LAPACK 3.11.0
LAPACK: Linear Algebra PACKage
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slalsd.f
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1*> \brief \b SLALSD uses the singular value decomposition of A to solve the least squares problem.
2*
3* =========== DOCUMENTATION ===========
4*
5* Online html documentation available at
6* http://www.netlib.org/lapack/explore-html/
7*
8*> \htmlonly
9*> Download SLALSD + dependencies
10*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/slalsd.f">
11*> [TGZ]</a>
12*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/slalsd.f">
13*> [ZIP]</a>
14*> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/slalsd.f">
15*> [TXT]</a>
16*> \endhtmlonly
17*
18* Definition:
19* ===========
20*
21* SUBROUTINE SLALSD( UPLO, SMLSIZ, N, NRHS, D, E, B, LDB, RCOND,
22* RANK, WORK, IWORK, INFO )
23*
24* .. Scalar Arguments ..
25* CHARACTER UPLO
26* INTEGER INFO, LDB, N, NRHS, RANK, SMLSIZ
27* REAL RCOND
28* ..
29* .. Array Arguments ..
30* INTEGER IWORK( * )
31* REAL B( LDB, * ), D( * ), E( * ), WORK( * )
32* ..
33*
34*
35*> \par Purpose:
36* =============
37*>
38*> \verbatim
39*>
40*> SLALSD uses the singular value decomposition of A to solve the least
41*> squares problem of finding X to minimize the Euclidean norm of each
42*> column of A*X-B, where A is N-by-N upper bidiagonal, and X and B
43*> are N-by-NRHS. The solution X overwrites B.
44*>
45*> The singular values of A smaller than RCOND times the largest
46*> singular value are treated as zero in solving the least squares
47*> problem; in this case a minimum norm solution is returned.
48*> The actual singular values are returned in D in ascending order.
49*>
50*> This code makes very mild assumptions about floating point
51*> arithmetic. It will work on machines with a guard digit in
52*> add/subtract, or on those binary machines without guard digits
53*> which subtract like the Cray XMP, Cray YMP, Cray C 90, or Cray 2.
54*> It could conceivably fail on hexadecimal or decimal machines
55*> without guard digits, but we know of none.
56*> \endverbatim
57*
58* Arguments:
59* ==========
60*
61*> \param[in] UPLO
62*> \verbatim
63*> UPLO is CHARACTER*1
64*> = 'U': D and E define an upper bidiagonal matrix.
65*> = 'L': D and E define a lower bidiagonal matrix.
66*> \endverbatim
67*>
68*> \param[in] SMLSIZ
69*> \verbatim
70*> SMLSIZ is INTEGER
71*> The maximum size of the subproblems at the bottom of the
72*> computation tree.
73*> \endverbatim
74*>
75*> \param[in] N
76*> \verbatim
77*> N is INTEGER
78*> The dimension of the bidiagonal matrix. N >= 0.
79*> \endverbatim
80*>
81*> \param[in] NRHS
82*> \verbatim
83*> NRHS is INTEGER
84*> The number of columns of B. NRHS must be at least 1.
85*> \endverbatim
86*>
87*> \param[in,out] D
88*> \verbatim
89*> D is REAL array, dimension (N)
90*> On entry D contains the main diagonal of the bidiagonal
91*> matrix. On exit, if INFO = 0, D contains its singular values.
92*> \endverbatim
93*>
94*> \param[in,out] E
95*> \verbatim
96*> E is REAL array, dimension (N-1)
97*> Contains the super-diagonal entries of the bidiagonal matrix.
98*> On exit, E has been destroyed.
99*> \endverbatim
100*>
101*> \param[in,out] B
102*> \verbatim
103*> B is REAL array, dimension (LDB,NRHS)
104*> On input, B contains the right hand sides of the least
105*> squares problem. On output, B contains the solution X.
106*> \endverbatim
107*>
108*> \param[in] LDB
109*> \verbatim
110*> LDB is INTEGER
111*> The leading dimension of B in the calling subprogram.
112*> LDB must be at least max(1,N).
113*> \endverbatim
114*>
115*> \param[in] RCOND
116*> \verbatim
117*> RCOND is REAL
118*> The singular values of A less than or equal to RCOND times
119*> the largest singular value are treated as zero in solving
120*> the least squares problem. If RCOND is negative,
121*> machine precision is used instead.
122*> For example, if diag(S)*X=B were the least squares problem,
123*> where diag(S) is a diagonal matrix of singular values, the
124*> solution would be X(i) = B(i) / S(i) if S(i) is greater than
125*> RCOND*max(S), and X(i) = 0 if S(i) is less than or equal to
126*> RCOND*max(S).
127*> \endverbatim
128*>
129*> \param[out] RANK
130*> \verbatim
131*> RANK is INTEGER
132*> The number of singular values of A greater than RCOND times
133*> the largest singular value.
134*> \endverbatim
135*>
136*> \param[out] WORK
137*> \verbatim
138*> WORK is REAL array, dimension at least
139*> (9*N + 2*N*SMLSIZ + 8*N*NLVL + N*NRHS + (SMLSIZ+1)**2),
140*> where NLVL = max(0, INT(log_2 (N/(SMLSIZ+1))) + 1).
141*> \endverbatim
142*>
143*> \param[out] IWORK
144*> \verbatim
145*> IWORK is INTEGER array, dimension at least
146*> (3*N*NLVL + 11*N)
147*> \endverbatim
148*>
149*> \param[out] INFO
150*> \verbatim
151*> INFO is INTEGER
152*> = 0: successful exit.
153*> < 0: if INFO = -i, the i-th argument had an illegal value.
154*> > 0: The algorithm failed to compute a singular value while
155*> working on the submatrix lying in rows and columns
156*> INFO/(N+1) through MOD(INFO,N+1).
157*> \endverbatim
158*
159* Authors:
160* ========
161*
162*> \author Univ. of Tennessee
163*> \author Univ. of California Berkeley
164*> \author Univ. of Colorado Denver
165*> \author NAG Ltd.
166*
167*> \ingroup realOTHERcomputational
168*
169*> \par Contributors:
170* ==================
171*>
172*> Ming Gu and Ren-Cang Li, Computer Science Division, University of
173*> California at Berkeley, USA \n
174*> Osni Marques, LBNL/NERSC, USA \n
175*
176* =====================================================================
177 SUBROUTINE slalsd( UPLO, SMLSIZ, N, NRHS, D, E, B, LDB, RCOND,
178 $ RANK, WORK, IWORK, INFO )
179*
180* -- LAPACK computational routine --
181* -- LAPACK is a software package provided by Univ. of Tennessee, --
182* -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
183*
184* .. Scalar Arguments ..
185 CHARACTER UPLO
186 INTEGER INFO, LDB, N, NRHS, RANK, SMLSIZ
187 REAL RCOND
188* ..
189* .. Array Arguments ..
190 INTEGER IWORK( * )
191 REAL B( LDB, * ), D( * ), E( * ), WORK( * )
192* ..
193*
194* =====================================================================
195*
196* .. Parameters ..
197 REAL ZERO, ONE, TWO
198 parameter( zero = 0.0e0, one = 1.0e0, two = 2.0e0 )
199* ..
200* .. Local Scalars ..
201 INTEGER BX, BXST, C, DIFL, DIFR, GIVCOL, GIVNUM,
202 $ givptr, i, icmpq1, icmpq2, iwk, j, k, nlvl,
203 $ nm1, nsize, nsub, nwork, perm, poles, s, sizei,
204 $ smlszp, sqre, st, st1, u, vt, z
205 REAL CS, EPS, ORGNRM, R, RCND, SN, TOL
206* ..
207* .. External Functions ..
208 INTEGER ISAMAX
209 REAL SLAMCH, SLANST
210 EXTERNAL isamax, slamch, slanst
211* ..
212* .. External Subroutines ..
213 EXTERNAL scopy, sgemm, slacpy, slalsa, slartg, slascl,
215* ..
216* .. Intrinsic Functions ..
217 INTRINSIC abs, int, log, real, sign
218* ..
219* .. Executable Statements ..
220*
221* Test the input parameters.
222*
223 info = 0
224*
225 IF( n.LT.0 ) THEN
226 info = -3
227 ELSE IF( nrhs.LT.1 ) THEN
228 info = -4
229 ELSE IF( ( ldb.LT.1 ) .OR. ( ldb.LT.n ) ) THEN
230 info = -8
231 END IF
232 IF( info.NE.0 ) THEN
233 CALL xerbla( 'SLALSD', -info )
234 RETURN
235 END IF
236*
237 eps = slamch( 'Epsilon' )
238*
239* Set up the tolerance.
240*
241 IF( ( rcond.LE.zero ) .OR. ( rcond.GE.one ) ) THEN
242 rcnd = eps
243 ELSE
244 rcnd = rcond
245 END IF
246*
247 rank = 0
248*
249* Quick return if possible.
250*
251 IF( n.EQ.0 ) THEN
252 RETURN
253 ELSE IF( n.EQ.1 ) THEN
254 IF( d( 1 ).EQ.zero ) THEN
255 CALL slaset( 'A', 1, nrhs, zero, zero, b, ldb )
256 ELSE
257 rank = 1
258 CALL slascl( 'G', 0, 0, d( 1 ), one, 1, nrhs, b, ldb, info )
259 d( 1 ) = abs( d( 1 ) )
260 END IF
261 RETURN
262 END IF
263*
264* Rotate the matrix if it is lower bidiagonal.
265*
266 IF( uplo.EQ.'L' ) THEN
267 DO 10 i = 1, n - 1
268 CALL slartg( d( i ), e( i ), cs, sn, r )
269 d( i ) = r
270 e( i ) = sn*d( i+1 )
271 d( i+1 ) = cs*d( i+1 )
272 IF( nrhs.EQ.1 ) THEN
273 CALL srot( 1, b( i, 1 ), 1, b( i+1, 1 ), 1, cs, sn )
274 ELSE
275 work( i*2-1 ) = cs
276 work( i*2 ) = sn
277 END IF
278 10 CONTINUE
279 IF( nrhs.GT.1 ) THEN
280 DO 30 i = 1, nrhs
281 DO 20 j = 1, n - 1
282 cs = work( j*2-1 )
283 sn = work( j*2 )
284 CALL srot( 1, b( j, i ), 1, b( j+1, i ), 1, cs, sn )
285 20 CONTINUE
286 30 CONTINUE
287 END IF
288 END IF
289*
290* Scale.
291*
292 nm1 = n - 1
293 orgnrm = slanst( 'M', n, d, e )
294 IF( orgnrm.EQ.zero ) THEN
295 CALL slaset( 'A', n, nrhs, zero, zero, b, ldb )
296 RETURN
297 END IF
298*
299 CALL slascl( 'G', 0, 0, orgnrm, one, n, 1, d, n, info )
300 CALL slascl( 'G', 0, 0, orgnrm, one, nm1, 1, e, nm1, info )
301*
302* If N is smaller than the minimum divide size SMLSIZ, then solve
303* the problem with another solver.
304*
305 IF( n.LE.smlsiz ) THEN
306 nwork = 1 + n*n
307 CALL slaset( 'A', n, n, zero, one, work, n )
308 CALL slasdq( 'U', 0, n, n, 0, nrhs, d, e, work, n, work, n, b,
309 $ ldb, work( nwork ), info )
310 IF( info.NE.0 ) THEN
311 RETURN
312 END IF
313 tol = rcnd*abs( d( isamax( n, d, 1 ) ) )
314 DO 40 i = 1, n
315 IF( d( i ).LE.tol ) THEN
316 CALL slaset( 'A', 1, nrhs, zero, zero, b( i, 1 ), ldb )
317 ELSE
318 CALL slascl( 'G', 0, 0, d( i ), one, 1, nrhs, b( i, 1 ),
319 $ ldb, info )
320 rank = rank + 1
321 END IF
322 40 CONTINUE
323 CALL sgemm( 'T', 'N', n, nrhs, n, one, work, n, b, ldb, zero,
324 $ work( nwork ), n )
325 CALL slacpy( 'A', n, nrhs, work( nwork ), n, b, ldb )
326*
327* Unscale.
328*
329 CALL slascl( 'G', 0, 0, one, orgnrm, n, 1, d, n, info )
330 CALL slasrt( 'D', n, d, info )
331 CALL slascl( 'G', 0, 0, orgnrm, one, n, nrhs, b, ldb, info )
332*
333 RETURN
334 END IF
335*
336* Book-keeping and setting up some constants.
337*
338 nlvl = int( log( real( n ) / real( smlsiz+1 ) ) / log( two ) ) + 1
339*
340 smlszp = smlsiz + 1
341*
342 u = 1
343 vt = 1 + smlsiz*n
344 difl = vt + smlszp*n
345 difr = difl + nlvl*n
346 z = difr + nlvl*n*2
347 c = z + nlvl*n
348 s = c + n
349 poles = s + n
350 givnum = poles + 2*nlvl*n
351 bx = givnum + 2*nlvl*n
352 nwork = bx + n*nrhs
353*
354 sizei = 1 + n
355 k = sizei + n
356 givptr = k + n
357 perm = givptr + n
358 givcol = perm + nlvl*n
359 iwk = givcol + nlvl*n*2
360*
361 st = 1
362 sqre = 0
363 icmpq1 = 1
364 icmpq2 = 0
365 nsub = 0
366*
367 DO 50 i = 1, n
368 IF( abs( d( i ) ).LT.eps ) THEN
369 d( i ) = sign( eps, d( i ) )
370 END IF
371 50 CONTINUE
372*
373 DO 60 i = 1, nm1
374 IF( ( abs( e( i ) ).LT.eps ) .OR. ( i.EQ.nm1 ) ) THEN
375 nsub = nsub + 1
376 iwork( nsub ) = st
377*
378* Subproblem found. First determine its size and then
379* apply divide and conquer on it.
380*
381 IF( i.LT.nm1 ) THEN
382*
383* A subproblem with E(I) small for I < NM1.
384*
385 nsize = i - st + 1
386 iwork( sizei+nsub-1 ) = nsize
387 ELSE IF( abs( e( i ) ).GE.eps ) THEN
388*
389* A subproblem with E(NM1) not too small but I = NM1.
390*
391 nsize = n - st + 1
392 iwork( sizei+nsub-1 ) = nsize
393 ELSE
394*
395* A subproblem with E(NM1) small. This implies an
396* 1-by-1 subproblem at D(N), which is not solved
397* explicitly.
398*
399 nsize = i - st + 1
400 iwork( sizei+nsub-1 ) = nsize
401 nsub = nsub + 1
402 iwork( nsub ) = n
403 iwork( sizei+nsub-1 ) = 1
404 CALL scopy( nrhs, b( n, 1 ), ldb, work( bx+nm1 ), n )
405 END IF
406 st1 = st - 1
407 IF( nsize.EQ.1 ) THEN
408*
409* This is a 1-by-1 subproblem and is not solved
410* explicitly.
411*
412 CALL scopy( nrhs, b( st, 1 ), ldb, work( bx+st1 ), n )
413 ELSE IF( nsize.LE.smlsiz ) THEN
414*
415* This is a small subproblem and is solved by SLASDQ.
416*
417 CALL slaset( 'A', nsize, nsize, zero, one,
418 $ work( vt+st1 ), n )
419 CALL slasdq( 'U', 0, nsize, nsize, 0, nrhs, d( st ),
420 $ e( st ), work( vt+st1 ), n, work( nwork ),
421 $ n, b( st, 1 ), ldb, work( nwork ), info )
422 IF( info.NE.0 ) THEN
423 RETURN
424 END IF
425 CALL slacpy( 'A', nsize, nrhs, b( st, 1 ), ldb,
426 $ work( bx+st1 ), n )
427 ELSE
428*
429* A large problem. Solve it using divide and conquer.
430*
431 CALL slasda( icmpq1, smlsiz, nsize, sqre, d( st ),
432 $ e( st ), work( u+st1 ), n, work( vt+st1 ),
433 $ iwork( k+st1 ), work( difl+st1 ),
434 $ work( difr+st1 ), work( z+st1 ),
435 $ work( poles+st1 ), iwork( givptr+st1 ),
436 $ iwork( givcol+st1 ), n, iwork( perm+st1 ),
437 $ work( givnum+st1 ), work( c+st1 ),
438 $ work( s+st1 ), work( nwork ), iwork( iwk ),
439 $ info )
440 IF( info.NE.0 ) THEN
441 RETURN
442 END IF
443 bxst = bx + st1
444 CALL slalsa( icmpq2, smlsiz, nsize, nrhs, b( st, 1 ),
445 $ ldb, work( bxst ), n, work( u+st1 ), n,
446 $ work( vt+st1 ), iwork( k+st1 ),
447 $ work( difl+st1 ), work( difr+st1 ),
448 $ work( z+st1 ), work( poles+st1 ),
449 $ iwork( givptr+st1 ), iwork( givcol+st1 ), n,
450 $ iwork( perm+st1 ), work( givnum+st1 ),
451 $ work( c+st1 ), work( s+st1 ), work( nwork ),
452 $ iwork( iwk ), info )
453 IF( info.NE.0 ) THEN
454 RETURN
455 END IF
456 END IF
457 st = i + 1
458 END IF
459 60 CONTINUE
460*
461* Apply the singular values and treat the tiny ones as zero.
462*
463 tol = rcnd*abs( d( isamax( n, d, 1 ) ) )
464*
465 DO 70 i = 1, n
466*
467* Some of the elements in D can be negative because 1-by-1
468* subproblems were not solved explicitly.
469*
470 IF( abs( d( i ) ).LE.tol ) THEN
471 CALL slaset( 'A', 1, nrhs, zero, zero, work( bx+i-1 ), n )
472 ELSE
473 rank = rank + 1
474 CALL slascl( 'G', 0, 0, d( i ), one, 1, nrhs,
475 $ work( bx+i-1 ), n, info )
476 END IF
477 d( i ) = abs( d( i ) )
478 70 CONTINUE
479*
480* Now apply back the right singular vectors.
481*
482 icmpq2 = 1
483 DO 80 i = 1, nsub
484 st = iwork( i )
485 st1 = st - 1
486 nsize = iwork( sizei+i-1 )
487 bxst = bx + st1
488 IF( nsize.EQ.1 ) THEN
489 CALL scopy( nrhs, work( bxst ), n, b( st, 1 ), ldb )
490 ELSE IF( nsize.LE.smlsiz ) THEN
491 CALL sgemm( 'T', 'N', nsize, nrhs, nsize, one,
492 $ work( vt+st1 ), n, work( bxst ), n, zero,
493 $ b( st, 1 ), ldb )
494 ELSE
495 CALL slalsa( icmpq2, smlsiz, nsize, nrhs, work( bxst ), n,
496 $ b( st, 1 ), ldb, work( u+st1 ), n,
497 $ work( vt+st1 ), iwork( k+st1 ),
498 $ work( difl+st1 ), work( difr+st1 ),
499 $ work( z+st1 ), work( poles+st1 ),
500 $ iwork( givptr+st1 ), iwork( givcol+st1 ), n,
501 $ iwork( perm+st1 ), work( givnum+st1 ),
502 $ work( c+st1 ), work( s+st1 ), work( nwork ),
503 $ iwork( iwk ), info )
504 IF( info.NE.0 ) THEN
505 RETURN
506 END IF
507 END IF
508 80 CONTINUE
509*
510* Unscale and sort the singular values.
511*
512 CALL slascl( 'G', 0, 0, one, orgnrm, n, 1, d, n, info )
513 CALL slasrt( 'D', n, d, info )
514 CALL slascl( 'G', 0, 0, orgnrm, one, n, nrhs, b, ldb, info )
515*
516 RETURN
517*
518* End of SLALSD
519*
520 END
subroutine slascl(TYPE, KL, KU, CFROM, CTO, M, N, A, LDA, INFO)
SLASCL multiplies a general rectangular matrix by a real scalar defined as cto/cfrom.
Definition: slascl.f:143
subroutine slaset(UPLO, M, N, ALPHA, BETA, A, LDA)
SLASET initializes the off-diagonal elements and the diagonal elements of a matrix to given values.
Definition: slaset.f:110
subroutine slacpy(UPLO, M, N, A, LDA, B, LDB)
SLACPY copies all or part of one two-dimensional array to another.
Definition: slacpy.f:103
subroutine slartg(f, g, c, s, r)
SLARTG generates a plane rotation with real cosine and real sine.
Definition: slartg.f90:111
subroutine slasdq(UPLO, SQRE, N, NCVT, NRU, NCC, D, E, VT, LDVT, U, LDU, C, LDC, WORK, INFO)
SLASDQ computes the SVD of a real bidiagonal matrix with diagonal d and off-diagonal e....
Definition: slasdq.f:211
subroutine slasda(ICOMPQ, SMLSIZ, N, SQRE, D, E, U, LDU, VT, K, DIFL, DIFR, Z, POLES, GIVPTR, GIVCOL, LDGCOL, PERM, GIVNUM, C, S, WORK, IWORK, INFO)
SLASDA computes the singular value decomposition (SVD) of a real upper bidiagonal matrix with diagona...
Definition: slasda.f:273
subroutine xerbla(SRNAME, INFO)
XERBLA
Definition: xerbla.f:60
subroutine slasrt(ID, N, D, INFO)
SLASRT sorts numbers in increasing or decreasing order.
Definition: slasrt.f:88
subroutine slalsd(UPLO, SMLSIZ, N, NRHS, D, E, B, LDB, RCOND, RANK, WORK, IWORK, INFO)
SLALSD uses the singular value decomposition of A to solve the least squares problem.
Definition: slalsd.f:179
subroutine slalsa(ICOMPQ, SMLSIZ, N, NRHS, B, LDB, BX, LDBX, U, LDU, VT, K, DIFL, DIFR, Z, POLES, GIVPTR, GIVCOL, LDGCOL, PERM, GIVNUM, C, S, WORK, IWORK, INFO)
SLALSA computes the SVD of the coefficient matrix in compact form. Used by sgelsd.
Definition: slalsa.f:267
subroutine srot(N, SX, INCX, SY, INCY, C, S)
SROT
Definition: srot.f:92
subroutine scopy(N, SX, INCX, SY, INCY)
SCOPY
Definition: scopy.f:82
subroutine sgemm(TRANSA, TRANSB, M, N, K, ALPHA, A, LDA, B, LDB, BETA, C, LDC)
SGEMM
Definition: sgemm.f:187