__1;
/* Local variables */
doublereal c__, a11, a12, a22, tau;
doublereal ssmax;
/* -- LAPACK auxiliary routine (version 3.2) -- */
/* November 2006 */
/* Purpose */
/* ======= */
/* Given two column vectors X and Y, let */
/* A = ( X Y ). */
/* The subroutine first computes the QR factorization of A = Q*R, */
/* and then computes the SVD of the 2-by-2 upper triangular matrix R. */
/* The smaller singular value of R is returned in SSMIN, which is used */
/* as the measurement of the linear dependency of the vectors X and Y. */
/* Arguments */
/* ========= */
/* N (input) INTEGER */
/* The length of the vectors X and Y. */
/* X (input/output) DOUBLE PRECISION array, */
/* dimension (1+(N-1)*INCX) */
/* On entry, X contains the N-vector X. */
/* On exit, X is overwritten. */
/* INCX (input) INTEGER */
/* The increment between successive elements of X. INCX > 0. */
/* Y (input/output) DOUBLE PRECISION array, */
/* dimension (1+(N-1)*INCY) */
/* On entry, Y contains the N-vector Y. */
/* On exit, Y is overwritten. */
/* INCY (input) INTEGER */
/* The increment between successive elements of Y. INCY > 0. */
/* SSMIN (output) DOUBLE PRECISION */
/* The smallest singular value of the N-by-2 matrix A = ( X Y ). */
/* ===================================================================== */
/* Quick return if possible */
/* Parameter adjustments */
--y;
--x;
/* Function Body */
if (*n <= 1) {
*ssmin = 0.;
return 0;
}
/* Compute the QR factorization of the N-by-2 matrix ( X Y ) */
dlarfg_(n, &x[1], &x[*incx + 1], incx, &tau);
a11 = x[1];
x[1] = 1.;
c__ = -tau * ddot_(n, &x[1], incx, &y[1], incy);
daxpy_(n, &c__, &x[1], incx, &y[1], incy);
i__1 = *n - 1;
dlarfg_(&i__1, &y[*incy + 1], &y[(*incy << 1) + 1], incy, &tau);
a12 = y[1];
a22 = y[*incy + 1];
/* Compute the SVD of 2-by-2 Upper triangular matrix. */
dlas2_(&a11, &a12, &a22, ssmin, &ssmax);
return 0;
/* End of DLAPLL */
} /* dlapll_ */
/* Subroutine */ int dlasq1_(integer *n, doublereal *d__, doublereal *e,
doublereal *work, integer *info)
{
/* System generated locals */
integer i__1, i__2;
doublereal d__1, d__2, d__3;
/* Builtin functions */
double sqrt(doublereal);
/* Local variables */
integer i__;
doublereal eps;
extern /* Subroutine */ int dlas2_(doublereal *, doublereal *, doublereal
*, doublereal *, doublereal *);
doublereal scale;
integer iinfo;
doublereal sigmn;
extern /* Subroutine */ int dcopy_(integer *, doublereal *, integer *,
doublereal *, integer *);
doublereal sigmx;
extern /* Subroutine */ int dlasq2_(integer *, doublereal *, integer *);
extern doublereal dlamch_(char *);
extern /* Subroutine */ int dlascl_(char *, integer *, integer *,
doublereal *, doublereal *, integer *, integer *, doublereal *,
integer *, integer *);
doublereal safmin;
extern /* Subroutine */ int xerbla_(char *, integer *), dlasrt_(
char *, integer *, doublereal *, integer *);
/* -- LAPACK routine (version 3.1) -- */
/* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. */
/* November 2006 */
/* .. Scalar Arguments .. */
/* .. */
/* .. Array Arguments .. */
/* .. */
/* Purpose */
/* ======= */
/* DLASQ1 computes the singular values of a real N-by-N bidiagonal */
/* matrix with diagonal D and off-diagonal E. The singular values */
/* are computed to high relative accuracy, in the absence of */
/* denormalization, underflow and overflow. The algorithm was first */
/* presented in */
/* "Accurate singular values and differential qd algorithms" by K. V. */
/* Fernando and B. N. Parlett, Numer. Math., Vol-67, No. 2, pp. 191-230, */
/* 1994, */
/* and the present implementation is described in "An implementation of */
/* the dqds Algorithm (Positive Case)", LAPACK Working Note. */
/* Arguments */
/* ========= */
/* N (input) INTEGER */
/* The number of rows and columns in the matrix. N >= 0. */
/* D (input/output) DOUBLE PRECISION array, dimension (N) */
/* On entry, D contains the diagonal elements of the */
/* bidiagonal matrix whose SVD is desired. On normal exit, */
/* D contains the singular values in decreasing order. */
/* E (input/output) DOUBLE PRECISION array, dimension (N) */
/* On entry, elements E(1:N-1) contain the off-diagonal elements */
/* of the bidiagonal matrix whose SVD is desired. */
/* On exit, E is overwritten. */
/* 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: the algorithm failed */
/* = 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) */
/* ===================================================================== */
/* .. Parameters .. */
/* .. */
/* .. Local Scalars .. */
/* .. */
/* .. External Subroutines .. */
/* .. */
/* .. External Functions .. */
/* .. */
/* .. Intrinsic Functions .. */
/* .. */
/* .. Executable Statements .. */
/* Parameter adjustments */
--work;
--e;
--d__;
/* Function Body */
*info = 0;
if (*n < 0) {
*info = -2;
i__1 = -(*info);
xerbla_("DLASQ1", &i__1);
return 0;
} else if (*n == 0) {
return 0;
} else if (*n == 1) {
d__[1] = abs(d__[1]);
return 0;
} else if (*n == 2) {
dlas2_(&d__[1], &e[1], &d__[2], &sigmn, &sigmx);
d__[1] = sigmx;
d__[2] = sigmn;
return 0;
}
/* Estimate the largest singular value. */
sigmx = 0.;
i__1 = *n - 1;
for (i__ = 1; i__ <= i__1; ++i__) {
d__[i__] = (d__1 = d__[i__], abs(d__1));
/* Computing MAX */
d__2 = sigmx, d__3 = (d__1 = e[i__], abs(d__1));
sigmx = max(d__2,d__3);
/* L10: */
}
d__[*n] = (d__1 = d__[*n], abs(d__1));
/* Early return if SIGMX is zero (matrix is already diagonal). */
if (sigmx == 0.) {
dlasrt_("D", n, &d__[1], &iinfo);
return 0;
}
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
/* Computing MAX */
d__1 = sigmx, d__2 = d__[i__];
sigmx = max(d__1,d__2);
/* L20: */
}
/* Copy D and E into WORK (in the Z format) and scale (squaring the */
/* input data makes scaling by a power of the radix pointless). */
eps = dlamch_("Precision");
safmin = dlamch_("Safe minimum");
scale = sqrt(eps / safmin);
dcopy_(n, &d__[1], &c__1, &work[1], &c__2);
i__1 = *n - 1;
dcopy_(&i__1, &e[1], &c__1, &work[2], &c__2);
i__1 = (*n << 1) - 1;
i__2 = (*n << 1) - 1;
dlascl_("G", &c__0, &c__0, &sigmx, &scale, &i__1, &c__1, &work[1], &i__2,
&iinfo);
/* Compute the q's and e's. */
i__1 = (*n << 1) - 1;
for (i__ = 1; i__ <= i__1; ++i__) {
/* Computing 2nd power */
d__1 = work[i__];
work[i__] = d__1 * d__1;
/* L30: */
}
work[*n * 2] = 0.;
dlasq2_(n, &work[1], info);
if (*info == 0) {
i__1 = *n;
for (i__ = 1; i__ <= i__1; ++i__) {
d__[i__] = sqrt(work[i__]);
/* L40: */
}
dlascl_("G", &c__0, &c__0, &scale, &sigmx, n, &c__1, &d__[1], n, &
iinfo);
}
return 0;
/* End of DLASQ1 */
} /* dlasq1_ */
/* Subroutine */ int dbdsqr_(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 */
doublereal f, g, h__;
integer i__, j, m;
doublereal r__, cs;
integer ll;
doublereal sn, mu;
integer nm1, nm12, nm13, lll;
doublereal eps, sll, tol, abse;
integer idir;
doublereal abss;
integer oldm;
doublereal cosl;
integer isub, iter;
doublereal unfl, sinl, cosr, smin, smax, sinr;
extern /* Subroutine */ int drot_(integer *, doublereal *, integer *,
doublereal *, integer *, doublereal *, doublereal *), dlas2_(
doublereal *, doublereal *, doublereal *, doublereal *,
doublereal *), dscal_(integer *, doublereal *, doublereal *,
integer *);
extern logical lsame_(char *, char *);
doublereal oldcs;
extern /* Subroutine */ int dlasr_(char *, char *, char *, integer *,
integer *, doublereal *, doublereal *, doublereal *, integer *);
integer oldll;
doublereal shift, sigmn, oldsn;
extern /* Subroutine */ int dswap_(integer *, doublereal *, integer *,
doublereal *, integer *);
integer maxit;
doublereal sminl, sigmx;
logical lower;
extern /* Subroutine */ int dlasq1_(integer *, doublereal *, doublereal *,
doublereal *, integer *), dlasv2_(doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *);
extern doublereal dlamch_(char *);
extern /* Subroutine */ int dlartg_(doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *), xerbla_(char *,
integer *);
doublereal sminoa, thresh;
logical rotate;
doublereal tolmul;
/* -- LAPACK routine (version 3.1.1) -- */
/* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. */
/* January 2007 */
/* .. Scalar Arguments .. */
/* .. */
/* .. Array Arguments .. */
/* .. */
/* 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 (2*N) */
/* if NCVT = NRU = NCC = 0, (max(1, 4*N)) otherwise */
/* INFO (output) INTEGER */
/* = 0: successful exit */
/* < 0: If INFO = -i, the i-th argument had an illegal value */
/* > 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. */
/* ===================================================================== */
/* .. Parameters .. */
/* .. */
/* .. Local Scalars .. */
/* .. */
/* .. External Functions .. */
/* .. */
/* .. External Subroutines .. */
/* .. */
/* .. Intrinsic Functions .. */
/* .. */
/* .. Executable Statements .. */
/* 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 = lsame_(uplo, "L");
if (! lsame_(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);
xerbla_("DBDSQR", &i__1);
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) {
dlasq1_(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 = dlamch_("Epsilon");
unfl = dlamch_("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__) {
dlartg_(&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) {
dlasr_("R", "V", "F", nru, n, &work[1], &work[*n], &u[u_offset],
ldu);
}
if (*ncc > 0) {
dlasr_("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 */
dlasv2_(&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) {
drot_(ncvt, &vt[m - 1 + vt_dim1], ldvt, &vt[m + vt_dim1], ldvt, &
cosr, &sinr);
}
if (*nru > 0) {
drot_(nru, &u[(m - 1) * u_dim1 + 1], &c__1, &u[m * u_dim1 + 1], &
c__1, &cosl, &sinl);
}
if (*ncc > 0) {
drot_(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));
dlas2_(&d__[m - 1], &e[m - 1], &d__[m], &shift, &r__);
} else {
sll = (d__1 = d__[m], abs(d__1));
dlas2_(&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;
dlartg_(&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;
dlartg_(&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;
dlasr_("L", "V", "F", &i__1, ncvt, &work[1], &work[*n], &vt[
ll + vt_dim1], ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
dlasr_("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;
dlasr_("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;
dlartg_(&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;
dlartg_(&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;
dlasr_("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;
dlasr_("R", "V", "B", nru, &i__1, &work[1], &work[*n], &u[ll *
u_dim1 + 1], ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
dlasr_("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__) {
dlartg_(&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];
dlartg_(&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;
dlasr_("L", "V", "F", &i__1, ncvt, &work[1], &work[*n], &vt[
ll + vt_dim1], ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
dlasr_("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;
dlasr_("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__) {
dlartg_(&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];
dlartg_(&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;
dlasr_("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;
dlasr_("R", "V", "B", nru, &i__1, &work[1], &work[*n], &u[ll *
u_dim1 + 1], ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
dlasr_("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) {
dscal_(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) {
dswap_(ncvt, &vt[isub + vt_dim1], ldvt, &vt[*n + 1 - i__ +
vt_dim1], ldvt);
}
if (*nru > 0) {
dswap_(nru, &u[isub * u_dim1 + 1], &c__1, &u[(*n + 1 - i__) *
u_dim1 + 1], &c__1);
}
if (*ncc > 0) {
dswap_(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 */
} /* dbdsqr_ */
/* Subroutine */ int zbdsqr_(char *uplo, integer *n, integer *ncvt, integer *
nru, integer *ncc, doublereal *d__, doublereal *e, doublecomplex *vt,
integer *ldvt, doublecomplex *u, integer *ldu, doublecomplex *c__,
integer *ldc, doublereal *rwork, 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 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 dlas2_(doublereal *, doublereal *, doublereal
*, doublereal *, doublereal *);
static doublereal f, g, h__;
static integer i__, j, m;
static doublereal r__;
extern logical lsame_(char *, char *);
static doublereal oldcs;
static integer oldll;
static doublereal shift, sigmn, oldsn;
static integer maxit;
static doublereal sminl, sigmx;
static logical lower;
extern /* Subroutine */ int zlasr_(char *, char *, char *, integer *,
integer *, doublereal *, doublereal *, doublecomplex *, integer *), zdrot_(integer *, doublecomplex *,
integer *, doublecomplex *, integer *, doublereal *, doublereal *)
, zswap_(integer *, doublecomplex *, integer *, doublecomplex *,
integer *), dlasq1_(integer *, doublereal *, doublereal *,
doublereal *, integer *), dlasv2_(doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *);
static doublereal cs;
static integer ll;
extern doublereal dlamch_(char *);
static doublereal sn, mu;
extern /* Subroutine */ int dlartg_(doublereal *, doublereal *,
doublereal *, doublereal *, doublereal *), xerbla_(char *,
integer *), zdscal_(integer *, doublereal *,
doublecomplex *, integer *);
static doublereal sminoa, thresh;
static logical rotate;
static doublereal sminlo;
static integer nm1;
static doublereal tolmul;
static integer nm12, nm13, lll;
static doublereal eps, sll, tol;
#define c___subscr(a_1,a_2) (a_2)*c_dim1 + a_1
#define c___ref(a_1,a_2) c__[c___subscr(a_1,a_2)]
#define u_subscr(a_1,a_2) (a_2)*u_dim1 + a_1
#define u_ref(a_1,a_2) u[u_subscr(a_1,a_2)]
#define vt_subscr(a_1,a_2) (a_2)*vt_dim1 + a_1
#define vt_ref(a_1,a_2) vt[vt_subscr(a_1,a_2)]
/* -- LAPACK routine (version 3.0) --
Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd.,
Courant Institute, Argonne National Lab, and Rice University
October 31, 1999
Purpose
=======
ZBDSQR computes the singular value decomposition (SVD) of a real
N-by-N (upper or lower) bidiagonal matrix B: B = Q * S * P' (P'
denotes the transpose of P), where S is a diagonal matrix with
non-negative diagonal elements (the singular values of B), and Q
and P are orthogonal matrices.
The routine computes S, and optionally computes U * Q, P' * VT,
or Q' * C, for given complex input matrices U, VT, and 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)
On entry, the elements of E contain the
offdiagonal elements of of the bidiagonal matrix whose SVD
is desired. On normal exit (INFO = 0), E is destroyed.
If the algorithm does not converge (INFO > 0), D and E
will contain the diagonal and superdiagonal elements of a
bidiagonal matrix orthogonally equivalent to the one given
as input. E(N) is used for workspace.
VT (input/output) COMPLEX*16 array, dimension (LDVT, NCVT)
On entry, an N-by-NCVT matrix VT.
On exit, VT is overwritten by P' * VT.
VT is 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) COMPLEX*16 array, dimension (LDU, N)
On entry, an NRU-by-N matrix U.
On exit, U is overwritten by U * Q.
U is not referenced if NRU = 0.
LDU (input) INTEGER
The leading dimension of the array U. LDU >= max(1,NRU).
C (input/output) COMPLEX*16 array, dimension (LDC, NCC)
On entry, an N-by-NCC matrix C.
On exit, C is overwritten by Q' * C.
C is 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.
RWORK (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: 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 * 1;
vt -= vt_offset;
u_dim1 = *ldu;
u_offset = 1 + u_dim1 * 1;
u -= u_offset;
c_dim1 = *ldc;
c_offset = 1 + c_dim1 * 1;
c__ -= c_offset;
--rwork;
/* Function Body */
*info = 0;
lower = lsame_(uplo, "L");
if (! lsame_(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);
xerbla_("ZBDSQR", &i__1);
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) {
dlasq1_(n, &d__[1], &e[1], &rwork[1], info);
return 0;
}
nm1 = *n - 1;
nm12 = nm1 + nm1;
nm13 = nm12 + nm1;
idir = 0;
/* Get machine constants */
eps = dlamch_("Epsilon");
unfl = dlamch_("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__) {
dlartg_(&d__[i__], &e[i__], &cs, &sn, &r__);
d__[i__] = r__;
e[i__] = sn * d__[i__ + 1];
d__[i__ + 1] = cs * d__[i__ + 1];
rwork[i__] = cs;
rwork[nm1 + i__] = sn;
/* L10: */
}
/* Update singular vectors if desired */
if (*nru > 0) {
zlasr_("R", "V", "F", nru, n, &rwork[1], &rwork[*n], &u[u_offset],
ldu);
}
if (*ncc > 0) {
zlasr_("L", "V", "F", n, ncc, &rwork[1], &rwork[*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 */
dlasv2_(&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) {
zdrot_(ncvt, &vt_ref(m - 1, 1), ldvt, &vt_ref(m, 1), ldvt, &cosr,
&sinr);
}
if (*nru > 0) {
zdrot_(nru, &u_ref(1, m - 1), &c__1, &u_ref(1, m), &c__1, &cosl, &
sinl);
}
if (*ncc > 0) {
zdrot_(ncc, &c___ref(m - 1, 1), ldc, &c___ref(m, 1), 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;
}
sminlo = sminl;
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;
}
sminlo = sminl;
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));
dlas2_(&d__[m - 1], &e[m - 1], &d__[m], &shift, &r__);
} else {
sll = (d__1 = d__[m], abs(d__1));
dlas2_(&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;
dlartg_(&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;
dlartg_(&d__1, &d__2, &oldcs, &oldsn, &d__[i__]);
rwork[i__ - ll + 1] = cs;
rwork[i__ - ll + 1 + nm1] = sn;
rwork[i__ - ll + 1 + nm12] = oldcs;
rwork[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;
zlasr_("L", "V", "F", &i__1, ncvt, &rwork[1], &rwork[*n], &
vt_ref(ll, 1), ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
zlasr_("R", "V", "F", nru, &i__1, &rwork[nm12 + 1], &rwork[
nm13 + 1], &u_ref(1, ll), ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
zlasr_("L", "V", "F", &i__1, ncc, &rwork[nm12 + 1], &rwork[
nm13 + 1], &c___ref(ll, 1), 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;
dlartg_(&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;
dlartg_(&d__1, &d__2, &oldcs, &oldsn, &d__[i__]);
rwork[i__ - ll] = cs;
rwork[i__ - ll + nm1] = -sn;
rwork[i__ - ll + nm12] = oldcs;
rwork[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;
zlasr_("L", "V", "B", &i__1, ncvt, &rwork[nm12 + 1], &rwork[
nm13 + 1], &vt_ref(ll, 1), ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
zlasr_("R", "V", "B", nru, &i__1, &rwork[1], &rwork[*n], &
u_ref(1, ll), ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
zlasr_("L", "V", "B", &i__1, ncc, &rwork[1], &rwork[*n], &
c___ref(ll, 1), 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__) {
dlartg_(&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];
dlartg_(&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];
}
rwork[i__ - ll + 1] = cosr;
rwork[i__ - ll + 1 + nm1] = sinr;
rwork[i__ - ll + 1 + nm12] = cosl;
rwork[i__ - ll + 1 + nm13] = sinl;
/* L140: */
}
e[m - 1] = f;
/* Update singular vectors */
if (*ncvt > 0) {
i__1 = m - ll + 1;
zlasr_("L", "V", "F", &i__1, ncvt, &rwork[1], &rwork[*n], &
vt_ref(ll, 1), ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
zlasr_("R", "V", "F", nru, &i__1, &rwork[nm12 + 1], &rwork[
nm13 + 1], &u_ref(1, ll), ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
zlasr_("L", "V", "F", &i__1, ncc, &rwork[nm12 + 1], &rwork[
nm13 + 1], &c___ref(ll, 1), 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__) {
dlartg_(&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];
dlartg_(&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];
}
rwork[i__ - ll] = cosr;
rwork[i__ - ll + nm1] = -sinr;
rwork[i__ - ll + nm12] = cosl;
rwork[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;
zlasr_("L", "V", "B", &i__1, ncvt, &rwork[nm12 + 1], &rwork[
nm13 + 1], &vt_ref(ll, 1), ldvt);
}
if (*nru > 0) {
i__1 = m - ll + 1;
zlasr_("R", "V", "B", nru, &i__1, &rwork[1], &rwork[*n], &
u_ref(1, ll), ldu);
}
if (*ncc > 0) {
i__1 = m - ll + 1;
zlasr_("L", "V", "B", &i__1, ncc, &rwork[1], &rwork[*n], &
c___ref(ll, 1), 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) {
zdscal_(ncvt, &c_b72, &vt_ref(i__, 1), 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) {
zswap_(ncvt, &vt_ref(isub, 1), ldvt, &vt_ref(*n + 1 - i__, 1),
ldvt);
}
if (*nru > 0) {
zswap_(nru, &u_ref(1, isub), &c__1, &u_ref(1, *n + 1 - i__), &
c__1);
}
if (*ncc > 0) {
zswap_(ncc, &c___ref(isub, 1), ldc, &c___ref(*n + 1 - i__, 1),
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 ZBDSQR */
} /* zbdsqr_ */
/* Subroutine */ int zlapll_(integer *n, doublecomplex *x, integer *incx,
doublecomplex *y, integer *incy, doublereal *ssmin)
{
/* System generated locals */
integer i__1;
doublereal d__1, d__2, d__3;
doublecomplex z__1, z__2, z__3, z__4;
/* Builtin functions */
void d_cnjg(doublecomplex *, doublecomplex *);
double z_abs(doublecomplex *);
/* Local variables */
static doublecomplex c__, a11, a12, a22, tau;
extern /* Subroutine */ int dlas2_(doublereal *, doublereal *, doublereal
*, doublereal *, doublereal *);
extern /* Double Complex */ VOID zdotc_(doublecomplex *, integer *,
doublecomplex *, integer *, doublecomplex *, integer *);
static doublereal ssmax;
extern /* Subroutine */ int zaxpy_(integer *, doublecomplex *,
doublecomplex *, integer *, doublecomplex *, integer *), zlarfg_(
integer *, doublecomplex *, doublecomplex *, integer *,
doublecomplex *);
/* -- LAPACK auxiliary routine (version 3.0) -- */
/* Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., */
/* Courant Institute, Argonne National Lab, and Rice University */
/* September 30, 1994 */
/* .. Scalar Arguments .. */
/* .. */
/* .. Array Arguments .. */
/* .. */
/* Purpose */
/* ======= */
/* Given two column vectors X and Y, let */
/* A = ( X Y ). */
/* The subroutine first computes the QR factorization of A = Q*R, */
/* and then computes the SVD of the 2-by-2 upper triangular matrix R. */
/* The smaller singular value of R is returned in SSMIN, which is used */
/* as the measurement of the linear dependency of the vectors X and Y. */
/* Arguments */
/* ========= */
/* N (input) INTEGER */
/* The length of the vectors X and Y. */
/* X (input/output) COMPLEX*16 array, dimension (1+(N-1)*INCX) */
/* On entry, X contains the N-vector X. */
/* On exit, X is overwritten. */
/* INCX (input) INTEGER */
/* The increment between successive elements of X. INCX > 0. */
/* Y (input/output) COMPLEX*16 array, dimension (1+(N-1)*INCY) */
/* On entry, Y contains the N-vector Y. */
/* On exit, Y is overwritten. */
/* INCY (input) INTEGER */
/* The increment between successive elements of Y. INCY > 0. */
/* SSMIN (output) DOUBLE PRECISION */
/* The smallest singular value of the N-by-2 matrix A = ( X Y ). */
/* ===================================================================== */
/* .. Parameters .. */
/* .. */
/* .. Local Scalars .. */
/* .. */
/* .. Intrinsic Functions .. */
/* .. */
/* .. External Functions .. */
/* .. */
/* .. External Subroutines .. */
/* .. */
/* .. Executable Statements .. */
/* Quick return if possible */
/* Parameter adjustments */
--y;
--x;
/* Function Body */
if (*n <= 1) {
*ssmin = 0.;
return 0;
}
/* Compute the QR factorization of the N-by-2 matrix ( X Y ) */
zlarfg_(n, &x[1], &x[*incx + 1], incx, &tau);
a11.r = x[1].r, a11.i = x[1].i;
x[1].r = 1., x[1].i = 0.;
d_cnjg(&z__3, &tau);
z__2.r = -z__3.r, z__2.i = -z__3.i;
zdotc_(&z__4, n, &x[1], incx, &y[1], incy);
z__1.r = z__2.r * z__4.r - z__2.i * z__4.i, z__1.i = z__2.r * z__4.i +
z__2.i * z__4.r;
c__.r = z__1.r, c__.i = z__1.i;
zaxpy_(n, &c__, &x[1], incx, &y[1], incy);
i__1 = *n - 1;
zlarfg_(&i__1, &y[*incy + 1], &y[(*incy << 1) + 1], incy, &tau);
a12.r = y[1].r, a12.i = y[1].i;
i__1 = *incy + 1;
a22.r = y[i__1].r, a22.i = y[i__1].i;
/* Compute the SVD of 2-by-2 Upper triangular matrix. */
d__1 = z_abs(&a11);
d__2 = z_abs(&a12);
d__3 = z_abs(&a22);
dlas2_(&d__1, &d__2, &d__3, ssmin, &ssmax);
return 0;
/* End of ZLAPLL */
} /* zlapll_ */