Annotation of rpl/lapack/lapack/dggbal.f, revision 1.19
1.9 bertrand 1: *> \brief \b DGGBAL
2: *
3: * =========== DOCUMENTATION ===========
4: *
1.16 bertrand 5: * Online html documentation available at
6: * http://www.netlib.org/lapack/explore-html/
1.9 bertrand 7: *
8: *> \htmlonly
1.16 bertrand 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">
1.9 bertrand 15: *> [TXT]</a>
1.16 bertrand 16: *> \endhtmlonly
1.9 bertrand 17: *
18: * Definition:
19: * ===========
20: *
21: * SUBROUTINE DGGBAL( JOB, N, A, LDA, B, LDB, ILO, IHI, LSCALE,
22: * RSCALE, WORK, INFO )
1.16 bertrand 23: *
1.9 bertrand 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: * ..
1.16 bertrand 32: *
1.9 bertrand 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: *
1.16 bertrand 158: *> \author Univ. of Tennessee
159: *> \author Univ. of California Berkeley
160: *> \author Univ. of Colorado Denver
161: *> \author NAG Ltd.
1.9 bertrand 162: *
163: *> \ingroup doubleGBcomputational
164: *
165: *> \par Further Details:
166: * =====================
167: *>
168: *> \verbatim
169: *>
170: *> See R.C. WARD, Balancing the generalized eigenvalue problem,
171: *> SIAM J. Sci. Stat. Comp. 2 (1981), 141-152.
172: *> \endverbatim
173: *>
174: * =====================================================================
1.1 bertrand 175: SUBROUTINE DGGBAL( JOB, N, A, LDA, B, LDB, ILO, IHI, LSCALE,
176: $ RSCALE, WORK, INFO )
177: *
1.19 ! bertrand 178: * -- LAPACK computational routine --
1.1 bertrand 179: * -- LAPACK is a software package provided by Univ. of Tennessee, --
180: * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
181: *
182: * .. Scalar Arguments ..
183: CHARACTER JOB
184: INTEGER IHI, ILO, INFO, LDA, LDB, N
185: * ..
186: * .. Array Arguments ..
187: DOUBLE PRECISION A( LDA, * ), B( LDB, * ), LSCALE( * ),
188: $ RSCALE( * ), WORK( * )
189: * ..
190: *
191: * =====================================================================
192: *
193: * .. Parameters ..
194: DOUBLE PRECISION ZERO, HALF, ONE
195: PARAMETER ( ZERO = 0.0D+0, HALF = 0.5D+0, ONE = 1.0D+0 )
196: DOUBLE PRECISION THREE, SCLFAC
197: PARAMETER ( THREE = 3.0D+0, SCLFAC = 1.0D+1 )
198: * ..
199: * .. Local Scalars ..
200: INTEGER I, ICAB, IFLOW, IP1, IR, IRAB, IT, J, JC, JP1,
201: $ K, KOUNT, L, LCAB, LM1, LRAB, LSFMAX, LSFMIN,
202: $ M, NR, NRP2
203: DOUBLE PRECISION ALPHA, BASL, BETA, CAB, CMAX, COEF, COEF2,
204: $ COEF5, COR, EW, EWC, GAMMA, PGAMMA, RAB, SFMAX,
205: $ SFMIN, SUM, T, TA, TB, TC
206: * ..
207: * .. External Functions ..
208: LOGICAL LSAME
209: INTEGER IDAMAX
210: DOUBLE PRECISION DDOT, DLAMCH
211: EXTERNAL LSAME, IDAMAX, DDOT, DLAMCH
212: * ..
213: * .. External Subroutines ..
214: EXTERNAL DAXPY, DSCAL, DSWAP, XERBLA
215: * ..
216: * .. Intrinsic Functions ..
217: INTRINSIC ABS, DBLE, INT, LOG10, MAX, MIN, SIGN
218: * ..
219: * .. Executable Statements ..
220: *
221: * Test the input parameters
222: *
223: INFO = 0
224: IF( .NOT.LSAME( JOB, 'N' ) .AND. .NOT.LSAME( JOB, 'P' ) .AND.
225: $ .NOT.LSAME( JOB, 'S' ) .AND. .NOT.LSAME( JOB, 'B' ) ) THEN
226: INFO = -1
227: ELSE IF( N.LT.0 ) THEN
228: INFO = -2
229: ELSE IF( LDA.LT.MAX( 1, N ) ) THEN
230: INFO = -4
231: ELSE IF( LDB.LT.MAX( 1, N ) ) THEN
232: INFO = -6
233: END IF
234: IF( INFO.NE.0 ) THEN
235: CALL XERBLA( 'DGGBAL', -INFO )
236: RETURN
237: END IF
238: *
239: * Quick return if possible
240: *
241: IF( N.EQ.0 ) THEN
242: ILO = 1
243: IHI = N
244: RETURN
245: END IF
246: *
247: IF( N.EQ.1 ) THEN
248: ILO = 1
249: IHI = N
250: LSCALE( 1 ) = ONE
251: RSCALE( 1 ) = ONE
252: RETURN
253: END IF
254: *
255: IF( LSAME( JOB, 'N' ) ) THEN
256: ILO = 1
257: IHI = N
258: DO 10 I = 1, N
259: LSCALE( I ) = ONE
260: RSCALE( I ) = ONE
261: 10 CONTINUE
262: RETURN
263: END IF
264: *
265: K = 1
266: L = N
267: IF( LSAME( JOB, 'S' ) )
268: $ GO TO 190
269: *
270: GO TO 30
271: *
272: * Permute the matrices A and B to isolate the eigenvalues.
273: *
274: * Find row with one nonzero in columns 1 through L
275: *
276: 20 CONTINUE
277: L = LM1
278: IF( L.NE.1 )
279: $ GO TO 30
280: *
281: RSCALE( 1 ) = ONE
282: LSCALE( 1 ) = ONE
283: GO TO 190
284: *
285: 30 CONTINUE
286: LM1 = L - 1
287: DO 80 I = L, 1, -1
288: DO 40 J = 1, LM1
289: JP1 = J + 1
290: IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
291: $ GO TO 50
292: 40 CONTINUE
293: J = L
294: GO TO 70
295: *
296: 50 CONTINUE
297: DO 60 J = JP1, L
298: IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
299: $ GO TO 80
300: 60 CONTINUE
301: J = JP1 - 1
302: *
303: 70 CONTINUE
304: M = L
305: IFLOW = 1
306: GO TO 160
307: 80 CONTINUE
308: GO TO 100
309: *
310: * Find column with one nonzero in rows K through N
311: *
312: 90 CONTINUE
313: K = K + 1
314: *
315: 100 CONTINUE
316: DO 150 J = K, L
317: DO 110 I = K, LM1
318: IP1 = I + 1
319: IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
320: $ GO TO 120
321: 110 CONTINUE
322: I = L
323: GO TO 140
324: 120 CONTINUE
325: DO 130 I = IP1, L
326: IF( A( I, J ).NE.ZERO .OR. B( I, J ).NE.ZERO )
327: $ GO TO 150
328: 130 CONTINUE
329: I = IP1 - 1
330: 140 CONTINUE
331: M = K
332: IFLOW = 2
333: GO TO 160
334: 150 CONTINUE
335: GO TO 190
336: *
337: * Permute rows M and I
338: *
339: 160 CONTINUE
340: LSCALE( M ) = I
341: IF( I.EQ.M )
342: $ GO TO 170
343: CALL DSWAP( N-K+1, A( I, K ), LDA, A( M, K ), LDA )
344: CALL DSWAP( N-K+1, B( I, K ), LDB, B( M, K ), LDB )
345: *
346: * Permute columns M and J
347: *
348: 170 CONTINUE
349: RSCALE( M ) = J
350: IF( J.EQ.M )
351: $ GO TO 180
352: CALL DSWAP( L, A( 1, J ), 1, A( 1, M ), 1 )
353: CALL DSWAP( L, B( 1, J ), 1, B( 1, M ), 1 )
354: *
355: 180 CONTINUE
356: GO TO ( 20, 90 )IFLOW
357: *
358: 190 CONTINUE
359: ILO = K
360: IHI = L
361: *
362: IF( LSAME( JOB, 'P' ) ) THEN
363: DO 195 I = ILO, IHI
364: LSCALE( I ) = ONE
365: RSCALE( I ) = ONE
366: 195 CONTINUE
367: RETURN
368: END IF
369: *
370: IF( ILO.EQ.IHI )
371: $ RETURN
372: *
373: * Balance the submatrix in rows ILO to IHI.
374: *
375: NR = IHI - ILO + 1
376: DO 200 I = ILO, IHI
377: RSCALE( I ) = ZERO
378: LSCALE( I ) = ZERO
379: *
380: WORK( I ) = ZERO
381: WORK( I+N ) = ZERO
382: WORK( I+2*N ) = ZERO
383: WORK( I+3*N ) = ZERO
384: WORK( I+4*N ) = ZERO
385: WORK( I+5*N ) = ZERO
386: 200 CONTINUE
387: *
388: * Compute right side vector in resulting linear equations
389: *
390: BASL = LOG10( SCLFAC )
391: DO 240 I = ILO, IHI
392: DO 230 J = ILO, IHI
393: TB = B( I, J )
394: TA = A( I, J )
395: IF( TA.EQ.ZERO )
396: $ GO TO 210
397: TA = LOG10( ABS( TA ) ) / BASL
398: 210 CONTINUE
399: IF( TB.EQ.ZERO )
400: $ GO TO 220
401: TB = LOG10( ABS( TB ) ) / BASL
402: 220 CONTINUE
403: WORK( I+4*N ) = WORK( I+4*N ) - TA - TB
404: WORK( J+5*N ) = WORK( J+5*N ) - TA - TB
405: 230 CONTINUE
406: 240 CONTINUE
407: *
408: COEF = ONE / DBLE( 2*NR )
409: COEF2 = COEF*COEF
410: COEF5 = HALF*COEF2
411: NRP2 = NR + 2
412: BETA = ZERO
413: IT = 1
414: *
415: * Start generalized conjugate gradient iteration
416: *
417: 250 CONTINUE
418: *
419: GAMMA = DDOT( NR, WORK( ILO+4*N ), 1, WORK( ILO+4*N ), 1 ) +
420: $ DDOT( NR, WORK( ILO+5*N ), 1, WORK( ILO+5*N ), 1 )
421: *
422: EW = ZERO
423: EWC = ZERO
424: DO 260 I = ILO, IHI
425: EW = EW + WORK( I+4*N )
426: EWC = EWC + WORK( I+5*N )
427: 260 CONTINUE
428: *
429: GAMMA = COEF*GAMMA - COEF2*( EW**2+EWC**2 ) - COEF5*( EW-EWC )**2
430: IF( GAMMA.EQ.ZERO )
431: $ GO TO 350
432: IF( IT.NE.1 )
433: $ BETA = GAMMA / PGAMMA
434: T = COEF5*( EWC-THREE*EW )
435: TC = COEF5*( EW-THREE*EWC )
436: *
437: CALL DSCAL( NR, BETA, WORK( ILO ), 1 )
438: CALL DSCAL( NR, BETA, WORK( ILO+N ), 1 )
439: *
440: CALL DAXPY( NR, COEF, WORK( ILO+4*N ), 1, WORK( ILO+N ), 1 )
441: CALL DAXPY( NR, COEF, WORK( ILO+5*N ), 1, WORK( ILO ), 1 )
442: *
443: DO 270 I = ILO, IHI
444: WORK( I ) = WORK( I ) + TC
445: WORK( I+N ) = WORK( I+N ) + T
446: 270 CONTINUE
447: *
448: * Apply matrix to vector
449: *
450: DO 300 I = ILO, IHI
451: KOUNT = 0
452: SUM = ZERO
453: DO 290 J = ILO, IHI
454: IF( A( I, J ).EQ.ZERO )
455: $ GO TO 280
456: KOUNT = KOUNT + 1
457: SUM = SUM + WORK( J )
458: 280 CONTINUE
459: IF( B( I, J ).EQ.ZERO )
460: $ GO TO 290
461: KOUNT = KOUNT + 1
462: SUM = SUM + WORK( J )
463: 290 CONTINUE
464: WORK( I+2*N ) = DBLE( KOUNT )*WORK( I+N ) + SUM
465: 300 CONTINUE
466: *
467: DO 330 J = ILO, IHI
468: KOUNT = 0
469: SUM = ZERO
470: DO 320 I = ILO, IHI
471: IF( A( I, J ).EQ.ZERO )
472: $ GO TO 310
473: KOUNT = KOUNT + 1
474: SUM = SUM + WORK( I+N )
475: 310 CONTINUE
476: IF( B( I, J ).EQ.ZERO )
477: $ GO TO 320
478: KOUNT = KOUNT + 1
479: SUM = SUM + WORK( I+N )
480: 320 CONTINUE
481: WORK( J+3*N ) = DBLE( KOUNT )*WORK( J ) + SUM
482: 330 CONTINUE
483: *
484: SUM = DDOT( NR, WORK( ILO+N ), 1, WORK( ILO+2*N ), 1 ) +
485: $ DDOT( NR, WORK( ILO ), 1, WORK( ILO+3*N ), 1 )
486: ALPHA = GAMMA / SUM
487: *
488: * Determine correction to current iteration
489: *
490: CMAX = ZERO
491: DO 340 I = ILO, IHI
492: COR = ALPHA*WORK( I+N )
493: IF( ABS( COR ).GT.CMAX )
494: $ CMAX = ABS( COR )
495: LSCALE( I ) = LSCALE( I ) + COR
496: COR = ALPHA*WORK( I )
497: IF( ABS( COR ).GT.CMAX )
498: $ CMAX = ABS( COR )
499: RSCALE( I ) = RSCALE( I ) + COR
500: 340 CONTINUE
501: IF( CMAX.LT.HALF )
502: $ GO TO 350
503: *
504: CALL DAXPY( NR, -ALPHA, WORK( ILO+2*N ), 1, WORK( ILO+4*N ), 1 )
505: CALL DAXPY( NR, -ALPHA, WORK( ILO+3*N ), 1, WORK( ILO+5*N ), 1 )
506: *
507: PGAMMA = GAMMA
508: IT = IT + 1
509: IF( IT.LE.NRP2 )
510: $ GO TO 250
511: *
512: * End generalized conjugate gradient iteration
513: *
514: 350 CONTINUE
515: SFMIN = DLAMCH( 'S' )
516: SFMAX = ONE / SFMIN
517: LSFMIN = INT( LOG10( SFMIN ) / BASL+ONE )
518: LSFMAX = INT( LOG10( SFMAX ) / BASL )
519: DO 360 I = ILO, IHI
520: IRAB = IDAMAX( N-ILO+1, A( I, ILO ), LDA )
521: RAB = ABS( A( I, IRAB+ILO-1 ) )
522: IRAB = IDAMAX( N-ILO+1, B( I, ILO ), LDB )
523: RAB = MAX( RAB, ABS( B( I, IRAB+ILO-1 ) ) )
524: LRAB = INT( LOG10( RAB+SFMIN ) / BASL+ONE )
1.14 bertrand 525: IR = INT(LSCALE( I ) + SIGN( HALF, LSCALE( I ) ))
1.1 bertrand 526: IR = MIN( MAX( IR, LSFMIN ), LSFMAX, LSFMAX-LRAB )
527: LSCALE( I ) = SCLFAC**IR
528: ICAB = IDAMAX( IHI, A( 1, I ), 1 )
529: CAB = ABS( A( ICAB, I ) )
530: ICAB = IDAMAX( IHI, B( 1, I ), 1 )
531: CAB = MAX( CAB, ABS( B( ICAB, I ) ) )
532: LCAB = INT( LOG10( CAB+SFMIN ) / BASL+ONE )
1.14 bertrand 533: JC = INT(RSCALE( I ) + SIGN( HALF, RSCALE( I ) ))
1.1 bertrand 534: JC = MIN( MAX( JC, LSFMIN ), LSFMAX, LSFMAX-LCAB )
535: RSCALE( I ) = SCLFAC**JC
536: 360 CONTINUE
537: *
538: * Row scaling of matrices A and B
539: *
540: DO 370 I = ILO, IHI
541: CALL DSCAL( N-ILO+1, LSCALE( I ), A( I, ILO ), LDA )
542: CALL DSCAL( N-ILO+1, LSCALE( I ), B( I, ILO ), LDB )
543: 370 CONTINUE
544: *
545: * Column scaling of matrices A and B
546: *
547: DO 380 J = ILO, IHI
548: CALL DSCAL( IHI, RSCALE( J ), A( 1, J ), 1 )
549: CALL DSCAL( IHI, RSCALE( J ), B( 1, J ), 1 )
550: 380 CONTINUE
551: *
552: RETURN
553: *
554: * End of DGGBAL
555: *
556: END
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