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slasdq.f 13 kB

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  1. *> \brief \b SLASDQ computes the SVD of a real bidiagonal matrix with diagonal d and off-diagonal e. Used by sbdsdc.
  2. *
  3. * =========== DOCUMENTATION ===========
  4. *
  5. * Online html documentation available at
  6. * http://www.netlib.org/lapack/explore-html/
  7. *
  8. *> \htmlonly
  9. *> Download SLASDQ + dependencies
  10. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/slasdq.f">
  11. *> [TGZ]</a>
  12. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/slasdq.f">
  13. *> [ZIP]</a>
  14. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/slasdq.f">
  15. *> [TXT]</a>
  16. *> \endhtmlonly
  17. *
  18. * Definition:
  19. * ===========
  20. *
  21. * SUBROUTINE SLASDQ( UPLO, SQRE, N, NCVT, NRU, NCC, D, E, VT, LDVT,
  22. * U, LDU, C, LDC, WORK, INFO )
  23. *
  24. * .. Scalar Arguments ..
  25. * CHARACTER UPLO
  26. * INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU, SQRE
  27. * ..
  28. * .. Array Arguments ..
  29. * REAL C( LDC, * ), D( * ), E( * ), U( LDU, * ),
  30. * $ VT( LDVT, * ), WORK( * )
  31. * ..
  32. *
  33. *
  34. *> \par Purpose:
  35. * =============
  36. *>
  37. *> \verbatim
  38. *>
  39. *> SLASDQ computes the singular value decomposition (SVD) of a real
  40. *> (upper or lower) bidiagonal matrix with diagonal D and offdiagonal
  41. *> E, accumulating the transformations if desired. Letting B denote
  42. *> the input bidiagonal matrix, the algorithm computes orthogonal
  43. *> matrices Q and P such that B = Q * S * P**T (P**T denotes the transpose
  44. *> of P). The singular values S are overwritten on D.
  45. *>
  46. *> The input matrix U is changed to U * Q if desired.
  47. *> The input matrix VT is changed to P**T * VT if desired.
  48. *> The input matrix C is changed to Q**T * C if desired.
  49. *>
  50. *> See "Computing Small Singular Values of Bidiagonal Matrices With
  51. *> Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan,
  52. *> LAPACK Working Note #3, for a detailed description of the algorithm.
  53. *> \endverbatim
  54. *
  55. * Arguments:
  56. * ==========
  57. *
  58. *> \param[in] UPLO
  59. *> \verbatim
  60. *> UPLO is CHARACTER*1
  61. *> On entry, UPLO specifies whether the input bidiagonal matrix
  62. *> is upper or lower bidiagonal, and whether it is square are
  63. *> not.
  64. *> UPLO = 'U' or 'u' B is upper bidiagonal.
  65. *> UPLO = 'L' or 'l' B is lower bidiagonal.
  66. *> \endverbatim
  67. *>
  68. *> \param[in] SQRE
  69. *> \verbatim
  70. *> SQRE is INTEGER
  71. *> = 0: then the input matrix is N-by-N.
  72. *> = 1: then the input matrix is N-by-(N+1) if UPLU = 'U' and
  73. *> (N+1)-by-N if UPLU = 'L'.
  74. *>
  75. *> The bidiagonal matrix has
  76. *> N = NL + NR + 1 rows and
  77. *> M = N + SQRE >= N columns.
  78. *> \endverbatim
  79. *>
  80. *> \param[in] N
  81. *> \verbatim
  82. *> N is INTEGER
  83. *> On entry, N specifies the number of rows and columns
  84. *> in the matrix. N must be at least 0.
  85. *> \endverbatim
  86. *>
  87. *> \param[in] NCVT
  88. *> \verbatim
  89. *> NCVT is INTEGER
  90. *> On entry, NCVT specifies the number of columns of
  91. *> the matrix VT. NCVT must be at least 0.
  92. *> \endverbatim
  93. *>
  94. *> \param[in] NRU
  95. *> \verbatim
  96. *> NRU is INTEGER
  97. *> On entry, NRU specifies the number of rows of
  98. *> the matrix U. NRU must be at least 0.
  99. *> \endverbatim
  100. *>
  101. *> \param[in] NCC
  102. *> \verbatim
  103. *> NCC is INTEGER
  104. *> On entry, NCC specifies the number of columns of
  105. *> the matrix C. NCC must be at least 0.
  106. *> \endverbatim
  107. *>
  108. *> \param[in,out] D
  109. *> \verbatim
  110. *> D is REAL array, dimension (N)
  111. *> On entry, D contains the diagonal entries of the
  112. *> bidiagonal matrix whose SVD is desired. On normal exit,
  113. *> D contains the singular values in ascending order.
  114. *> \endverbatim
  115. *>
  116. *> \param[in,out] E
  117. *> \verbatim
  118. *> E is REAL array.
  119. *> dimension is (N-1) if SQRE = 0 and N if SQRE = 1.
  120. *> On entry, the entries of E contain the offdiagonal entries
  121. *> of the bidiagonal matrix whose SVD is desired. On normal
  122. *> exit, E will contain 0. If the algorithm does not converge,
  123. *> D and E will contain the diagonal and superdiagonal entries
  124. *> of a bidiagonal matrix orthogonally equivalent to the one
  125. *> given as input.
  126. *> \endverbatim
  127. *>
  128. *> \param[in,out] VT
  129. *> \verbatim
  130. *> VT is REAL array, dimension (LDVT, NCVT)
  131. *> On entry, contains a matrix which on exit has been
  132. *> premultiplied by P**T, dimension N-by-NCVT if SQRE = 0
  133. *> and (N+1)-by-NCVT if SQRE = 1 (not referenced if NCVT=0).
  134. *> \endverbatim
  135. *>
  136. *> \param[in] LDVT
  137. *> \verbatim
  138. *> LDVT is INTEGER
  139. *> On entry, LDVT specifies the leading dimension of VT as
  140. *> declared in the calling (sub) program. LDVT must be at
  141. *> least 1. If NCVT is nonzero LDVT must also be at least N.
  142. *> \endverbatim
  143. *>
  144. *> \param[in,out] U
  145. *> \verbatim
  146. *> U is REAL array, dimension (LDU, N)
  147. *> On entry, contains a matrix which on exit has been
  148. *> postmultiplied by Q, dimension NRU-by-N if SQRE = 0
  149. *> and NRU-by-(N+1) if SQRE = 1 (not referenced if NRU=0).
  150. *> \endverbatim
  151. *>
  152. *> \param[in] LDU
  153. *> \verbatim
  154. *> LDU is INTEGER
  155. *> On entry, LDU specifies the leading dimension of U as
  156. *> declared in the calling (sub) program. LDU must be at
  157. *> least max( 1, NRU ) .
  158. *> \endverbatim
  159. *>
  160. *> \param[in,out] C
  161. *> \verbatim
  162. *> C is REAL array, dimension (LDC, NCC)
  163. *> On entry, contains an N-by-NCC matrix which on exit
  164. *> has been premultiplied by Q**T dimension N-by-NCC if SQRE = 0
  165. *> and (N+1)-by-NCC if SQRE = 1 (not referenced if NCC=0).
  166. *> \endverbatim
  167. *>
  168. *> \param[in] LDC
  169. *> \verbatim
  170. *> LDC is INTEGER
  171. *> On entry, LDC specifies the leading dimension of C as
  172. *> declared in the calling (sub) program. LDC must be at
  173. *> least 1. If NCC is nonzero, LDC must also be at least N.
  174. *> \endverbatim
  175. *>
  176. *> \param[out] WORK
  177. *> \verbatim
  178. *> WORK is REAL array, dimension (4*N)
  179. *> Workspace. Only referenced if one of NCVT, NRU, or NCC is
  180. *> nonzero, and if N is at least 2.
  181. *> \endverbatim
  182. *>
  183. *> \param[out] INFO
  184. *> \verbatim
  185. *> INFO is INTEGER
  186. *> On exit, a value of 0 indicates a successful exit.
  187. *> If INFO < 0, argument number -INFO is illegal.
  188. *> If INFO > 0, the algorithm did not converge, and INFO
  189. *> specifies how many superdiagonals did not converge.
  190. *> \endverbatim
  191. *
  192. * Authors:
  193. * ========
  194. *
  195. *> \author Univ. of Tennessee
  196. *> \author Univ. of California Berkeley
  197. *> \author Univ. of Colorado Denver
  198. *> \author NAG Ltd.
  199. *
  200. *> \ingroup OTHERauxiliary
  201. *
  202. *> \par Contributors:
  203. * ==================
  204. *>
  205. *> Ming Gu and Huan Ren, Computer Science Division, University of
  206. *> California at Berkeley, USA
  207. *>
  208. * =====================================================================
  209. SUBROUTINE SLASDQ( UPLO, SQRE, N, NCVT, NRU, NCC, D, E, VT, LDVT,
  210. $ U, LDU, C, LDC, WORK, INFO )
  211. *
  212. * -- LAPACK auxiliary routine --
  213. * -- LAPACK is a software package provided by Univ. of Tennessee, --
  214. * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
  215. *
  216. * .. Scalar Arguments ..
  217. CHARACTER UPLO
  218. INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU, SQRE
  219. * ..
  220. * .. Array Arguments ..
  221. REAL C( LDC, * ), D( * ), E( * ), U( LDU, * ),
  222. $ VT( LDVT, * ), WORK( * )
  223. * ..
  224. *
  225. * =====================================================================
  226. *
  227. * .. Parameters ..
  228. REAL ZERO
  229. PARAMETER ( ZERO = 0.0E+0 )
  230. * ..
  231. * .. Local Scalars ..
  232. LOGICAL ROTATE
  233. INTEGER I, ISUB, IUPLO, J, NP1, SQRE1
  234. REAL CS, R, SMIN, SN
  235. * ..
  236. * .. External Subroutines ..
  237. EXTERNAL SBDSQR, SLARTG, SLASR, SSWAP, XERBLA
  238. * ..
  239. * .. External Functions ..
  240. LOGICAL LSAME
  241. EXTERNAL LSAME
  242. * ..
  243. * .. Intrinsic Functions ..
  244. INTRINSIC MAX
  245. * ..
  246. * .. Executable Statements ..
  247. *
  248. * Test the input parameters.
  249. *
  250. INFO = 0
  251. IUPLO = 0
  252. IF( LSAME( UPLO, 'U' ) )
  253. $ IUPLO = 1
  254. IF( LSAME( UPLO, 'L' ) )
  255. $ IUPLO = 2
  256. IF( IUPLO.EQ.0 ) THEN
  257. INFO = -1
  258. ELSE IF( ( SQRE.LT.0 ) .OR. ( SQRE.GT.1 ) ) THEN
  259. INFO = -2
  260. ELSE IF( N.LT.0 ) THEN
  261. INFO = -3
  262. ELSE IF( NCVT.LT.0 ) THEN
  263. INFO = -4
  264. ELSE IF( NRU.LT.0 ) THEN
  265. INFO = -5
  266. ELSE IF( NCC.LT.0 ) THEN
  267. INFO = -6
  268. ELSE IF( ( NCVT.EQ.0 .AND. LDVT.LT.1 ) .OR.
  269. $ ( NCVT.GT.0 .AND. LDVT.LT.MAX( 1, N ) ) ) THEN
  270. INFO = -10
  271. ELSE IF( LDU.LT.MAX( 1, NRU ) ) THEN
  272. INFO = -12
  273. ELSE IF( ( NCC.EQ.0 .AND. LDC.LT.1 ) .OR.
  274. $ ( NCC.GT.0 .AND. LDC.LT.MAX( 1, N ) ) ) THEN
  275. INFO = -14
  276. END IF
  277. IF( INFO.NE.0 ) THEN
  278. CALL XERBLA( 'SLASDQ', -INFO )
  279. RETURN
  280. END IF
  281. IF( N.EQ.0 )
  282. $ RETURN
  283. *
  284. * ROTATE is true if any singular vectors desired, false otherwise
  285. *
  286. ROTATE = ( NCVT.GT.0 ) .OR. ( NRU.GT.0 ) .OR. ( NCC.GT.0 )
  287. NP1 = N + 1
  288. SQRE1 = SQRE
  289. *
  290. * If matrix non-square upper bidiagonal, rotate to be lower
  291. * bidiagonal. The rotations are on the right.
  292. *
  293. IF( ( IUPLO.EQ.1 ) .AND. ( SQRE1.EQ.1 ) ) THEN
  294. DO 10 I = 1, N - 1
  295. CALL SLARTG( D( I ), E( I ), CS, SN, R )
  296. D( I ) = R
  297. E( I ) = SN*D( I+1 )
  298. D( I+1 ) = CS*D( I+1 )
  299. IF( ROTATE ) THEN
  300. WORK( I ) = CS
  301. WORK( N+I ) = SN
  302. END IF
  303. 10 CONTINUE
  304. CALL SLARTG( D( N ), E( N ), CS, SN, R )
  305. D( N ) = R
  306. E( N ) = ZERO
  307. IF( ROTATE ) THEN
  308. WORK( N ) = CS
  309. WORK( N+N ) = SN
  310. END IF
  311. IUPLO = 2
  312. SQRE1 = 0
  313. *
  314. * Update singular vectors if desired.
  315. *
  316. IF( NCVT.GT.0 )
  317. $ CALL SLASR( 'L', 'V', 'F', NP1, NCVT, WORK( 1 ),
  318. $ WORK( NP1 ), VT, LDVT )
  319. END IF
  320. *
  321. * If matrix lower bidiagonal, rotate to be upper bidiagonal
  322. * by applying Givens rotations on the left.
  323. *
  324. IF( IUPLO.EQ.2 ) THEN
  325. DO 20 I = 1, N - 1
  326. CALL SLARTG( D( I ), E( I ), CS, SN, R )
  327. D( I ) = R
  328. E( I ) = SN*D( I+1 )
  329. D( I+1 ) = CS*D( I+1 )
  330. IF( ROTATE ) THEN
  331. WORK( I ) = CS
  332. WORK( N+I ) = SN
  333. END IF
  334. 20 CONTINUE
  335. *
  336. * If matrix (N+1)-by-N lower bidiagonal, one additional
  337. * rotation is needed.
  338. *
  339. IF( SQRE1.EQ.1 ) THEN
  340. CALL SLARTG( D( N ), E( N ), CS, SN, R )
  341. D( N ) = R
  342. IF( ROTATE ) THEN
  343. WORK( N ) = CS
  344. WORK( N+N ) = SN
  345. END IF
  346. END IF
  347. *
  348. * Update singular vectors if desired.
  349. *
  350. IF( NRU.GT.0 ) THEN
  351. IF( SQRE1.EQ.0 ) THEN
  352. CALL SLASR( 'R', 'V', 'F', NRU, N, WORK( 1 ),
  353. $ WORK( NP1 ), U, LDU )
  354. ELSE
  355. CALL SLASR( 'R', 'V', 'F', NRU, NP1, WORK( 1 ),
  356. $ WORK( NP1 ), U, LDU )
  357. END IF
  358. END IF
  359. IF( NCC.GT.0 ) THEN
  360. IF( SQRE1.EQ.0 ) THEN
  361. CALL SLASR( 'L', 'V', 'F', N, NCC, WORK( 1 ),
  362. $ WORK( NP1 ), C, LDC )
  363. ELSE
  364. CALL SLASR( 'L', 'V', 'F', NP1, NCC, WORK( 1 ),
  365. $ WORK( NP1 ), C, LDC )
  366. END IF
  367. END IF
  368. END IF
  369. *
  370. * Call SBDSQR to compute the SVD of the reduced real
  371. * N-by-N upper bidiagonal matrix.
  372. *
  373. CALL SBDSQR( 'U', N, NCVT, NRU, NCC, D, E, VT, LDVT, U, LDU, C,
  374. $ LDC, WORK, INFO )
  375. *
  376. * Sort the singular values into ascending order (insertion sort on
  377. * singular values, but only one transposition per singular vector)
  378. *
  379. DO 40 I = 1, N
  380. *
  381. * Scan for smallest D(I).
  382. *
  383. ISUB = I
  384. SMIN = D( I )
  385. DO 30 J = I + 1, N
  386. IF( D( J ).LT.SMIN ) THEN
  387. ISUB = J
  388. SMIN = D( J )
  389. END IF
  390. 30 CONTINUE
  391. IF( ISUB.NE.I ) THEN
  392. *
  393. * Swap singular values and vectors.
  394. *
  395. D( ISUB ) = D( I )
  396. D( I ) = SMIN
  397. IF( NCVT.GT.0 )
  398. $ CALL SSWAP( NCVT, VT( ISUB, 1 ), LDVT, VT( I, 1 ), LDVT )
  399. IF( NRU.GT.0 )
  400. $ CALL SSWAP( NRU, U( 1, ISUB ), 1, U( 1, I ), 1 )
  401. IF( NCC.GT.0 )
  402. $ CALL SSWAP( NCC, C( ISUB, 1 ), LDC, C( I, 1 ), LDC )
  403. END IF
  404. 40 CONTINUE
  405. *
  406. RETURN
  407. *
  408. * End of SLASDQ
  409. *
  410. END