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dggbal.f 16 kB

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  1. *> \brief \b DGGBAL
  2. *
  3. * =========== DOCUMENTATION ===========
  4. *
  5. * Online html documentation available at
  6. * http://www.netlib.org/lapack/explore-html/
  7. *
  8. *> \htmlonly
  9. *> Download DGGBAL + dependencies
  10. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/dggbal.f">
  11. *> [TGZ]</a>
  12. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/dggbal.f">
  13. *> [ZIP]</a>
  14. *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/dggbal.f">
  15. *> [TXT]</a>
  16. *> \endhtmlonly
  17. *
  18. * Definition:
  19. * ===========
  20. *
  21. * SUBROUTINE DGGBAL( JOB, N, A, LDA, B, LDB, ILO, IHI, LSCALE,
  22. * RSCALE, WORK, INFO )
  23. *
  24. * .. Scalar Arguments ..
  25. * CHARACTER JOB
  26. * INTEGER IHI, ILO, INFO, LDA, LDB, N
  27. * ..
  28. * .. Array Arguments ..
  29. * DOUBLE PRECISION A( LDA, * ), B( LDB, * ), LSCALE( * ),
  30. * $ RSCALE( * ), WORK( * )
  31. * ..
  32. *
  33. *
  34. *> \par Purpose:
  35. * =============
  36. *>
  37. *> \verbatim
  38. *>
  39. *> DGGBAL balances a pair of general real matrices (A,B). This
  40. *> involves, first, permuting A and B by similarity transformations to
  41. *> isolate eigenvalues in the first 1 to ILO$-$1 and last IHI+1 to N
  42. *> elements on the diagonal; and second, applying a diagonal similarity
  43. *> transformation to rows and columns ILO to IHI to make the rows
  44. *> and columns as close in norm as possible. Both steps are optional.
  45. *>
  46. *> Balancing may reduce the 1-norm of the matrices, and improve the
  47. *> accuracy of the computed eigenvalues and/or eigenvectors in the
  48. *> generalized eigenvalue problem A*x = lambda*B*x.
  49. *> \endverbatim
  50. *
  51. * Arguments:
  52. * ==========
  53. *
  54. *> \param[in] JOB
  55. *> \verbatim
  56. *> JOB is CHARACTER*1
  57. *> Specifies the operations to be performed on A and B:
  58. *> = 'N': none: simply set ILO = 1, IHI = N, LSCALE(I) = 1.0
  59. *> and RSCALE(I) = 1.0 for i = 1,...,N.
  60. *> = 'P': permute only;
  61. *> = 'S': scale only;
  62. *> = 'B': both permute and scale.
  63. *> \endverbatim
  64. *>
  65. *> \param[in] N
  66. *> \verbatim
  67. *> N is INTEGER
  68. *> The order of the matrices A and B. N >= 0.
  69. *> \endverbatim
  70. *>
  71. *> \param[in,out] A
  72. *> \verbatim
  73. *> A is DOUBLE PRECISION array, dimension (LDA,N)
  74. *> On entry, the input matrix A.
  75. *> On exit, A is overwritten by the balanced matrix.
  76. *> If JOB = 'N', A is not referenced.
  77. *> \endverbatim
  78. *>
  79. *> \param[in] LDA
  80. *> \verbatim
  81. *> LDA is INTEGER
  82. *> The leading dimension of the array A. LDA >= max(1,N).
  83. *> \endverbatim
  84. *>
  85. *> \param[in,out] B
  86. *> \verbatim
  87. *> B is DOUBLE PRECISION array, dimension (LDB,N)
  88. *> On entry, the input matrix B.
  89. *> On exit, B is overwritten by the balanced matrix.
  90. *> If JOB = 'N', B is not referenced.
  91. *> \endverbatim
  92. *>
  93. *> \param[in] LDB
  94. *> \verbatim
  95. *> LDB is INTEGER
  96. *> The leading dimension of the array B. LDB >= max(1,N).
  97. *> \endverbatim
  98. *>
  99. *> \param[out] ILO
  100. *> \verbatim
  101. *> ILO is INTEGER
  102. *> \endverbatim
  103. *>
  104. *> \param[out] IHI
  105. *> \verbatim
  106. *> IHI is INTEGER
  107. *> ILO and IHI are set to integers such that on exit
  108. *> A(i,j) = 0 and B(i,j) = 0 if i > j and
  109. *> j = 1,...,ILO-1 or i = IHI+1,...,N.
  110. *> If JOB = 'N' or 'S', ILO = 1 and IHI = N.
  111. *> \endverbatim
  112. *>
  113. *> \param[out] LSCALE
  114. *> \verbatim
  115. *> LSCALE is DOUBLE PRECISION array, dimension (N)
  116. *> Details of the permutations and scaling factors applied
  117. *> to the left side of A and B. If P(j) is the index of the
  118. *> row interchanged with row j, and D(j)
  119. *> is the scaling factor applied to row j, then
  120. *> LSCALE(j) = P(j) for J = 1,...,ILO-1
  121. *> = D(j) for J = ILO,...,IHI
  122. *> = P(j) for J = IHI+1,...,N.
  123. *> The order in which the interchanges are made is N to IHI+1,
  124. *> then 1 to ILO-1.
  125. *> \endverbatim
  126. *>
  127. *> \param[out] RSCALE
  128. *> \verbatim
  129. *> RSCALE is DOUBLE PRECISION array, dimension (N)
  130. *> Details of the permutations and scaling factors applied
  131. *> to the right side of A and B. If P(j) is the index of the
  132. *> column interchanged with column j, and D(j)
  133. *> is the scaling factor applied to column j, then
  134. *> LSCALE(j) = P(j) for J = 1,...,ILO-1
  135. *> = D(j) for J = ILO,...,IHI
  136. *> = P(j) for J = IHI+1,...,N.
  137. *> The order in which the interchanges are made is N to IHI+1,
  138. *> then 1 to ILO-1.
  139. *> \endverbatim
  140. *>
  141. *> \param[out] WORK
  142. *> \verbatim
  143. *> WORK is DOUBLE PRECISION array, dimension (lwork)
  144. *> lwork must be at least max(1,6*N) when JOB = 'S' or 'B', and
  145. *> at least 1 when JOB = 'N' or 'P'.
  146. *> \endverbatim
  147. *>
  148. *> \param[out] INFO
  149. *> \verbatim
  150. *> INFO is INTEGER
  151. *> = 0: successful exit
  152. *> < 0: if INFO = -i, the i-th argument had an illegal value.
  153. *> \endverbatim
  154. *
  155. * Authors:
  156. * ========
  157. *
  158. *> \author Univ. of Tennessee
  159. *> \author Univ. of California Berkeley
  160. *> \author Univ. of Colorado Denver
  161. *> \author NAG Ltd.
  162. *
  163. *> \date December 2016
  164. *
  165. *> \ingroup doubleGBcomputational
  166. *
  167. *> \par Further Details:
  168. * =====================
  169. *>
  170. *> \verbatim
  171. *>
  172. *> See R.C. WARD, Balancing the generalized eigenvalue problem,
  173. *> SIAM J. Sci. Stat. Comp. 2 (1981), 141-152.
  174. *> \endverbatim
  175. *>
  176. * =====================================================================
  177. SUBROUTINE DGGBAL( JOB, N, A, LDA, B, LDB, ILO, IHI, LSCALE,
  178. $ RSCALE, WORK, INFO )
  179. *
  180. * -- LAPACK computational routine (version 3.7.0) --
  181. * -- LAPACK is a software package provided by Univ. of Tennessee, --
  182. * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
  183. * December 2016
  184. *
  185. * .. Scalar Arguments ..
  186. CHARACTER JOB
  187. INTEGER IHI, ILO, INFO, LDA, LDB, N
  188. * ..
  189. * .. Array Arguments ..
  190. DOUBLE PRECISION A( LDA, * ), B( LDB, * ), LSCALE( * ),
  191. $ RSCALE( * ), WORK( * )
  192. * ..
  193. *
  194. * =====================================================================
  195. *
  196. * .. Parameters ..
  197. DOUBLE PRECISION ZERO, HALF, ONE
  198. PARAMETER ( ZERO = 0.0D+0, HALF = 0.5D+0, ONE = 1.0D+0 )
  199. DOUBLE PRECISION THREE, SCLFAC
  200. PARAMETER ( THREE = 3.0D+0, SCLFAC = 1.0D+1 )
  201. * ..
  202. * .. Local Scalars ..
  203. INTEGER I, ICAB, IFLOW, IP1, IR, IRAB, IT, J, JC, JP1,
  204. $ K, KOUNT, L, LCAB, LM1, LRAB, LSFMAX, LSFMIN,
  205. $ M, NR, NRP2
  206. DOUBLE PRECISION ALPHA, BASL, BETA, CAB, CMAX, COEF, COEF2,
  207. $ COEF5, COR, EW, EWC, GAMMA, PGAMMA, RAB, SFMAX,
  208. $ SFMIN, SUM, T, TA, TB, TC
  209. * ..
  210. * .. External Functions ..
  211. LOGICAL LSAME
  212. INTEGER IDAMAX
  213. DOUBLE PRECISION DDOT, DLAMCH
  214. EXTERNAL LSAME, IDAMAX, DDOT, DLAMCH
  215. * ..
  216. * .. External Subroutines ..
  217. EXTERNAL DAXPY, DSCAL, DSWAP, XERBLA
  218. * ..
  219. * .. Intrinsic Functions ..
  220. INTRINSIC ABS, DBLE, INT, LOG10, MAX, MIN, SIGN
  221. * ..
  222. * .. Executable Statements ..
  223. *
  224. * Test the input parameters
  225. *
  226. INFO = 0
  227. IF( .NOT.LSAME( JOB, 'N' ) .AND. .NOT.LSAME( JOB, 'P' ) .AND.
  228. $ .NOT.LSAME( JOB, 'S' ) .AND. .NOT.LSAME( JOB, 'B' ) ) THEN
  229. INFO = -1
  230. ELSE IF( N.LT.0 ) THEN
  231. INFO = -2
  232. ELSE IF( LDA.LT.MAX( 1, N ) ) THEN
  233. INFO = -4
  234. ELSE IF( LDB.LT.MAX( 1, N ) ) THEN
  235. INFO = -6
  236. END IF
  237. IF( INFO.NE.0 ) THEN
  238. CALL XERBLA( 'DGGBAL', -INFO )
  239. RETURN
  240. END IF
  241. *
  242. * Quick return if possible
  243. *
  244. IF( N.EQ.0 ) THEN
  245. ILO = 1
  246. IHI = N
  247. RETURN
  248. END IF
  249. *
  250. IF( N.EQ.1 ) THEN
  251. ILO = 1
  252. IHI = N
  253. LSCALE( 1 ) = ONE
  254. RSCALE( 1 ) = ONE
  255. RETURN
  256. END IF
  257. *
  258. IF( LSAME( JOB, 'N' ) ) THEN
  259. ILO = 1
  260. IHI = N
  261. DO 10 I = 1, N
  262. LSCALE( I ) = ONE
  263. RSCALE( I ) = ONE
  264. 10 CONTINUE
  265. RETURN
  266. END IF
  267. *
  268. K = 1
  269. L = N
  270. IF( LSAME( JOB, 'S' ) )
  271. $ GO TO 190
  272. *
  273. GO TO 30
  274. *
  275. * Permute the matrices A and B to isolate the eigenvalues.
  276. *
  277. * Find row with one nonzero in columns 1 through L
  278. *
  279. 20 CONTINUE
  280. L = LM1
  281. IF( L.NE.1 )
  282. $ GO TO 30
  283. *
  284. RSCALE( 1 ) = ONE
  285. LSCALE( 1 ) = ONE
  286. GO TO 190
  287. *
  288. 30 CONTINUE
  289. LM1 = L - 1
  290. DO 80 I = L, 1, -1
  291. DO 40 J = 1, LM1
  292. JP1 = J + 1
  293. IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
  294. $ GO TO 50
  295. 40 CONTINUE
  296. J = L
  297. GO TO 70
  298. *
  299. 50 CONTINUE
  300. DO 60 J = JP1, L
  301. IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
  302. $ GO TO 80
  303. 60 CONTINUE
  304. J = JP1 - 1
  305. *
  306. 70 CONTINUE
  307. M = L
  308. IFLOW = 1
  309. GO TO 160
  310. 80 CONTINUE
  311. GO TO 100
  312. *
  313. * Find column with one nonzero in rows K through N
  314. *
  315. 90 CONTINUE
  316. K = K + 1
  317. *
  318. 100 CONTINUE
  319. DO 150 J = K, L
  320. DO 110 I = K, LM1
  321. IP1 = I + 1
  322. IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
  323. $ GO TO 120
  324. 110 CONTINUE
  325. I = L
  326. GO TO 140
  327. 120 CONTINUE
  328. DO 130 I = IP1, L
  329. IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
  330. $ GO TO 150
  331. 130 CONTINUE
  332. I = IP1 - 1
  333. 140 CONTINUE
  334. M = K
  335. IFLOW = 2
  336. GO TO 160
  337. 150 CONTINUE
  338. GO TO 190
  339. *
  340. * Permute rows M and I
  341. *
  342. 160 CONTINUE
  343. LSCALE( M ) = I
  344. IF( I.EQ.M )
  345. $ GO TO 170
  346. CALL DSWAP( N-K+1, A( I, K ), LDA, A( M, K ), LDA )
  347. CALL DSWAP( N-K+1, B( I, K ), LDB, B( M, K ), LDB )
  348. *
  349. * Permute columns M and J
  350. *
  351. 170 CONTINUE
  352. RSCALE( M ) = J
  353. IF( J.EQ.M )
  354. $ GO TO 180
  355. CALL DSWAP( L, A( 1, J ), 1, A( 1, M ), 1 )
  356. CALL DSWAP( L, B( 1, J ), 1, B( 1, M ), 1 )
  357. *
  358. 180 CONTINUE
  359. GO TO ( 20, 90 )IFLOW
  360. *
  361. 190 CONTINUE
  362. ILO = K
  363. IHI = L
  364. *
  365. IF( LSAME( JOB, 'P' ) ) THEN
  366. DO 195 I = ILO, IHI
  367. LSCALE( I ) = ONE
  368. RSCALE( I ) = ONE
  369. 195 CONTINUE
  370. RETURN
  371. END IF
  372. *
  373. IF( ILO.EQ.IHI )
  374. $ RETURN
  375. *
  376. * Balance the submatrix in rows ILO to IHI.
  377. *
  378. NR = IHI - ILO + 1
  379. DO 200 I = ILO, IHI
  380. RSCALE( I ) = ZERO
  381. LSCALE( I ) = ZERO
  382. *
  383. WORK( I ) = ZERO
  384. WORK( I+N ) = ZERO
  385. WORK( I+2*N ) = ZERO
  386. WORK( I+3*N ) = ZERO
  387. WORK( I+4*N ) = ZERO
  388. WORK( I+5*N ) = ZERO
  389. 200 CONTINUE
  390. *
  391. * Compute right side vector in resulting linear equations
  392. *
  393. BASL = LOG10( SCLFAC )
  394. DO 240 I = ILO, IHI
  395. DO 230 J = ILO, IHI
  396. TB = B( I, J )
  397. TA = A( I, J )
  398. IF( TA.EQ.ZERO )
  399. $ GO TO 210
  400. TA = LOG10( ABS( TA ) ) / BASL
  401. 210 CONTINUE
  402. IF( TB.EQ.ZERO )
  403. $ GO TO 220
  404. TB = LOG10( ABS( TB ) ) / BASL
  405. 220 CONTINUE
  406. WORK( I+4*N ) = WORK( I+4*N ) - TA - TB
  407. WORK( J+5*N ) = WORK( J+5*N ) - TA - TB
  408. 230 CONTINUE
  409. 240 CONTINUE
  410. *
  411. COEF = ONE / DBLE( 2*NR )
  412. COEF2 = COEF*COEF
  413. COEF5 = HALF*COEF2
  414. NRP2 = NR + 2
  415. BETA = ZERO
  416. IT = 1
  417. *
  418. * Start generalized conjugate gradient iteration
  419. *
  420. 250 CONTINUE
  421. *
  422. GAMMA = DDOT( NR, WORK( ILO+4*N ), 1, WORK( ILO+4*N ), 1 ) +
  423. $ DDOT( NR, WORK( ILO+5*N ), 1, WORK( ILO+5*N ), 1 )
  424. *
  425. EW = ZERO
  426. EWC = ZERO
  427. DO 260 I = ILO, IHI
  428. EW = EW + WORK( I+4*N )
  429. EWC = EWC + WORK( I+5*N )
  430. 260 CONTINUE
  431. *
  432. GAMMA = COEF*GAMMA - COEF2*( EW**2+EWC**2 ) - COEF5*( EW-EWC )**2
  433. IF( GAMMA.EQ.ZERO )
  434. $ GO TO 350
  435. IF( IT.NE.1 )
  436. $ BETA = GAMMA / PGAMMA
  437. T = COEF5*( EWC-THREE*EW )
  438. TC = COEF5*( EW-THREE*EWC )
  439. *
  440. CALL DSCAL( NR, BETA, WORK( ILO ), 1 )
  441. CALL DSCAL( NR, BETA, WORK( ILO+N ), 1 )
  442. *
  443. CALL DAXPY( NR, COEF, WORK( ILO+4*N ), 1, WORK( ILO+N ), 1 )
  444. CALL DAXPY( NR, COEF, WORK( ILO+5*N ), 1, WORK( ILO ), 1 )
  445. *
  446. DO 270 I = ILO, IHI
  447. WORK( I ) = WORK( I ) + TC
  448. WORK( I+N ) = WORK( I+N ) + T
  449. 270 CONTINUE
  450. *
  451. * Apply matrix to vector
  452. *
  453. DO 300 I = ILO, IHI
  454. KOUNT = 0
  455. SUM = ZERO
  456. DO 290 J = ILO, IHI
  457. IF( A( I, J ).EQ.ZERO )
  458. $ GO TO 280
  459. KOUNT = KOUNT + 1
  460. SUM = SUM + WORK( J )
  461. 280 CONTINUE
  462. IF( B( I, J ).EQ.ZERO )
  463. $ GO TO 290
  464. KOUNT = KOUNT + 1
  465. SUM = SUM + WORK( J )
  466. 290 CONTINUE
  467. WORK( I+2*N ) = DBLE( KOUNT )*WORK( I+N ) + SUM
  468. 300 CONTINUE
  469. *
  470. DO 330 J = ILO, IHI
  471. KOUNT = 0
  472. SUM = ZERO
  473. DO 320 I = ILO, IHI
  474. IF( A( I, J ).EQ.ZERO )
  475. $ GO TO 310
  476. KOUNT = KOUNT + 1
  477. SUM = SUM + WORK( I+N )
  478. 310 CONTINUE
  479. IF( B( I, J ).EQ.ZERO )
  480. $ GO TO 320
  481. KOUNT = KOUNT + 1
  482. SUM = SUM + WORK( I+N )
  483. 320 CONTINUE
  484. WORK( J+3*N ) = DBLE( KOUNT )*WORK( J ) + SUM
  485. 330 CONTINUE
  486. *
  487. SUM = DDOT( NR, WORK( ILO+N ), 1, WORK( ILO+2*N ), 1 ) +
  488. $ DDOT( NR, WORK( ILO ), 1, WORK( ILO+3*N ), 1 )
  489. ALPHA = GAMMA / SUM
  490. *
  491. * Determine correction to current iteration
  492. *
  493. CMAX = ZERO
  494. DO 340 I = ILO, IHI
  495. COR = ALPHA*WORK( I+N )
  496. IF( ABS( COR ).GT.CMAX )
  497. $ CMAX = ABS( COR )
  498. LSCALE( I ) = LSCALE( I ) + COR
  499. COR = ALPHA*WORK( I )
  500. IF( ABS( COR ).GT.CMAX )
  501. $ CMAX = ABS( COR )
  502. RSCALE( I ) = RSCALE( I ) + COR
  503. 340 CONTINUE
  504. IF( CMAX.LT.HALF )
  505. $ GO TO 350
  506. *
  507. CALL DAXPY( NR, -ALPHA, WORK( ILO+2*N ), 1, WORK( ILO+4*N ), 1 )
  508. CALL DAXPY( NR, -ALPHA, WORK( ILO+3*N ), 1, WORK( ILO+5*N ), 1 )
  509. *
  510. PGAMMA = GAMMA
  511. IT = IT + 1
  512. IF( IT.LE.NRP2 )
  513. $ GO TO 250
  514. *
  515. * End generalized conjugate gradient iteration
  516. *
  517. 350 CONTINUE
  518. SFMIN = DLAMCH( 'S' )
  519. SFMAX = ONE / SFMIN
  520. LSFMIN = INT( LOG10( SFMIN ) / BASL+ONE )
  521. LSFMAX = INT( LOG10( SFMAX ) / BASL )
  522. DO 360 I = ILO, IHI
  523. IRAB = IDAMAX( N-ILO+1, A( I, ILO ), LDA )
  524. RAB = ABS( A( I, IRAB+ILO-1 ) )
  525. IRAB = IDAMAX( N-ILO+1, B( I, ILO ), LDB )
  526. RAB = MAX( RAB, ABS( B( I, IRAB+ILO-1 ) ) )
  527. LRAB = INT( LOG10( RAB+SFMIN ) / BASL+ONE )
  528. IR = INT(LSCALE( I ) + SIGN( HALF, LSCALE( I ) ))
  529. IR = MIN( MAX( IR, LSFMIN ), LSFMAX, LSFMAX-LRAB )
  530. LSCALE( I ) = SCLFAC**IR
  531. ICAB = IDAMAX( IHI, A( 1, I ), 1 )
  532. CAB = ABS( A( ICAB, I ) )
  533. ICAB = IDAMAX( IHI, B( 1, I ), 1 )
  534. CAB = MAX( CAB, ABS( B( ICAB, I ) ) )
  535. LCAB = INT( LOG10( CAB+SFMIN ) / BASL+ONE )
  536. JC = INT(RSCALE( I ) + SIGN( HALF, RSCALE( I ) ))
  537. JC = MIN( MAX( JC, LSFMIN ), LSFMAX, LSFMAX-LCAB )
  538. RSCALE( I ) = SCLFAC**JC
  539. 360 CONTINUE
  540. *
  541. * Row scaling of matrices A and B
  542. *
  543. DO 370 I = ILO, IHI
  544. CALL DSCAL( N-ILO+1, LSCALE( I ), A( I, ILO ), LDA )
  545. CALL DSCAL( N-ILO+1, LSCALE( I ), B( I, ILO ), LDB )
  546. 370 CONTINUE
  547. *
  548. * Column scaling of matrices A and B
  549. *
  550. DO 380 J = ILO, IHI
  551. CALL DSCAL( IHI, RSCALE( J ), A( 1, J ), 1 )
  552. CALL DSCAL( IHI, RSCALE( J ), B( 1, J ), 1 )
  553. 380 CONTINUE
  554. *
  555. RETURN
  556. *
  557. * End of DGGBAL
  558. *
  559. END