Annotation of rpl/lapack/lapack/dbdsqr.f, revision 1.19
1.8 bertrand 1: *> \brief \b DBDSQR
2: *
3: * =========== DOCUMENTATION ===========
4: *
1.14 bertrand 5: * Online html documentation available at
6: * http://www.netlib.org/lapack/explore-html/
1.8 bertrand 7: *
8: *> \htmlonly
1.14 bertrand 9: *> Download DBDSQR + dependencies
10: *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dbdsqr.f">
11: *> [TGZ]</a>
12: *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dbdsqr.f">
13: *> [ZIP]</a>
14: *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dbdsqr.f">
1.8 bertrand 15: *> [TXT]</a>
1.14 bertrand 16: *> \endhtmlonly
1.8 bertrand 17: *
18: * Definition:
19: * ===========
20: *
21: * SUBROUTINE DBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
22: * LDU, C, LDC, WORK, INFO )
1.14 bertrand 23: *
1.8 bertrand 24: * .. Scalar Arguments ..
25: * CHARACTER UPLO
26: * INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
27: * ..
28: * .. Array Arguments ..
29: * DOUBLE PRECISION C( LDC, * ), D( * ), E( * ), U( LDU, * ),
30: * $ VT( LDVT, * ), WORK( * )
31: * ..
1.14 bertrand 32: *
1.8 bertrand 33: *
34: *> \par Purpose:
35: * =============
36: *>
37: *> \verbatim
38: *>
39: *> DBDSQR computes the singular values and, optionally, the right and/or
40: *> left singular vectors from the singular value decomposition (SVD) of
41: *> a real N-by-N (upper or lower) bidiagonal matrix B using the implicit
42: *> zero-shift QR algorithm. The SVD of B has the form
1.14 bertrand 43: *>
1.8 bertrand 44: *> B = Q * S * P**T
1.14 bertrand 45: *>
1.8 bertrand 46: *> where S is the diagonal matrix of singular values, Q is an orthogonal
47: *> matrix of left singular vectors, and P is an orthogonal matrix of
48: *> right singular vectors. If left singular vectors are requested, this
49: *> subroutine actually returns U*Q instead of Q, and, if right singular
50: *> vectors are requested, this subroutine returns P**T*VT instead of
51: *> P**T, for given real input matrices U and VT. When U and VT are the
52: *> orthogonal matrices that reduce a general matrix A to bidiagonal
53: *> form: A = U*B*VT, as computed by DGEBRD, then
54: *>
55: *> A = (U*Q) * S * (P**T*VT)
56: *>
57: *> is the SVD of A. Optionally, the subroutine may also compute Q**T*C
58: *> for a given real input matrix C.
59: *>
60: *> See "Computing Small Singular Values of Bidiagonal Matrices With
61: *> Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan,
62: *> LAPACK Working Note #3 (or SIAM J. Sci. Statist. Comput. vol. 11,
63: *> no. 5, pp. 873-912, Sept 1990) and
64: *> "Accurate singular values and differential qd algorithms," by
65: *> B. Parlett and V. Fernando, Technical Report CPAM-554, Mathematics
66: *> Department, University of California at Berkeley, July 1992
67: *> for a detailed description of the algorithm.
68: *> \endverbatim
69: *
70: * Arguments:
71: * ==========
72: *
73: *> \param[in] UPLO
74: *> \verbatim
75: *> UPLO is CHARACTER*1
76: *> = 'U': B is upper bidiagonal;
77: *> = 'L': B is lower bidiagonal.
78: *> \endverbatim
79: *>
80: *> \param[in] N
81: *> \verbatim
82: *> N is INTEGER
83: *> The order of the matrix B. N >= 0.
84: *> \endverbatim
85: *>
86: *> \param[in] NCVT
87: *> \verbatim
88: *> NCVT is INTEGER
89: *> The number of columns of the matrix VT. NCVT >= 0.
90: *> \endverbatim
91: *>
92: *> \param[in] NRU
93: *> \verbatim
94: *> NRU is INTEGER
95: *> The number of rows of the matrix U. NRU >= 0.
96: *> \endverbatim
97: *>
98: *> \param[in] NCC
99: *> \verbatim
100: *> NCC is INTEGER
101: *> The number of columns of the matrix C. NCC >= 0.
102: *> \endverbatim
103: *>
104: *> \param[in,out] D
105: *> \verbatim
106: *> D is DOUBLE PRECISION array, dimension (N)
107: *> On entry, the n diagonal elements of the bidiagonal matrix B.
108: *> On exit, if INFO=0, the singular values of B in decreasing
109: *> order.
110: *> \endverbatim
111: *>
112: *> \param[in,out] E
113: *> \verbatim
114: *> E is DOUBLE PRECISION array, dimension (N-1)
115: *> On entry, the N-1 offdiagonal elements of the bidiagonal
1.14 bertrand 116: *> matrix B.
1.8 bertrand 117: *> On exit, if INFO = 0, E is destroyed; if INFO > 0, D and E
118: *> will contain the diagonal and superdiagonal elements of a
119: *> bidiagonal matrix orthogonally equivalent to the one given
120: *> as input.
121: *> \endverbatim
122: *>
123: *> \param[in,out] VT
124: *> \verbatim
125: *> VT is DOUBLE PRECISION array, dimension (LDVT, NCVT)
126: *> On entry, an N-by-NCVT matrix VT.
127: *> On exit, VT is overwritten by P**T * VT.
128: *> Not referenced if NCVT = 0.
129: *> \endverbatim
130: *>
131: *> \param[in] LDVT
132: *> \verbatim
133: *> LDVT is INTEGER
134: *> The leading dimension of the array VT.
135: *> LDVT >= max(1,N) if NCVT > 0; LDVT >= 1 if NCVT = 0.
136: *> \endverbatim
137: *>
138: *> \param[in,out] U
139: *> \verbatim
140: *> U is DOUBLE PRECISION array, dimension (LDU, N)
141: *> On entry, an NRU-by-N matrix U.
142: *> On exit, U is overwritten by U * Q.
143: *> Not referenced if NRU = 0.
144: *> \endverbatim
145: *>
146: *> \param[in] LDU
147: *> \verbatim
148: *> LDU is INTEGER
149: *> The leading dimension of the array U. LDU >= max(1,NRU).
150: *> \endverbatim
151: *>
152: *> \param[in,out] C
153: *> \verbatim
154: *> C is DOUBLE PRECISION array, dimension (LDC, NCC)
155: *> On entry, an N-by-NCC matrix C.
156: *> On exit, C is overwritten by Q**T * C.
157: *> Not referenced if NCC = 0.
158: *> \endverbatim
159: *>
160: *> \param[in] LDC
161: *> \verbatim
162: *> LDC is INTEGER
163: *> The leading dimension of the array C.
164: *> LDC >= max(1,N) if NCC > 0; LDC >=1 if NCC = 0.
165: *> \endverbatim
166: *>
167: *> \param[out] WORK
168: *> \verbatim
1.18 bertrand 169: *> WORK is DOUBLE PRECISION array, dimension (4*(N-1))
1.8 bertrand 170: *> \endverbatim
171: *>
172: *> \param[out] INFO
173: *> \verbatim
174: *> INFO is INTEGER
175: *> = 0: successful exit
176: *> < 0: If INFO = -i, the i-th argument had an illegal value
177: *> > 0:
178: *> if NCVT = NRU = NCC = 0,
179: *> = 1, a split was marked by a positive value in E
180: *> = 2, current block of Z not diagonalized after 30*N
181: *> iterations (in inner while loop)
1.14 bertrand 182: *> = 3, termination criterion of outer while loop not met
1.8 bertrand 183: *> (program created more than N unreduced blocks)
184: *> else NCVT = NRU = NCC = 0,
185: *> the algorithm did not converge; D and E contain the
186: *> elements of a bidiagonal matrix which is orthogonally
187: *> similar to the input matrix B; if INFO = i, i
188: *> elements of E have not converged to zero.
189: *> \endverbatim
190: *
191: *> \par Internal Parameters:
192: * =========================
193: *>
194: *> \verbatim
195: *> TOLMUL DOUBLE PRECISION, default = max(10,min(100,EPS**(-1/8)))
196: *> TOLMUL controls the convergence criterion of the QR loop.
197: *> If it is positive, TOLMUL*EPS is the desired relative
198: *> precision in the computed singular values.
199: *> If it is negative, abs(TOLMUL*EPS*sigma_max) is the
200: *> desired absolute accuracy in the computed singular
201: *> values (corresponds to relative accuracy
202: *> abs(TOLMUL*EPS) in the largest singular value.
203: *> abs(TOLMUL) should be between 1 and 1/EPS, and preferably
204: *> between 10 (for fast convergence) and .1/EPS
205: *> (for there to be some accuracy in the results).
206: *> Default is to lose at either one eighth or 2 of the
207: *> available decimal digits in each computed singular value
208: *> (whichever is smaller).
209: *>
210: *> MAXITR INTEGER, default = 6
211: *> MAXITR controls the maximum number of passes of the
212: *> algorithm through its inner loop. The algorithms stops
213: *> (and so fails to converge) if the number of passes
214: *> through the inner loop exceeds MAXITR*N**2.
1.16 bertrand 215: *>
216: *> \endverbatim
217: *
218: *> \par Note:
219: * ===========
220: *>
221: *> \verbatim
222: *> Bug report from Cezary Dendek.
223: *> On March 23rd 2017, the INTEGER variable MAXIT = MAXITR*N**2 is
224: *> removed since it can overflow pretty easily (for N larger or equal
225: *> than 18,919). We instead use MAXITDIVN = MAXITR*N.
1.8 bertrand 226: *> \endverbatim
227: *
228: * Authors:
229: * ========
230: *
1.14 bertrand 231: *> \author Univ. of Tennessee
232: *> \author Univ. of California Berkeley
233: *> \author Univ. of Colorado Denver
234: *> \author NAG Ltd.
1.8 bertrand 235: *
236: *> \ingroup auxOTHERcomputational
237: *
238: * =====================================================================
1.1 bertrand 239: SUBROUTINE DBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U,
240: $ LDU, C, LDC, WORK, INFO )
241: *
1.19 ! bertrand 242: * -- LAPACK computational routine --
1.1 bertrand 243: * -- LAPACK is a software package provided by Univ. of Tennessee, --
244: * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
245: *
246: * .. Scalar Arguments ..
247: CHARACTER UPLO
248: INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
249: * ..
250: * .. Array Arguments ..
251: DOUBLE PRECISION C( LDC, * ), D( * ), E( * ), U( LDU, * ),
252: $ VT( LDVT, * ), WORK( * )
253: * ..
254: *
255: * =====================================================================
256: *
257: * .. Parameters ..
258: DOUBLE PRECISION ZERO
259: PARAMETER ( ZERO = 0.0D0 )
260: DOUBLE PRECISION ONE
261: PARAMETER ( ONE = 1.0D0 )
262: DOUBLE PRECISION NEGONE
263: PARAMETER ( NEGONE = -1.0D0 )
264: DOUBLE PRECISION HNDRTH
265: PARAMETER ( HNDRTH = 0.01D0 )
266: DOUBLE PRECISION TEN
267: PARAMETER ( TEN = 10.0D0 )
268: DOUBLE PRECISION HNDRD
269: PARAMETER ( HNDRD = 100.0D0 )
270: DOUBLE PRECISION MEIGTH
271: PARAMETER ( MEIGTH = -0.125D0 )
272: INTEGER MAXITR
273: PARAMETER ( MAXITR = 6 )
274: * ..
275: * .. Local Scalars ..
276: LOGICAL LOWER, ROTATE
1.16 bertrand 277: INTEGER I, IDIR, ISUB, ITER, ITERDIVN, J, LL, LLL, M,
278: $ MAXITDIVN, NM1, NM12, NM13, OLDLL, OLDM
1.1 bertrand 279: DOUBLE PRECISION ABSE, ABSS, COSL, COSR, CS, EPS, F, G, H, MU,
280: $ OLDCS, OLDSN, R, SHIFT, SIGMN, SIGMX, SINL,
281: $ SINR, SLL, SMAX, SMIN, SMINL, SMINOA,
282: $ SN, THRESH, TOL, TOLMUL, UNFL
283: * ..
284: * .. External Functions ..
285: LOGICAL LSAME
286: DOUBLE PRECISION DLAMCH
287: EXTERNAL LSAME, DLAMCH
288: * ..
289: * .. External Subroutines ..
290: EXTERNAL DLARTG, DLAS2, DLASQ1, DLASR, DLASV2, DROT,
291: $ DSCAL, DSWAP, XERBLA
292: * ..
293: * .. Intrinsic Functions ..
294: INTRINSIC ABS, DBLE, MAX, MIN, SIGN, SQRT
295: * ..
296: * .. Executable Statements ..
297: *
298: * Test the input parameters.
299: *
300: INFO = 0
301: LOWER = LSAME( UPLO, 'L' )
302: IF( .NOT.LSAME( UPLO, 'U' ) .AND. .NOT.LOWER ) THEN
303: INFO = -1
304: ELSE IF( N.LT.0 ) THEN
305: INFO = -2
306: ELSE IF( NCVT.LT.0 ) THEN
307: INFO = -3
308: ELSE IF( NRU.LT.0 ) THEN
309: INFO = -4
310: ELSE IF( NCC.LT.0 ) THEN
311: INFO = -5
312: ELSE IF( ( NCVT.EQ.0 .AND. LDVT.LT.1 ) .OR.
313: $ ( NCVT.GT.0 .AND. LDVT.LT.MAX( 1, N ) ) ) THEN
314: INFO = -9
315: ELSE IF( LDU.LT.MAX( 1, NRU ) ) THEN
316: INFO = -11
317: ELSE IF( ( NCC.EQ.0 .AND. LDC.LT.1 ) .OR.
318: $ ( NCC.GT.0 .AND. LDC.LT.MAX( 1, N ) ) ) THEN
319: INFO = -13
320: END IF
321: IF( INFO.NE.0 ) THEN
322: CALL XERBLA( 'DBDSQR', -INFO )
323: RETURN
324: END IF
325: IF( N.EQ.0 )
326: $ RETURN
327: IF( N.EQ.1 )
328: $ GO TO 160
329: *
330: * ROTATE is true if any singular vectors desired, false otherwise
331: *
332: ROTATE = ( NCVT.GT.0 ) .OR. ( NRU.GT.0 ) .OR. ( NCC.GT.0 )
333: *
334: * If no singular vectors desired, use qd algorithm
335: *
336: IF( .NOT.ROTATE ) THEN
337: CALL DLASQ1( N, D, E, WORK, INFO )
1.8 bertrand 338: *
339: * If INFO equals 2, dqds didn't finish, try to finish
1.14 bertrand 340: *
1.8 bertrand 341: IF( INFO .NE. 2 ) RETURN
342: INFO = 0
1.1 bertrand 343: END IF
344: *
345: NM1 = N - 1
346: NM12 = NM1 + NM1
347: NM13 = NM12 + NM1
348: IDIR = 0
349: *
350: * Get machine constants
351: *
352: EPS = DLAMCH( 'Epsilon' )
353: UNFL = DLAMCH( 'Safe minimum' )
354: *
355: * If matrix lower bidiagonal, rotate to be upper bidiagonal
356: * by applying Givens rotations on the left
357: *
358: IF( LOWER ) THEN
359: DO 10 I = 1, N - 1
360: CALL DLARTG( D( I ), E( I ), CS, SN, R )
361: D( I ) = R
362: E( I ) = SN*D( I+1 )
363: D( I+1 ) = CS*D( I+1 )
364: WORK( I ) = CS
365: WORK( NM1+I ) = SN
366: 10 CONTINUE
367: *
368: * Update singular vectors if desired
369: *
370: IF( NRU.GT.0 )
371: $ CALL DLASR( 'R', 'V', 'F', NRU, N, WORK( 1 ), WORK( N ), U,
372: $ LDU )
373: IF( NCC.GT.0 )
374: $ CALL DLASR( 'L', 'V', 'F', N, NCC, WORK( 1 ), WORK( N ), C,
375: $ LDC )
376: END IF
377: *
378: * Compute singular values to relative accuracy TOL
379: * (By setting TOL to be negative, algorithm will compute
380: * singular values to absolute accuracy ABS(TOL)*norm(input matrix))
381: *
382: TOLMUL = MAX( TEN, MIN( HNDRD, EPS**MEIGTH ) )
383: TOL = TOLMUL*EPS
384: *
385: * Compute approximate maximum, minimum singular values
386: *
387: SMAX = ZERO
388: DO 20 I = 1, N
389: SMAX = MAX( SMAX, ABS( D( I ) ) )
390: 20 CONTINUE
391: DO 30 I = 1, N - 1
392: SMAX = MAX( SMAX, ABS( E( I ) ) )
393: 30 CONTINUE
394: SMINL = ZERO
395: IF( TOL.GE.ZERO ) THEN
396: *
397: * Relative accuracy desired
398: *
399: SMINOA = ABS( D( 1 ) )
400: IF( SMINOA.EQ.ZERO )
401: $ GO TO 50
402: MU = SMINOA
403: DO 40 I = 2, N
404: MU = ABS( D( I ) )*( MU / ( MU+ABS( E( I-1 ) ) ) )
405: SMINOA = MIN( SMINOA, MU )
406: IF( SMINOA.EQ.ZERO )
407: $ GO TO 50
408: 40 CONTINUE
409: 50 CONTINUE
410: SMINOA = SMINOA / SQRT( DBLE( N ) )
1.16 bertrand 411: THRESH = MAX( TOL*SMINOA, MAXITR*(N*(N*UNFL)) )
1.1 bertrand 412: ELSE
413: *
414: * Absolute accuracy desired
415: *
1.16 bertrand 416: THRESH = MAX( ABS( TOL )*SMAX, MAXITR*(N*(N*UNFL)) )
1.1 bertrand 417: END IF
418: *
419: * Prepare for main iteration loop for the singular values
420: * (MAXIT is the maximum number of passes through the inner
421: * loop permitted before nonconvergence signalled.)
422: *
1.16 bertrand 423: MAXITDIVN = MAXITR*N
424: ITERDIVN = 0
425: ITER = -1
1.1 bertrand 426: OLDLL = -1
427: OLDM = -1
428: *
429: * M points to last element of unconverged part of matrix
430: *
431: M = N
432: *
433: * Begin main iteration loop
434: *
435: 60 CONTINUE
436: *
437: * Check for convergence or exceeding iteration count
438: *
439: IF( M.LE.1 )
440: $ GO TO 160
1.16 bertrand 441: *
442: IF( ITER.GE.N ) THEN
443: ITER = ITER - N
444: ITERDIVN = ITERDIVN + 1
445: IF( ITERDIVN.GE.MAXITDIVN )
446: $ GO TO 200
447: END IF
1.1 bertrand 448: *
449: * Find diagonal block of matrix to work on
450: *
451: IF( TOL.LT.ZERO .AND. ABS( D( M ) ).LE.THRESH )
452: $ D( M ) = ZERO
453: SMAX = ABS( D( M ) )
454: SMIN = SMAX
455: DO 70 LLL = 1, M - 1
456: LL = M - LLL
457: ABSS = ABS( D( LL ) )
458: ABSE = ABS( E( LL ) )
459: IF( TOL.LT.ZERO .AND. ABSS.LE.THRESH )
460: $ D( LL ) = ZERO
461: IF( ABSE.LE.THRESH )
462: $ GO TO 80
463: SMIN = MIN( SMIN, ABSS )
464: SMAX = MAX( SMAX, ABSS, ABSE )
465: 70 CONTINUE
466: LL = 0
467: GO TO 90
468: 80 CONTINUE
469: E( LL ) = ZERO
470: *
471: * Matrix splits since E(LL) = 0
472: *
473: IF( LL.EQ.M-1 ) THEN
474: *
475: * Convergence of bottom singular value, return to top of loop
476: *
477: M = M - 1
478: GO TO 60
479: END IF
480: 90 CONTINUE
481: LL = LL + 1
482: *
483: * E(LL) through E(M-1) are nonzero, E(LL-1) is zero
484: *
485: IF( LL.EQ.M-1 ) THEN
486: *
487: * 2 by 2 block, handle separately
488: *
489: CALL DLASV2( D( M-1 ), E( M-1 ), D( M ), SIGMN, SIGMX, SINR,
490: $ COSR, SINL, COSL )
491: D( M-1 ) = SIGMX
492: E( M-1 ) = ZERO
493: D( M ) = SIGMN
494: *
495: * Compute singular vectors, if desired
496: *
497: IF( NCVT.GT.0 )
498: $ CALL DROT( NCVT, VT( M-1, 1 ), LDVT, VT( M, 1 ), LDVT, COSR,
499: $ SINR )
500: IF( NRU.GT.0 )
501: $ CALL DROT( NRU, U( 1, M-1 ), 1, U( 1, M ), 1, COSL, SINL )
502: IF( NCC.GT.0 )
503: $ CALL DROT( NCC, C( M-1, 1 ), LDC, C( M, 1 ), LDC, COSL,
504: $ SINL )
505: M = M - 2
506: GO TO 60
507: END IF
508: *
509: * If working on new submatrix, choose shift direction
510: * (from larger end diagonal element towards smaller)
511: *
512: IF( LL.GT.OLDM .OR. M.LT.OLDLL ) THEN
513: IF( ABS( D( LL ) ).GE.ABS( D( M ) ) ) THEN
514: *
515: * Chase bulge from top (big end) to bottom (small end)
516: *
517: IDIR = 1
518: ELSE
519: *
520: * Chase bulge from bottom (big end) to top (small end)
521: *
522: IDIR = 2
523: END IF
524: END IF
525: *
526: * Apply convergence tests
527: *
528: IF( IDIR.EQ.1 ) THEN
529: *
530: * Run convergence test in forward direction
531: * First apply standard test to bottom of matrix
532: *
533: IF( ABS( E( M-1 ) ).LE.ABS( TOL )*ABS( D( M ) ) .OR.
534: $ ( TOL.LT.ZERO .AND. ABS( E( M-1 ) ).LE.THRESH ) ) THEN
535: E( M-1 ) = ZERO
536: GO TO 60
537: END IF
538: *
539: IF( TOL.GE.ZERO ) THEN
540: *
541: * If relative accuracy desired,
542: * apply convergence criterion forward
543: *
544: MU = ABS( D( LL ) )
545: SMINL = MU
546: DO 100 LLL = LL, M - 1
547: IF( ABS( E( LLL ) ).LE.TOL*MU ) THEN
548: E( LLL ) = ZERO
549: GO TO 60
550: END IF
551: MU = ABS( D( LLL+1 ) )*( MU / ( MU+ABS( E( LLL ) ) ) )
552: SMINL = MIN( SMINL, MU )
553: 100 CONTINUE
554: END IF
555: *
556: ELSE
557: *
558: * Run convergence test in backward direction
559: * First apply standard test to top of matrix
560: *
561: IF( ABS( E( LL ) ).LE.ABS( TOL )*ABS( D( LL ) ) .OR.
562: $ ( TOL.LT.ZERO .AND. ABS( E( LL ) ).LE.THRESH ) ) THEN
563: E( LL ) = ZERO
564: GO TO 60
565: END IF
566: *
567: IF( TOL.GE.ZERO ) THEN
568: *
569: * If relative accuracy desired,
570: * apply convergence criterion backward
571: *
572: MU = ABS( D( M ) )
573: SMINL = MU
574: DO 110 LLL = M - 1, LL, -1
575: IF( ABS( E( LLL ) ).LE.TOL*MU ) THEN
576: E( LLL ) = ZERO
577: GO TO 60
578: END IF
579: MU = ABS( D( LLL ) )*( MU / ( MU+ABS( E( LLL ) ) ) )
580: SMINL = MIN( SMINL, MU )
581: 110 CONTINUE
582: END IF
583: END IF
584: OLDLL = LL
585: OLDM = M
586: *
587: * Compute shift. First, test if shifting would ruin relative
588: * accuracy, and if so set the shift to zero.
589: *
590: IF( TOL.GE.ZERO .AND. N*TOL*( SMINL / SMAX ).LE.
591: $ MAX( EPS, HNDRTH*TOL ) ) THEN
592: *
593: * Use a zero shift to avoid loss of relative accuracy
594: *
595: SHIFT = ZERO
596: ELSE
597: *
598: * Compute the shift from 2-by-2 block at end of matrix
599: *
600: IF( IDIR.EQ.1 ) THEN
601: SLL = ABS( D( LL ) )
602: CALL DLAS2( D( M-1 ), E( M-1 ), D( M ), SHIFT, R )
603: ELSE
604: SLL = ABS( D( M ) )
605: CALL DLAS2( D( LL ), E( LL ), D( LL+1 ), SHIFT, R )
606: END IF
607: *
608: * Test if shift negligible, and if so set to zero
609: *
610: IF( SLL.GT.ZERO ) THEN
611: IF( ( SHIFT / SLL )**2.LT.EPS )
612: $ SHIFT = ZERO
613: END IF
614: END IF
615: *
616: * Increment iteration count
617: *
618: ITER = ITER + M - LL
619: *
620: * If SHIFT = 0, do simplified QR iteration
621: *
622: IF( SHIFT.EQ.ZERO ) THEN
623: IF( IDIR.EQ.1 ) THEN
624: *
625: * Chase bulge from top to bottom
626: * Save cosines and sines for later singular vector updates
627: *
628: CS = ONE
629: OLDCS = ONE
630: DO 120 I = LL, M - 1
631: CALL DLARTG( D( I )*CS, E( I ), CS, SN, R )
632: IF( I.GT.LL )
633: $ E( I-1 ) = OLDSN*R
634: CALL DLARTG( OLDCS*R, D( I+1 )*SN, OLDCS, OLDSN, D( I ) )
635: WORK( I-LL+1 ) = CS
636: WORK( I-LL+1+NM1 ) = SN
637: WORK( I-LL+1+NM12 ) = OLDCS
638: WORK( I-LL+1+NM13 ) = OLDSN
639: 120 CONTINUE
640: H = D( M )*CS
641: D( M ) = H*OLDCS
642: E( M-1 ) = H*OLDSN
643: *
644: * Update singular vectors
645: *
646: IF( NCVT.GT.0 )
647: $ CALL DLASR( 'L', 'V', 'F', M-LL+1, NCVT, WORK( 1 ),
648: $ WORK( N ), VT( LL, 1 ), LDVT )
649: IF( NRU.GT.0 )
650: $ CALL DLASR( 'R', 'V', 'F', NRU, M-LL+1, WORK( NM12+1 ),
651: $ WORK( NM13+1 ), U( 1, LL ), LDU )
652: IF( NCC.GT.0 )
653: $ CALL DLASR( 'L', 'V', 'F', M-LL+1, NCC, WORK( NM12+1 ),
654: $ WORK( NM13+1 ), C( LL, 1 ), LDC )
655: *
656: * Test convergence
657: *
658: IF( ABS( E( M-1 ) ).LE.THRESH )
659: $ E( M-1 ) = ZERO
660: *
661: ELSE
662: *
663: * Chase bulge from bottom to top
664: * Save cosines and sines for later singular vector updates
665: *
666: CS = ONE
667: OLDCS = ONE
668: DO 130 I = M, LL + 1, -1
669: CALL DLARTG( D( I )*CS, E( I-1 ), CS, SN, R )
670: IF( I.LT.M )
671: $ E( I ) = OLDSN*R
672: CALL DLARTG( OLDCS*R, D( I-1 )*SN, OLDCS, OLDSN, D( I ) )
673: WORK( I-LL ) = CS
674: WORK( I-LL+NM1 ) = -SN
675: WORK( I-LL+NM12 ) = OLDCS
676: WORK( I-LL+NM13 ) = -OLDSN
677: 130 CONTINUE
678: H = D( LL )*CS
679: D( LL ) = H*OLDCS
680: E( LL ) = H*OLDSN
681: *
682: * Update singular vectors
683: *
684: IF( NCVT.GT.0 )
685: $ CALL DLASR( 'L', 'V', 'B', M-LL+1, NCVT, WORK( NM12+1 ),
686: $ WORK( NM13+1 ), VT( LL, 1 ), LDVT )
687: IF( NRU.GT.0 )
688: $ CALL DLASR( 'R', 'V', 'B', NRU, M-LL+1, WORK( 1 ),
689: $ WORK( N ), U( 1, LL ), LDU )
690: IF( NCC.GT.0 )
691: $ CALL DLASR( 'L', 'V', 'B', M-LL+1, NCC, WORK( 1 ),
692: $ WORK( N ), C( LL, 1 ), LDC )
693: *
694: * Test convergence
695: *
696: IF( ABS( E( LL ) ).LE.THRESH )
697: $ E( LL ) = ZERO
698: END IF
699: ELSE
700: *
701: * Use nonzero shift
702: *
703: IF( IDIR.EQ.1 ) THEN
704: *
705: * Chase bulge from top to bottom
706: * Save cosines and sines for later singular vector updates
707: *
708: F = ( ABS( D( LL ) )-SHIFT )*
709: $ ( SIGN( ONE, D( LL ) )+SHIFT / D( LL ) )
710: G = E( LL )
711: DO 140 I = LL, M - 1
712: CALL DLARTG( F, G, COSR, SINR, R )
713: IF( I.GT.LL )
714: $ E( I-1 ) = R
715: F = COSR*D( I ) + SINR*E( I )
716: E( I ) = COSR*E( I ) - SINR*D( I )
717: G = SINR*D( I+1 )
718: D( I+1 ) = COSR*D( I+1 )
719: CALL DLARTG( F, G, COSL, SINL, R )
720: D( I ) = R
721: F = COSL*E( I ) + SINL*D( I+1 )
722: D( I+1 ) = COSL*D( I+1 ) - SINL*E( I )
723: IF( I.LT.M-1 ) THEN
724: G = SINL*E( I+1 )
725: E( I+1 ) = COSL*E( I+1 )
726: END IF
727: WORK( I-LL+1 ) = COSR
728: WORK( I-LL+1+NM1 ) = SINR
729: WORK( I-LL+1+NM12 ) = COSL
730: WORK( I-LL+1+NM13 ) = SINL
731: 140 CONTINUE
732: E( M-1 ) = F
733: *
734: * Update singular vectors
735: *
736: IF( NCVT.GT.0 )
737: $ CALL DLASR( 'L', 'V', 'F', M-LL+1, NCVT, WORK( 1 ),
738: $ WORK( N ), VT( LL, 1 ), LDVT )
739: IF( NRU.GT.0 )
740: $ CALL DLASR( 'R', 'V', 'F', NRU, M-LL+1, WORK( NM12+1 ),
741: $ WORK( NM13+1 ), U( 1, LL ), LDU )
742: IF( NCC.GT.0 )
743: $ CALL DLASR( 'L', 'V', 'F', M-LL+1, NCC, WORK( NM12+1 ),
744: $ WORK( NM13+1 ), C( LL, 1 ), LDC )
745: *
746: * Test convergence
747: *
748: IF( ABS( E( M-1 ) ).LE.THRESH )
749: $ E( M-1 ) = ZERO
750: *
751: ELSE
752: *
753: * Chase bulge from bottom to top
754: * Save cosines and sines for later singular vector updates
755: *
756: F = ( ABS( D( M ) )-SHIFT )*( SIGN( ONE, D( M ) )+SHIFT /
757: $ D( M ) )
758: G = E( M-1 )
759: DO 150 I = M, LL + 1, -1
760: CALL DLARTG( F, G, COSR, SINR, R )
761: IF( I.LT.M )
762: $ E( I ) = R
763: F = COSR*D( I ) + SINR*E( I-1 )
764: E( I-1 ) = COSR*E( I-1 ) - SINR*D( I )
765: G = SINR*D( I-1 )
766: D( I-1 ) = COSR*D( I-1 )
767: CALL DLARTG( F, G, COSL, SINL, R )
768: D( I ) = R
769: F = COSL*E( I-1 ) + SINL*D( I-1 )
770: D( I-1 ) = COSL*D( I-1 ) - SINL*E( I-1 )
771: IF( I.GT.LL+1 ) THEN
772: G = SINL*E( I-2 )
773: E( I-2 ) = COSL*E( I-2 )
774: END IF
775: WORK( I-LL ) = COSR
776: WORK( I-LL+NM1 ) = -SINR
777: WORK( I-LL+NM12 ) = COSL
778: WORK( I-LL+NM13 ) = -SINL
779: 150 CONTINUE
780: E( LL ) = F
781: *
782: * Test convergence
783: *
784: IF( ABS( E( LL ) ).LE.THRESH )
785: $ E( LL ) = ZERO
786: *
787: * Update singular vectors if desired
788: *
789: IF( NCVT.GT.0 )
790: $ CALL DLASR( 'L', 'V', 'B', M-LL+1, NCVT, WORK( NM12+1 ),
791: $ WORK( NM13+1 ), VT( LL, 1 ), LDVT )
792: IF( NRU.GT.0 )
793: $ CALL DLASR( 'R', 'V', 'B', NRU, M-LL+1, WORK( 1 ),
794: $ WORK( N ), U( 1, LL ), LDU )
795: IF( NCC.GT.0 )
796: $ CALL DLASR( 'L', 'V', 'B', M-LL+1, NCC, WORK( 1 ),
797: $ WORK( N ), C( LL, 1 ), LDC )
798: END IF
799: END IF
800: *
801: * QR iteration finished, go back and check convergence
802: *
803: GO TO 60
804: *
805: * All singular values converged, so make them positive
806: *
807: 160 CONTINUE
808: DO 170 I = 1, N
809: IF( D( I ).LT.ZERO ) THEN
810: D( I ) = -D( I )
811: *
812: * Change sign of singular vectors, if desired
813: *
814: IF( NCVT.GT.0 )
815: $ CALL DSCAL( NCVT, NEGONE, VT( I, 1 ), LDVT )
816: END IF
817: 170 CONTINUE
818: *
819: * Sort the singular values into decreasing order (insertion sort on
820: * singular values, but only one transposition per singular vector)
821: *
822: DO 190 I = 1, N - 1
823: *
824: * Scan for smallest D(I)
825: *
826: ISUB = 1
827: SMIN = D( 1 )
828: DO 180 J = 2, N + 1 - I
829: IF( D( J ).LE.SMIN ) THEN
830: ISUB = J
831: SMIN = D( J )
832: END IF
833: 180 CONTINUE
834: IF( ISUB.NE.N+1-I ) THEN
835: *
836: * Swap singular values and vectors
837: *
838: D( ISUB ) = D( N+1-I )
839: D( N+1-I ) = SMIN
840: IF( NCVT.GT.0 )
841: $ CALL DSWAP( NCVT, VT( ISUB, 1 ), LDVT, VT( N+1-I, 1 ),
842: $ LDVT )
843: IF( NRU.GT.0 )
844: $ CALL DSWAP( NRU, U( 1, ISUB ), 1, U( 1, N+1-I ), 1 )
845: IF( NCC.GT.0 )
846: $ CALL DSWAP( NCC, C( ISUB, 1 ), LDC, C( N+1-I, 1 ), LDC )
847: END IF
848: 190 CONTINUE
849: GO TO 220
850: *
851: * Maximum number of iterations exceeded, failure to converge
852: *
853: 200 CONTINUE
854: INFO = 0
855: DO 210 I = 1, N - 1
856: IF( E( I ).NE.ZERO )
857: $ INFO = INFO + 1
858: 210 CONTINUE
859: 220 CONTINUE
860: RETURN
861: *
862: * End of DBDSQR
863: *
864: END
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