#include "clapack.h" /* Table of constant values */ static integer c__1 = 1; static doublereal c_b30 = 0.; /* Subroutine */ int dlasd2_(integer *nl, integer *nr, integer *sqre, integer *k, doublereal *d__, doublereal *z__, doublereal *alpha, doublereal * beta, doublereal *u, integer *ldu, doublereal *vt, integer *ldvt, doublereal *dsigma, doublereal *u2, integer *ldu2, doublereal *vt2, integer *ldvt2, integer *idxp, integer *idx, integer *idxc, integer * idxq, integer *coltyp, integer *info) { /* System generated locals */ integer u_dim1, u_offset, u2_dim1, u2_offset, vt_dim1, vt_offset, vt2_dim1, vt2_offset, i__1; doublereal d__1, d__2; /* Local variables */ doublereal c__; integer i__, j, m, n; doublereal s; integer k2; doublereal z1; integer ct, jp; doublereal eps, tau, tol; integer psm[4], nlp1, nlp2, idxi, idxj; extern /* Subroutine */ int drot_(integer *, doublereal *, integer *, doublereal *, integer *, doublereal *, doublereal *); integer ctot[4], idxjp; extern /* Subroutine */ int dcopy_(integer *, doublereal *, integer *, doublereal *, integer *); integer jprev; extern doublereal dlapy2_(doublereal *, doublereal *), dlamch_(char *); extern /* Subroutine */ int dlamrg_(integer *, integer *, doublereal *, integer *, integer *, integer *), dlacpy_(char *, integer *, integer *, doublereal *, integer *, doublereal *, integer *), dlaset_(char *, integer *, integer *, doublereal *, doublereal *, doublereal *, integer *), xerbla_(char *, integer *); doublereal hlftol; /* -- LAPACK auxiliary routine (version 3.1) -- */ /* Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. */ /* November 2006 */ /* .. Scalar Arguments .. */ /* .. */ /* .. Array Arguments .. */ /* .. */ /* Purpose */ /* ======= */ /* DLASD2 merges the two sets of singular values together into a single */ /* sorted set. Then it tries to deflate the size of the problem. */ /* There are two ways in which deflation can occur: when two or more */ /* singular values are close together or if there is a tiny entry in the */ /* Z vector. For each such occurrence the order of the related secular */ /* equation problem is reduced by one. */ /* DLASD2 is called from DLASD1. */ /* Arguments */ /* ========= */ /* NL (input) INTEGER */ /* The row dimension of the upper block. NL >= 1. */ /* NR (input) INTEGER */ /* The row dimension of the lower block. NR >= 1. */ /* SQRE (input) INTEGER */ /* = 0: the lower block is an NR-by-NR square matrix. */ /* = 1: the lower block is an NR-by-(NR+1) rectangular matrix. */ /* The bidiagonal matrix has N = NL + NR + 1 rows and */ /* M = N + SQRE >= N columns. */ /* K (output) INTEGER */ /* Contains the dimension of the non-deflated matrix, */ /* This is the order of the related secular equation. 1 <= K <=N. */ /* D (input/output) DOUBLE PRECISION array, dimension(N) */ /* On entry D contains the singular values of the two submatrices */ /* to be combined. On exit D contains the trailing (N-K) updated */ /* singular values (those which were deflated) sorted into */ /* increasing order. */ /* Z (output) DOUBLE PRECISION array, dimension(N) */ /* On exit Z contains the updating row vector in the secular */ /* equation. */ /* ALPHA (input) DOUBLE PRECISION */ /* Contains the diagonal element associated with the added row. */ /* BETA (input) DOUBLE PRECISION */ /* Contains the off-diagonal element associated with the added */ /* row. */ /* U (input/output) DOUBLE PRECISION array, dimension(LDU,N) */ /* On entry U contains the left singular vectors of two */ /* submatrices in the two square blocks with corners at (1,1), */ /* (NL, NL), and (NL+2, NL+2), (N,N). */ /* On exit U contains the trailing (N-K) updated left singular */ /* vectors (those which were deflated) in its last N-K columns. */ /* LDU (input) INTEGER */ /* The leading dimension of the array U. LDU >= N. */ /* VT (input/output) DOUBLE PRECISION array, dimension(LDVT,M) */ /* On entry VT' contains the right singular vectors of two */ /* submatrices in the two square blocks with corners at (1,1), */ /* (NL+1, NL+1), and (NL+2, NL+2), (M,M). */ /* On exit VT' contains the trailing (N-K) updated right singular */ /* vectors (those which were deflated) in its last N-K columns. */ /* In case SQRE =1, the last row of VT spans the right null */ /* space. */ /* LDVT (input) INTEGER */ /* The leading dimension of the array VT. LDVT >= M. */ /* DSIGMA (output) DOUBLE PRECISION array, dimension (N) */ /* Contains a copy of the diagonal elements (K-1 singular values */ /* and one zero) in the secular equation. */ /* U2 (output) DOUBLE PRECISION array, dimension(LDU2,N) */ /* Contains a copy of the first K-1 left singular vectors which */ /* will be used by DLASD3 in a matrix multiply (DGEMM) to solve */ /* for the new left singular vectors. U2 is arranged into four */ /* blocks. The first block contains a column with 1 at NL+1 and */ /* zero everywhere else; the second block contains non-zero */ /* entries only at and above NL; the third contains non-zero */ /* entries only below NL+1; and the fourth is dense. */ /* LDU2 (input) INTEGER */ /* The leading dimension of the array U2. LDU2 >= N. */ /* VT2 (output) DOUBLE PRECISION array, dimension(LDVT2,N) */ /* VT2' contains a copy of the first K right singular vectors */ /* which will be used by DLASD3 in a matrix multiply (DGEMM) to */ /* solve for the new right singular vectors. VT2 is arranged into */ /* three blocks. The first block contains a row that corresponds */ /* to the special 0 diagonal element in SIGMA; the second block */ /* contains non-zeros only at and before NL +1; the third block */ /* contains non-zeros only at and after NL +2. */ /* LDVT2 (input) INTEGER */ /* The leading dimension of the array VT2. LDVT2 >= M. */ /* IDXP (workspace) INTEGER array dimension(N) */ /* This will contain the permutation used to place deflated */ /* values of D at the end of the array. On output IDXP(2:K) */ /* points to the nondeflated D-values and IDXP(K+1:N) */ /* points to the deflated singular values. */ /* IDX (workspace) INTEGER array dimension(N) */ /* This will contain the permutation used to sort the contents of */ /* D into ascending order. */ /* IDXC (output) INTEGER array dimension(N) */ /* This will contain the permutation used to arrange the columns */ /* of the deflated U matrix into three groups: the first group */ /* contains non-zero entries only at and above NL, the second */ /* contains non-zero entries only below NL+2, and the third is */ /* dense. */ /* IDXQ (input/output) INTEGER array dimension(N) */ /* This contains the permutation which separately sorts the two */ /* sub-problems in D into ascending order. Note that entries in */ /* the first hlaf of this permutation must first be moved one */ /* position backward; and entries in the second half */ /* must first have NL+1 added to their values. */ /* COLTYP (workspace/output) INTEGER array dimension(N) */ /* As workspace, this will contain a label which will indicate */ /* which of the following types a column in the U2 matrix or a */ /* row in the VT2 matrix is: */ /* 1 : non-zero in the upper half only */ /* 2 : non-zero in the lower half only */ /* 3 : dense */ /* 4 : deflated */ /* On exit, it is an array of dimension 4, with COLTYP(I) being */ /* the dimension of the I-th type columns. */ /* INFO (output) INTEGER */ /* = 0: successful exit. */ /* < 0: if INFO = -i, the i-th argument had an illegal value. */ /* Further Details */ /* =============== */ /* Based on contributions by */ /* Ming Gu and Huan Ren, Computer Science Division, University of */ /* California at Berkeley, USA */ /* ===================================================================== */ /* .. Parameters .. */ /* .. */ /* .. Local Arrays .. */ /* .. */ /* .. Local Scalars .. */ /* .. */ /* .. External Functions .. */ /* .. */ /* .. External Subroutines .. */ /* .. */ /* .. Intrinsic Functions .. */ /* .. */ /* .. Executable Statements .. */ /* Test the input parameters. */ /* Parameter adjustments */ --d__; --z__; u_dim1 = *ldu; u_offset = 1 + u_dim1; u -= u_offset; vt_dim1 = *ldvt; vt_offset = 1 + vt_dim1; vt -= vt_offset; --dsigma; u2_dim1 = *ldu2; u2_offset = 1 + u2_dim1; u2 -= u2_offset; vt2_dim1 = *ldvt2; vt2_offset = 1 + vt2_dim1; vt2 -= vt2_offset; --idxp; --idx; --idxc; --idxq; --coltyp; /* Function Body */ *info = 0; if (*nl < 1) { *info = -1; } else if (*nr < 1) { *info = -2; } else if (*sqre != 1 && *sqre != 0) { *info = -3; } n = *nl + *nr + 1; m = n + *sqre; if (*ldu < n) { *info = -10; } else if (*ldvt < m) { *info = -12; } else if (*ldu2 < n) { *info = -15; } else if (*ldvt2 < m) { *info = -17; } if (*info != 0) { i__1 = -(*info); xerbla_("DLASD2", &i__1); return 0; } nlp1 = *nl + 1; nlp2 = *nl + 2; /* Generate the first part of the vector Z; and move the singular */ /* values in the first part of D one position backward. */ z1 = *alpha * vt[nlp1 + nlp1 * vt_dim1]; z__[1] = z1; for (i__ = *nl; i__ >= 1; --i__) { z__[i__ + 1] = *alpha * vt[i__ + nlp1 * vt_dim1]; d__[i__ + 1] = d__[i__]; idxq[i__ + 1] = idxq[i__] + 1; /* L10: */ } /* Generate the second part of the vector Z. */ i__1 = m; for (i__ = nlp2; i__ <= i__1; ++i__) { z__[i__] = *beta * vt[i__ + nlp2 * vt_dim1]; /* L20: */ } /* Initialize some reference arrays. */ i__1 = nlp1; for (i__ = 2; i__ <= i__1; ++i__) { coltyp[i__] = 1; /* L30: */ } i__1 = n; for (i__ = nlp2; i__ <= i__1; ++i__) { coltyp[i__] = 2; /* L40: */ } /* Sort the singular values into increasing order */ i__1 = n; for (i__ = nlp2; i__ <= i__1; ++i__) { idxq[i__] += nlp1; /* L50: */ } /* DSIGMA, IDXC, IDXC, and the first column of U2 */ /* are used as storage space. */ i__1 = n; for (i__ = 2; i__ <= i__1; ++i__) { dsigma[i__] = d__[idxq[i__]]; u2[i__ + u2_dim1] = z__[idxq[i__]]; idxc[i__] = coltyp[idxq[i__]]; /* L60: */ } dlamrg_(nl, nr, &dsigma[2], &c__1, &c__1, &idx[2]); i__1 = n; for (i__ = 2; i__ <= i__1; ++i__) { idxi = idx[i__] + 1; d__[i__] = dsigma[idxi]; z__[i__] = u2[idxi + u2_dim1]; coltyp[i__] = idxc[idxi]; /* L70: */ } /* Calculate the allowable deflation tolerance */ eps = dlamch_("Epsilon"); /* Computing MAX */ d__1 = abs(*alpha), d__2 = abs(*beta); tol = max(d__1,d__2); /* Computing MAX */ d__2 = (d__1 = d__[n], abs(d__1)); tol = eps * 8. * max(d__2,tol); /* There are 2 kinds of deflation -- first a value in the z-vector */ /* is small, second two (or more) singular values are very close */ /* together (their difference is small). */ /* If the value in the z-vector is small, we simply permute the */ /* array so that the corresponding singular value is moved to the */ /* end. */ /* If two values in the D-vector are close, we perform a two-sided */ /* rotation designed to make one of the corresponding z-vector */ /* entries zero, and then permute the array so that the deflated */ /* singular value is moved to the end. */ /* If there are multiple singular values then the problem deflates. */ /* Here the number of equal singular values are found. As each equal */ /* singular value is found, an elementary reflector is computed to */ /* rotate the corresponding singular subspace so that the */ /* corresponding components of Z are zero in this new basis. */ *k = 1; k2 = n + 1; i__1 = n; for (j = 2; j <= i__1; ++j) { if ((d__1 = z__[j], abs(d__1)) <= tol) { /* Deflate due to small z component. */ --k2; idxp[k2] = j; coltyp[j] = 4; if (j == n) { goto L120; } } else { jprev = j; goto L90; } /* L80: */ } L90: j = jprev; L100: ++j; if (j > n) { goto L110; } if ((d__1 = z__[j], abs(d__1)) <= tol) { /* Deflate due to small z component. */ --k2; idxp[k2] = j; coltyp[j] = 4; } else { /* Check if singular values are close enough to allow deflation. */ if ((d__1 = d__[j] - d__[jprev], abs(d__1)) <= tol) { /* Deflation is possible. */ s = z__[jprev]; c__ = z__[j]; /* Find sqrt(a**2+b**2) without overflow or */ /* destructive underflow. */ tau = dlapy2_(&c__, &s); c__ /= tau; s = -s / tau; z__[j] = tau; z__[jprev] = 0.; /* Apply back the Givens rotation to the left and right */ /* singular vector matrices. */ idxjp = idxq[idx[jprev] + 1]; idxj = idxq[idx[j] + 1]; if (idxjp <= nlp1) { --idxjp; } if (idxj <= nlp1) { --idxj; } drot_(&n, &u[idxjp * u_dim1 + 1], &c__1, &u[idxj * u_dim1 + 1], & c__1, &c__, &s); drot_(&m, &vt[idxjp + vt_dim1], ldvt, &vt[idxj + vt_dim1], ldvt, & c__, &s); if (coltyp[j] != coltyp[jprev]) { coltyp[j] = 3; } coltyp[jprev] = 4; --k2; idxp[k2] = jprev; jprev = j; } else { ++(*k); u2[*k + u2_dim1] = z__[jprev]; dsigma[*k] = d__[jprev]; idxp[*k] = jprev; jprev = j; } } goto L100; L110: /* Record the last singular value. */ ++(*k); u2[*k + u2_dim1] = z__[jprev]; dsigma[*k] = d__[jprev]; idxp[*k] = jprev; L120: /* Count up the total number of the various types of columns, then */ /* form a permutation which positions the four column types into */ /* four groups of uniform structure (although one or more of these */ /* groups may be empty). */ for (j = 1; j <= 4; ++j) { ctot[j - 1] = 0; /* L130: */ } i__1 = n; for (j = 2; j <= i__1; ++j) { ct = coltyp[j]; ++ctot[ct - 1]; /* L140: */ } /* PSM(*) = Position in SubMatrix (of types 1 through 4) */ psm[0] = 2; psm[1] = ctot[0] + 2; psm[2] = psm[1] + ctot[1]; psm[3] = psm[2] + ctot[2]; /* Fill out the IDXC array so that the permutation which it induces */ /* will place all type-1 columns first, all type-2 columns next, */ /* then all type-3's, and finally all type-4's, starting from the */ /* second column. This applies similarly to the rows of VT. */ i__1 = n; for (j = 2; j <= i__1; ++j) { jp = idxp[j]; ct = coltyp[jp]; idxc[psm[ct - 1]] = j; ++psm[ct - 1]; /* L150: */ } /* Sort the singular values and corresponding singular vectors into */ /* DSIGMA, U2, and VT2 respectively. The singular values/vectors */ /* which were not deflated go into the first K slots of DSIGMA, U2, */ /* and VT2 respectively, while those which were deflated go into the */ /* last N - K slots, except that the first column/row will be treated */ /* separately. */ i__1 = n; for (j = 2; j <= i__1; ++j) { jp = idxp[j]; dsigma[j] = d__[jp]; idxj = idxq[idx[idxp[idxc[j]]] + 1]; if (idxj <= nlp1) { --idxj; } dcopy_(&n, &u[idxj * u_dim1 + 1], &c__1, &u2[j * u2_dim1 + 1], &c__1); dcopy_(&m, &vt[idxj + vt_dim1], ldvt, &vt2[j + vt2_dim1], ldvt2); /* L160: */ } /* Determine DSIGMA(1), DSIGMA(2) and Z(1) */ dsigma[1] = 0.; hlftol = tol / 2.; if (abs(dsigma[2]) <= hlftol) { dsigma[2] = hlftol; } if (m > n) { z__[1] = dlapy2_(&z1, &z__[m]); if (z__[1] <= tol) { c__ = 1.; s = 0.; z__[1] = tol; } else { c__ = z1 / z__[1]; s = z__[m] / z__[1]; } } else { if (abs(z1) <= tol) { z__[1] = tol; } else { z__[1] = z1; } } /* Move the rest of the updating row to Z. */ i__1 = *k - 1; dcopy_(&i__1, &u2[u2_dim1 + 2], &c__1, &z__[2], &c__1); /* Determine the first column of U2, the first row of VT2 and the */ /* last row of VT. */ dlaset_("A", &n, &c__1, &c_b30, &c_b30, &u2[u2_offset], ldu2); u2[nlp1 + u2_dim1] = 1.; if (m > n) { i__1 = nlp1; for (i__ = 1; i__ <= i__1; ++i__) { vt[m + i__ * vt_dim1] = -s * vt[nlp1 + i__ * vt_dim1]; vt2[i__ * vt2_dim1 + 1] = c__ * vt[nlp1 + i__ * vt_dim1]; /* L170: */ } i__1 = m; for (i__ = nlp2; i__ <= i__1; ++i__) { vt2[i__ * vt2_dim1 + 1] = s * vt[m + i__ * vt_dim1]; vt[m + i__ * vt_dim1] = c__ * vt[m + i__ * vt_dim1]; /* L180: */ } } else { dcopy_(&m, &vt[nlp1 + vt_dim1], ldvt, &vt2[vt2_dim1 + 1], ldvt2); } if (m > n) { dcopy_(&m, &vt[m + vt_dim1], ldvt, &vt2[m + vt2_dim1], ldvt2); } /* The deflated singular values and their corresponding vectors go */ /* into the back of D, U, and V respectively. */ if (n > *k) { i__1 = n - *k; dcopy_(&i__1, &dsigma[*k + 1], &c__1, &d__[*k + 1], &c__1); i__1 = n - *k; dlacpy_("A", &n, &i__1, &u2[(*k + 1) * u2_dim1 + 1], ldu2, &u[(*k + 1) * u_dim1 + 1], ldu); i__1 = n - *k; dlacpy_("A", &i__1, &m, &vt2[*k + 1 + vt2_dim1], ldvt2, &vt[*k + 1 + vt_dim1], ldvt); } /* Copy CTOT into COLTYP for referencing in DLASD3. */ for (j = 1; j <= 4; ++j) { coltyp[j] = ctot[j - 1]; /* L190: */ } return 0; /* End of DLASD2 */ } /* dlasd2_ */