LAPACK  3.6.1
LAPACK: Linear Algebra PACKage
sggsvd3.f
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1 *> \brief <b> SGGSVD3 computes the singular value decomposition (SVD) for OTHER matrices</b>
2 *
3 * =========== DOCUMENTATION ===========
4 *
5 * Online html documentation available at
6 * http://www.netlib.org/lapack/explore-html/
7 *
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14 *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/sggsvd3.f">
15 *> [TXT]</a>
16 *> \endhtmlonly
17 *
18 * Definition:
19 * ===========
20 *
21 * SUBROUTINE SGGSVD3( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B,
22 * LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK,
23 * LWORK, IWORK, INFO )
24 *
25 * .. Scalar Arguments ..
26 * CHARACTER JOBQ, JOBU, JOBV
27 * INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P, LWORK
28 * ..
29 * .. Array Arguments ..
30 * INTEGER IWORK( * )
31 * REAL A( LDA, * ), ALPHA( * ), B( LDB, * ),
32 * $ BETA( * ), Q( LDQ, * ), U( LDU, * ),
33 * $ V( LDV, * ), WORK( * )
34 * ..
35 *
36 *
37 *> \par Purpose:
38 * =============
39 *>
40 *> \verbatim
41 *>
42 *> SGGSVD3 computes the generalized singular value decomposition (GSVD)
43 *> of an M-by-N real matrix A and P-by-N real matrix B:
44 *>
45 *> U**T*A*Q = D1*( 0 R ), V**T*B*Q = D2*( 0 R )
46 *>
47 *> where U, V and Q are orthogonal matrices.
48 *> Let K+L = the effective numerical rank of the matrix (A**T,B**T)**T,
49 *> then R is a K+L-by-K+L nonsingular upper triangular matrix, D1 and
50 *> D2 are M-by-(K+L) and P-by-(K+L) "diagonal" matrices and of the
51 *> following structures, respectively:
52 *>
53 *> If M-K-L >= 0,
54 *>
55 *> K L
56 *> D1 = K ( I 0 )
57 *> L ( 0 C )
58 *> M-K-L ( 0 0 )
59 *>
60 *> K L
61 *> D2 = L ( 0 S )
62 *> P-L ( 0 0 )
63 *>
64 *> N-K-L K L
65 *> ( 0 R ) = K ( 0 R11 R12 )
66 *> L ( 0 0 R22 )
67 *>
68 *> where
69 *>
70 *> C = diag( ALPHA(K+1), ... , ALPHA(K+L) ),
71 *> S = diag( BETA(K+1), ... , BETA(K+L) ),
72 *> C**2 + S**2 = I.
73 *>
74 *> R is stored in A(1:K+L,N-K-L+1:N) on exit.
75 *>
76 *> If M-K-L < 0,
77 *>
78 *> K M-K K+L-M
79 *> D1 = K ( I 0 0 )
80 *> M-K ( 0 C 0 )
81 *>
82 *> K M-K K+L-M
83 *> D2 = M-K ( 0 S 0 )
84 *> K+L-M ( 0 0 I )
85 *> P-L ( 0 0 0 )
86 *>
87 *> N-K-L K M-K K+L-M
88 *> ( 0 R ) = K ( 0 R11 R12 R13 )
89 *> M-K ( 0 0 R22 R23 )
90 *> K+L-M ( 0 0 0 R33 )
91 *>
92 *> where
93 *>
94 *> C = diag( ALPHA(K+1), ... , ALPHA(M) ),
95 *> S = diag( BETA(K+1), ... , BETA(M) ),
96 *> C**2 + S**2 = I.
97 *>
98 *> (R11 R12 R13 ) is stored in A(1:M, N-K-L+1:N), and R33 is stored
99 *> ( 0 R22 R23 )
100 *> in B(M-K+1:L,N+M-K-L+1:N) on exit.
101 *>
102 *> The routine computes C, S, R, and optionally the orthogonal
103 *> transformation matrices U, V and Q.
104 *>
105 *> In particular, if B is an N-by-N nonsingular matrix, then the GSVD of
106 *> A and B implicitly gives the SVD of A*inv(B):
107 *> A*inv(B) = U*(D1*inv(D2))*V**T.
108 *> If ( A**T,B**T)**T has orthonormal columns, then the GSVD of A and B is
109 *> also equal to the CS decomposition of A and B. Furthermore, the GSVD
110 *> can be used to derive the solution of the eigenvalue problem:
111 *> A**T*A x = lambda* B**T*B x.
112 *> In some literature, the GSVD of A and B is presented in the form
113 *> U**T*A*X = ( 0 D1 ), V**T*B*X = ( 0 D2 )
114 *> where U and V are orthogonal and X is nonsingular, D1 and D2 are
115 *> ``diagonal''. The former GSVD form can be converted to the latter
116 *> form by taking the nonsingular matrix X as
117 *>
118 *> X = Q*( I 0 )
119 *> ( 0 inv(R) ).
120 *> \endverbatim
121 *
122 * Arguments:
123 * ==========
124 *
125 *> \param[in] JOBU
126 *> \verbatim
127 *> JOBU is CHARACTER*1
128 *> = 'U': Orthogonal matrix U is computed;
129 *> = 'N': U is not computed.
130 *> \endverbatim
131 *>
132 *> \param[in] JOBV
133 *> \verbatim
134 *> JOBV is CHARACTER*1
135 *> = 'V': Orthogonal matrix V is computed;
136 *> = 'N': V is not computed.
137 *> \endverbatim
138 *>
139 *> \param[in] JOBQ
140 *> \verbatim
141 *> JOBQ is CHARACTER*1
142 *> = 'Q': Orthogonal matrix Q is computed;
143 *> = 'N': Q is not computed.
144 *> \endverbatim
145 *>
146 *> \param[in] M
147 *> \verbatim
148 *> M is INTEGER
149 *> The number of rows of the matrix A. M >= 0.
150 *> \endverbatim
151 *>
152 *> \param[in] N
153 *> \verbatim
154 *> N is INTEGER
155 *> The number of columns of the matrices A and B. N >= 0.
156 *> \endverbatim
157 *>
158 *> \param[in] P
159 *> \verbatim
160 *> P is INTEGER
161 *> The number of rows of the matrix B. P >= 0.
162 *> \endverbatim
163 *>
164 *> \param[out] K
165 *> \verbatim
166 *> K is INTEGER
167 *> \endverbatim
168 *>
169 *> \param[out] L
170 *> \verbatim
171 *> L is INTEGER
172 *>
173 *> On exit, K and L specify the dimension of the subblocks
174 *> described in Purpose.
175 *> K + L = effective numerical rank of (A**T,B**T)**T.
176 *> \endverbatim
177 *>
178 *> \param[in,out] A
179 *> \verbatim
180 *> A is REAL array, dimension (LDA,N)
181 *> On entry, the M-by-N matrix A.
182 *> On exit, A contains the triangular matrix R, or part of R.
183 *> See Purpose for details.
184 *> \endverbatim
185 *>
186 *> \param[in] LDA
187 *> \verbatim
188 *> LDA is INTEGER
189 *> The leading dimension of the array A. LDA >= max(1,M).
190 *> \endverbatim
191 *>
192 *> \param[in,out] B
193 *> \verbatim
194 *> B is REAL array, dimension (LDB,N)
195 *> On entry, the P-by-N matrix B.
196 *> On exit, B contains the triangular matrix R if M-K-L < 0.
197 *> See Purpose for details.
198 *> \endverbatim
199 *>
200 *> \param[in] LDB
201 *> \verbatim
202 *> LDB is INTEGER
203 *> The leading dimension of the array B. LDB >= max(1,P).
204 *> \endverbatim
205 *>
206 *> \param[out] ALPHA
207 *> \verbatim
208 *> ALPHA is REAL array, dimension (N)
209 *> \endverbatim
210 *>
211 *> \param[out] BETA
212 *> \verbatim
213 *> BETA is REAL array, dimension (N)
214 *>
215 *> On exit, ALPHA and BETA contain the generalized singular
216 *> value pairs of A and B;
217 *> ALPHA(1:K) = 1,
218 *> BETA(1:K) = 0,
219 *> and if M-K-L >= 0,
220 *> ALPHA(K+1:K+L) = C,
221 *> BETA(K+1:K+L) = S,
222 *> or if M-K-L < 0,
223 *> ALPHA(K+1:M)=C, ALPHA(M+1:K+L)=0
224 *> BETA(K+1:M) =S, BETA(M+1:K+L) =1
225 *> and
226 *> ALPHA(K+L+1:N) = 0
227 *> BETA(K+L+1:N) = 0
228 *> \endverbatim
229 *>
230 *> \param[out] U
231 *> \verbatim
232 *> U is REAL array, dimension (LDU,M)
233 *> If JOBU = 'U', U contains the M-by-M orthogonal matrix U.
234 *> If JOBU = 'N', U is not referenced.
235 *> \endverbatim
236 *>
237 *> \param[in] LDU
238 *> \verbatim
239 *> LDU is INTEGER
240 *> The leading dimension of the array U. LDU >= max(1,M) if
241 *> JOBU = 'U'; LDU >= 1 otherwise.
242 *> \endverbatim
243 *>
244 *> \param[out] V
245 *> \verbatim
246 *> V is REAL array, dimension (LDV,P)
247 *> If JOBV = 'V', V contains the P-by-P orthogonal matrix V.
248 *> If JOBV = 'N', V is not referenced.
249 *> \endverbatim
250 *>
251 *> \param[in] LDV
252 *> \verbatim
253 *> LDV is INTEGER
254 *> The leading dimension of the array V. LDV >= max(1,P) if
255 *> JOBV = 'V'; LDV >= 1 otherwise.
256 *> \endverbatim
257 *>
258 *> \param[out] Q
259 *> \verbatim
260 *> Q is REAL array, dimension (LDQ,N)
261 *> If JOBQ = 'Q', Q contains the N-by-N orthogonal matrix Q.
262 *> If JOBQ = 'N', Q is not referenced.
263 *> \endverbatim
264 *>
265 *> \param[in] LDQ
266 *> \verbatim
267 *> LDQ is INTEGER
268 *> The leading dimension of the array Q. LDQ >= max(1,N) if
269 *> JOBQ = 'Q'; LDQ >= 1 otherwise.
270 *> \endverbatim
271 *>
272 *> \param[out] WORK
273 *> \verbatim
274 *> WORK is REAL array, dimension (MAX(1,LWORK))
275 *> On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
276 *> \endverbatim
277 *>
278 *> \param[in] LWORK
279 *> \verbatim
280 *> LWORK is INTEGER
281 *> The dimension of the array WORK.
282 *>
283 *> If LWORK = -1, then a workspace query is assumed; the routine
284 *> only calculates the optimal size of the WORK array, returns
285 *> this value as the first entry of the WORK array, and no error
286 *> message related to LWORK is issued by XERBLA.
287 *> \endverbatim
288 *>
289 *> \param[out] IWORK
290 *> \verbatim
291 *> IWORK is INTEGER array, dimension (N)
292 *> On exit, IWORK stores the sorting information. More
293 *> precisely, the following loop will sort ALPHA
294 *> for I = K+1, min(M,K+L)
295 *> swap ALPHA(I) and ALPHA(IWORK(I))
296 *> endfor
297 *> such that ALPHA(1) >= ALPHA(2) >= ... >= ALPHA(N).
298 *> \endverbatim
299 *>
300 *> \param[out] INFO
301 *> \verbatim
302 *> INFO is INTEGER
303 *> = 0: successful exit.
304 *> < 0: if INFO = -i, the i-th argument had an illegal value.
305 *> > 0: if INFO = 1, the Jacobi-type procedure failed to
306 *> converge. For further details, see subroutine STGSJA.
307 *> \endverbatim
308 *
309 *> \par Internal Parameters:
310 * =========================
311 *>
312 *> \verbatim
313 *> TOLA REAL
314 *> TOLB REAL
315 *> TOLA and TOLB are the thresholds to determine the effective
316 *> rank of (A**T,B**T)**T. Generally, they are set to
317 *> TOLA = MAX(M,N)*norm(A)*MACHEPS,
318 *> TOLB = MAX(P,N)*norm(B)*MACHEPS.
319 *> The size of TOLA and TOLB may affect the size of backward
320 *> errors of the decomposition.
321 *> \endverbatim
322 *
323 * Authors:
324 * ========
325 *
326 *> \author Univ. of Tennessee
327 *> \author Univ. of California Berkeley
328 *> \author Univ. of Colorado Denver
329 *> \author NAG Ltd.
330 *
331 *> \date August 2015
332 *
333 *> \ingroup realOTHERsing
334 *
335 *> \par Contributors:
336 * ==================
337 *>
338 *> Ming Gu and Huan Ren, Computer Science Division, University of
339 *> California at Berkeley, USA
340 *>
341 *
342 *> \par Further Details:
343 * =====================
344 *>
345 *> SGGSVD3 replaces the deprecated subroutine SGGSVD.
346 *>
347 * =====================================================================
348  SUBROUTINE sggsvd3( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B,
349  $ ldb, alpha, beta, u, ldu, v, ldv, q, ldq,
350  $ work, lwork, iwork, info )
351 *
352 * -- LAPACK driver routine (version 3.6.0) --
353 * -- LAPACK is a software package provided by Univ. of Tennessee, --
354 * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
355 * August 2015
356 *
357 * .. Scalar Arguments ..
358  CHARACTER JOBQ, JOBU, JOBV
359  INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P,
360  $ lwork
361 * ..
362 * .. Array Arguments ..
363  INTEGER IWORK( * )
364  REAL A( lda, * ), ALPHA( * ), B( ldb, * ),
365  $ beta( * ), q( ldq, * ), u( ldu, * ),
366  $ v( ldv, * ), work( * )
367 * ..
368 *
369 * =====================================================================
370 *
371 * .. Local Scalars ..
372  LOGICAL WANTQ, WANTU, WANTV, LQUERY
373  INTEGER I, IBND, ISUB, J, NCYCLE, LWKOPT
374  REAL ANORM, BNORM, SMAX, TEMP, TOLA, TOLB, ULP, UNFL
375 * ..
376 * .. External Functions ..
377  LOGICAL LSAME
378  REAL SLAMCH, SLANGE
379  EXTERNAL lsame, slamch, slange
380 * ..
381 * .. External Subroutines ..
382  EXTERNAL scopy, sggsvp3, stgsja, xerbla
383 * ..
384 * .. Intrinsic Functions ..
385  INTRINSIC max, min
386 * ..
387 * .. Executable Statements ..
388 *
389 * Decode and test the input parameters
390 *
391  wantu = lsame( jobu, 'U' )
392  wantv = lsame( jobv, 'V' )
393  wantq = lsame( jobq, 'Q' )
394  lquery = ( lwork.EQ.-1 )
395  lwkopt = 1
396 *
397 * Test the input arguments
398 *
399  info = 0
400  IF( .NOT.( wantu .OR. lsame( jobu, 'N' ) ) ) THEN
401  info = -1
402  ELSE IF( .NOT.( wantv .OR. lsame( jobv, 'N' ) ) ) THEN
403  info = -2
404  ELSE IF( .NOT.( wantq .OR. lsame( jobq, 'N' ) ) ) THEN
405  info = -3
406  ELSE IF( m.LT.0 ) THEN
407  info = -4
408  ELSE IF( n.LT.0 ) THEN
409  info = -5
410  ELSE IF( p.LT.0 ) THEN
411  info = -6
412  ELSE IF( lda.LT.max( 1, m ) ) THEN
413  info = -10
414  ELSE IF( ldb.LT.max( 1, p ) ) THEN
415  info = -12
416  ELSE IF( ldu.LT.1 .OR. ( wantu .AND. ldu.LT.m ) ) THEN
417  info = -16
418  ELSE IF( ldv.LT.1 .OR. ( wantv .AND. ldv.LT.p ) ) THEN
419  info = -18
420  ELSE IF( ldq.LT.1 .OR. ( wantq .AND. ldq.LT.n ) ) THEN
421  info = -20
422  ELSE IF( lwork.LT.1 .AND. .NOT.lquery ) THEN
423  info = -24
424  END IF
425 *
426 * Compute workspace
427 *
428  IF( info.EQ.0 ) THEN
429  CALL sggsvp3( jobu, jobv, jobq, m, p, n, a, lda, b, ldb, tola,
430  $ tolb, k, l, u, ldu, v, ldv, q, ldq, iwork, work,
431  $ work, -1, info )
432  lwkopt = n + int( work( 1 ) )
433  lwkopt = max( 2*n, lwkopt )
434  lwkopt = max( 1, lwkopt )
435  work( 1 ) = REAL( lwkopt )
436  END IF
437 *
438  IF( info.NE.0 ) THEN
439  CALL xerbla( 'SGGSVD3', -info )
440  RETURN
441  END IF
442  IF( lquery ) THEN
443  RETURN
444  ENDIF
445 *
446 * Compute the Frobenius norm of matrices A and B
447 *
448  anorm = slange( '1', m, n, a, lda, work )
449  bnorm = slange( '1', p, n, b, ldb, work )
450 *
451 * Get machine precision and set up threshold for determining
452 * the effective numerical rank of the matrices A and B.
453 *
454  ulp = slamch( 'Precision' )
455  unfl = slamch( 'Safe Minimum' )
456  tola = max( m, n )*max( anorm, unfl )*ulp
457  tolb = max( p, n )*max( bnorm, unfl )*ulp
458 *
459 * Preprocessing
460 *
461  CALL sggsvp3( jobu, jobv, jobq, m, p, n, a, lda, b, ldb, tola,
462  $ tolb, k, l, u, ldu, v, ldv, q, ldq, iwork, work,
463  $ work( n+1 ), lwork-n, info )
464 *
465 * Compute the GSVD of two upper "triangular" matrices
466 *
467  CALL stgsja( jobu, jobv, jobq, m, p, n, k, l, a, lda, b, ldb,
468  $ tola, tolb, alpha, beta, u, ldu, v, ldv, q, ldq,
469  $ work, ncycle, info )
470 *
471 * Sort the singular values and store the pivot indices in IWORK
472 * Copy ALPHA to WORK, then sort ALPHA in WORK
473 *
474  CALL scopy( n, alpha, 1, work, 1 )
475  ibnd = min( l, m-k )
476  DO 20 i = 1, ibnd
477 *
478 * Scan for largest ALPHA(K+I)
479 *
480  isub = i
481  smax = work( k+i )
482  DO 10 j = i + 1, ibnd
483  temp = work( k+j )
484  IF( temp.GT.smax ) THEN
485  isub = j
486  smax = temp
487  END IF
488  10 CONTINUE
489  IF( isub.NE.i ) THEN
490  work( k+isub ) = work( k+i )
491  work( k+i ) = smax
492  iwork( k+i ) = k + isub
493  ELSE
494  iwork( k+i ) = k + i
495  END IF
496  20 CONTINUE
497 *
498  work( 1 ) = REAL( lwkopt )
499  RETURN
500 *
501 * End of SGGSVD3
502 *
503  END
subroutine sggsvd3(JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B, LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK, LWORK, IWORK, INFO)
SGGSVD3 computes the singular value decomposition (SVD) for OTHER matrices
Definition: sggsvd3.f:351
subroutine sggsvp3(JOBU, JOBV, JOBQ, M, P, N, A, LDA, B, LDB, TOLA, TOLB, K, L, U, LDU, V, LDV, Q, LDQ, IWORK, TAU, WORK, LWORK, INFO)
SGGSVP3
Definition: sggsvp3.f:274
subroutine xerbla(SRNAME, INFO)
XERBLA
Definition: xerbla.f:62
subroutine scopy(N, SX, INCX, SY, INCY)
SCOPY
Definition: scopy.f:53
subroutine stgsja(JOBU, JOBV, JOBQ, M, P, N, K, L, A, LDA, B, LDB, TOLA, TOLB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK, NCYCLE, INFO)
STGSJA
Definition: stgsja.f:380